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bert-large-uncased-whole-word-masking-finetuned-squad

question-answering

--- language: en license: apache-2.0 library_name: pytorch tags: - deep-reinforcement-learning - reinforcement-learning - DI-engine - BipedalWalker-v3 benchmark_name: OpenAI/Gym/Box2d task_name: BipedalWalker-v3 pipeline_tag: reinforcement-learning model-index: - name: SAC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: OpenAI/Gym/Box2d-BipedalWalker-v3 type: OpenAI/Gym/Box2d-BipedalWalker-v3 metrics: - type: mean_reward value: 319.48 +/- 0.62 name: mean_reward --- # Play **BipedalWalker-v3** with **SAC** Policy ## Model Description <!-- Provide a longer summary of what this model is. --> This is a simple **SAC** implementation to OpenAI/Gym/Box2d **BipedalWalker-v3** using the [DI-engine library](https://github.com/opendilab/di-engine) and the [DI-zoo](https://github.com/opendilab/DI-engine/tree/main/dizoo). **DI-engine** is a python library for solving general decision intelligence problems, which is based on implementations of reinforcement learning framework using PyTorch or JAX. This library aims to standardize the reinforcement learning framework across different algorithms, benchmarks, environments, and to support both academic researches and prototype applications. Besides, self-customized training pipelines and applications are supported by reusing different abstraction levels of DI-engine reinforcement learning framework. ## Model Usage ### Install the Dependencies <details close> <summary>(Click for Details)</summary> ```shell # install huggingface_ding git clone https://github.com/opendilab/huggingface_ding.git pip3 install -e ./huggingface_ding/ # install environment dependencies if needed pip3 install DI-engine[common_env] ``` </details> ### Git Clone from Huggingface and Run the Model <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` **run.py** ```python from ding.bonus import SACAgent from ding.config import Config from easydict import EasyDict import torch # Pull model from files which are git cloned from huggingface policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu")) cfg = EasyDict(Config.file_to_dict("policy_config.py")) # Instantiate the agent agent = SACAgent( env="bipedalwalker", exp_name="BipedalWalker-v3-SAC", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ### Run Model by Using Huggingface_ding <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` **run.py** ```python from ding.bonus import SACAgent from huggingface_ding import pull_model_from_hub # Pull model from Hugggingface hub policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/BipedalWalker-v3-SAC") # Instantiate the agent agent = SACAgent( env="bipedalwalker", exp_name="BipedalWalker-v3-SAC", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ## Model Training ### Train the Model and Push to Huggingface_hub <details close> <summary>(Click for Details)</summary> ```shell #Training Your Own Agent python3 -u train.py ``` **train.py** ```python from ding.bonus import SACAgent from huggingface_ding import push_model_to_hub # Instantiate the agent agent = SACAgent("bipedalwalker", exp_name="BipedalWalker-v3-SAC") # Train the agent return_ = agent.train(step=int(200000)) # Push model to huggingface hub push_model_to_hub( agent=agent.best, env_name="OpenAI/Gym/Box2d", task_name="BipedalWalker-v3", algo_name="SAC", wandb_url=return_.wandb_url, github_repo_url="https://github.com/opendilab/DI-engine", github_doc_model_url="https://di-engine-docs.readthedocs.io/en/latest/12_policies/sac.html", github_doc_env_url="https://di-engine-docs.readthedocs.io/en/latest/13_envs/bipedalwalker.html", installation_guide="pip3 install DI-engine[common_env]", usage_file_by_git_clone="./sac/bipedalwalker_sac_deploy.py", usage_file_by_huggingface_ding="./sac/bipedalwalker_sac_download.py", train_file="./sac/bipedalwalker_sac.py", repo_id="OpenDILabCommunity/BipedalWalker-v3-SAC" ) ``` </details> **Configuration** <details close> <summary>(Click for Details)</summary> ```python exp_config = { 'env': { 'manager': { 'episode_num': float("inf"), 'max_retry': 1, 'retry_type': 'reset', 'auto_reset': True, 'step_timeout': None, 'reset_timeout': None, 'retry_waiting_time': 0.1, 'cfg_type': 'BaseEnvManagerDict' }, 'stop_value': 10000000000, 'n_evaluator_episode': 5, 'env_id': 'BipedalWalker-v3', 'collector_env_num': 8, 'evaluator_env_num': 5, 'act_scale': True, 'rew_clip': True }, 'policy': { 'model': { 'twin_critic': True, 'action_space': 'reparameterization', 'obs_shape': 24, 'action_shape': 4, 'actor_head_hidden_size': 128, 'critic_head_hidden_size': 128 }, 'learn': { 'learner': { 'train_iterations': 1000000000, 'dataloader': { 'num_workers': 0 }, 'log_policy': True, 'hook': { 'load_ckpt_before_run': '', 'log_show_after_iter': 1000, 'save_ckpt_after_iter': 10000, 'save_ckpt_after_run': True }, 'cfg_type': 'BaseLearnerDict' }, 'update_per_collect': 64, 'batch_size': 256, 'learning_rate_q': 0.0003, 'learning_rate_policy': 0.0003, 'learning_rate_alpha': 0.0003, 'target_theta': 0.005, 'discount_factor': 0.99, 'alpha': 0.2, 'auto_alpha': True, 'log_space': True, 'target_entropy': None, 'ignore_done': False, 'init_w': 0.003 }, 'collect': { 'collector': {}, 'n_sample': 64, 'unroll_len': 1, 'collector_logit': False }, 'eval': { 'evaluator': { 'eval_freq': 1000, 'render': { 'render_freq': -1, 'mode': 'train_iter' }, 'cfg_type': 'InteractionSerialEvaluatorDict', 'stop_value': 10000000000, 'n_episode': 5 } }, 'other': { 'replay_buffer': { 'replay_buffer_size': 300000 } }, 'on_policy': False, 'cuda': True, 'multi_gpu': False, 'bp_update_sync': True, 'traj_len_inf': False, 'type': 'sac', 'priority': False, 'priority_IS_weight': False, 'random_collect_size': 10000, 'transition_with_policy_data': True, 'multi_agent': False, 'cfg_type': 'SACPolicyDict' }, 'exp_name': 'BipedalWalker-v3-SAC', 'seed': 0, 'wandb_logger': { 'gradient_logger': True, 'video_logger': True, 'plot_logger': True, 'action_logger': True, 'return_logger': False } } ``` </details> **Training Procedure** <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> - **Weights & Biases (wandb):** [monitor link](https://wandb.ai/zhangpaipai/BipedalWalker-v3-SAC) ## Model Information <!-- Provide the basic links for the model. --> - **Github Repository:** [repo link](https://github.com/opendilab/DI-engine) - **Doc**: [DI-engine-docs Algorithm link](https://di-engine-docs.readthedocs.io/en/latest/12_policies/sac.html) - **Configuration:** [config link](https://huggingface.co/OpenDILabCommunity/BipedalWalker-v3-SAC/blob/main/policy_config.py) - **Demo:** [video](https://huggingface.co/OpenDILabCommunity/BipedalWalker-v3-SAC/blob/main/replay.mp4) <!-- Provide the size information for the model. --> - **Parameters total size:** 240.04 KB - **Last Update Date:** 2023-05-17 ## Environments <!-- Address questions around what environment the model is intended to be trained and deployed at, including the necessary information needed to be provided for future users. --> - **Benchmark:** OpenAI/Gym/Box2d - **Task:** BipedalWalker-v3 - **Gym version:** 0.25.1 - **DI-engine version:** v0.4.7 - **PyTorch version:** 1.7.1 - **Doc**: [DI-engine-docs Environments link](https://di-engine-docs.readthedocs.io/en/latest/13_envs/bipedalwalker.html)

distilbert-base-uncased-distilled-squad

question-answering

--- language: ko tags: - korean - klue mask_token: '[MASK]' widget: - text: "이순신 장군은 [MASK] 중기의 무신이다." ---

distilroberta-base

fill-mask

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: zsomai/bert-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # zsomai/bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6221 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 765, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 1.5436 | 0 | | 0.8980 | 1 | | 0.6221 | 2 | ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

AK270802/DialoGPT-small-harrypotter

conversational

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Find your model_id: huanvo88/ppo-SnowballTargetTESTCOLAB 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

ALINEAR/albert-japanese-v2

fill-mask

--- license: creativeml-openrail-m tags: - stablediffusionapi.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # hc-any-v3-fp32-better-vae API Inference  ## Get API Key Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed. Replace Key in below code, change **model_id** to "hc-any-v3-fp32-bette" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs) Model link: [View model](https://stablediffusionapi.com/models/hc-any-v3-fp32-bette) Credits: [View credits](https://civitai.com/?query=hc-any-v3-fp32-better-vae) View all models: [View Models](https://stablediffusionapi.com/models) import requests import json url = "https://stablediffusionapi.com/api/v3/dreambooth" payload = json.dumps({ "key": "", "model_id": "hc-any-v3-fp32-bette", "prompt": "actual 8K portrait photo of gareth person, portrait, happy colors, bright eyes, clear eyes, warm smile, smooth soft skin, big dreamy eyes, beautiful intricate colored hair, symmetrical, anime wide eyes, soft lighting, detailed face, by makoto shinkai, stanley artgerm lau, wlop, rossdraws, concept art, digital painting, looking into camera", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**

ActivationAI/distilbert-base-uncased-finetuned-emotion

text-classification

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 266.10 +/- 20.81 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

AdWeeb/HTI_mbert

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.44 +/- 2.73 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="kekstroke/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

AdapterHub/bert-base-uncased-pf-art

--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: roberta_crypto_profiling_task1_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta_crypto_profiling_task1_2 This model is a fine-tuned version of [cardiffnlp/twitter-roberta-large-2022-154m](https://huggingface.co/cardiffnlp/twitter-roberta-large-2022-154m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9451 - Accuracy: 0.3765 - F1: 0.3577 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

AdapterHub/bert-base-uncased-pf-boolq

text-classification

--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -2.55 +/- 0.85 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

AdapterHub/bert-base-uncased-pf-commonsense_qa

--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Find your model_id: afos950/ppo-PyramidTraining 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

AdapterHub/bert-base-uncased-pf-conll2003_pos

token-classification

--- license: apache-2.0 datasets: - mozilla-foundation/common_voice_13_0 language: - bn metrics: - wer --- wer=57.33751127569672 and normalized_wer=29.29902387732354 {'eval/wer': 57.33751127569672, 'eval/normalized_wer': 29.29902387732354}

AdapterHub/bert-base-uncased-pf-copa

--- language: - hi license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small Id - nypnop results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Id - nypnop This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

AdapterHub/bert-base-uncased-pf-imdb

text-classification

--- license: mit tags: - generated_from_trainer model-index: - name: donut-base-graph_test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # donut-base-graph_test This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

AdapterHub/bert-base-uncased-pf-mrpc

text-classification

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 262.96 +/- 20.76 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

AdapterHub/bert-base-uncased-pf-newsqa

question-answering

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # nihiluis/argureviews-component-mpnet This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("nihiluis/argureviews-component-mpnet") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

AdapterHub/bert-base-uncased-pf-qnli

text-classification

--- language: - pt license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - Testes-2-audios model-index: - name: whisper-fine-test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-fine-test This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Modelo-teste dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

AdapterHub/bert-base-uncased-pf-scitail

text-classification

--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: my_awesome_sindarin_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_sindarin_model This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1459 - Bleu: 0.3288 - Gen Len: 12.348 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 199 | 3.6560 | 0.0612 | 17.4414 | | No log | 2.0 | 398 | 3.4073 | 0.1168 | 12.5788 | | 3.9239 | 3.0 | 597 | 3.2573 | 0.2427 | 12.3178 | | 3.9239 | 4.0 | 796 | 3.1767 | 0.2812 | 12.2585 | | 3.9239 | 5.0 | 995 | 3.1459 | 0.3288 | 12.348 | ### Framework versions - Transformers 4.28.1 - Pytorch 1.13.1+cu116 - Datasets 2.11.0 - Tokenizers 0.13.3

AdapterHub/bert-base-uncased-pf-squad_v2

question-answering

--- license: other datasets: - OpenAssistant/oasst1 language: - aa metrics: - accuracy - cer library_name: open_clip pipeline_tag: text-to-image tags: - art - medical ---

AdapterHub/bert-base-uncased-pf-ud_en_ewt

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: masked-sentence-generation-t5-base results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # masked-sentence-generation-t5-base This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7392 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.9984 | 0.05 | 80 | 2.7041 | | 2.8752 | 0.1 | 160 | 2.7021 | | 2.9314 | 0.15 | 240 | 2.6966 | | 2.8541 | 0.2 | 320 | 2.6968 | | 2.8674 | 0.25 | 400 | 2.6900 | | 2.8706 | 0.3 | 480 | 2.6886 | | 2.7718 | 0.34 | 560 | 2.6908 | | 2.8503 | 0.39 | 640 | 2.6877 | | 2.8195 | 0.44 | 720 | 2.6902 | | 2.8569 | 0.49 | 800 | 2.6893 | | 2.8372 | 0.54 | 880 | 2.6859 | | 2.8915 | 0.59 | 960 | 2.6898 | | 2.9687 | 0.64 | 1040 | 2.6909 | | 2.832 | 0.69 | 1120 | 2.6841 | | 2.8425 | 0.74 | 1200 | 2.6842 | | 2.8114 | 0.79 | 1280 | 2.6766 | | 2.8101 | 0.84 | 1360 | 2.6783 | | 2.8837 | 0.89 | 1440 | 2.6781 | | 2.894 | 0.94 | 1520 | 2.6754 | | 2.9183 | 0.99 | 1600 | 2.6762 | | 2.6916 | 1.03 | 1680 | 2.6889 | | 2.5812 | 1.08 | 1760 | 2.6896 | | 2.5522 | 1.13 | 1840 | 2.6943 | | 2.5368 | 1.18 | 1920 | 2.6928 | | 2.5987 | 1.23 | 2000 | 2.6927 | | 2.5625 | 1.28 | 2080 | 2.6899 | | 2.4946 | 1.33 | 2160 | 2.6942 | | 2.5902 | 1.38 | 2240 | 2.6900 | | 2.5415 | 1.43 | 2320 | 2.6897 | | 2.5767 | 1.48 | 2400 | 2.6858 | | 2.6262 | 1.53 | 2480 | 2.6825 | | 2.6066 | 1.58 | 2560 | 2.6818 | | 2.5387 | 1.63 | 2640 | 2.6840 | | 2.5795 | 1.67 | 2720 | 2.6828 | | 2.5521 | 1.72 | 2800 | 2.6871 | | 2.5477 | 1.77 | 2880 | 2.6836 | | 2.587 | 1.82 | 2960 | 2.6824 | | 2.529 | 1.87 | 3040 | 2.6871 | | 2.6221 | 1.92 | 3120 | 2.6838 | | 2.6353 | 1.97 | 3200 | 2.6803 | | 2.5419 | 2.02 | 3280 | 2.6879 | | 2.4521 | 2.07 | 3360 | 2.7027 | | 2.3415 | 2.12 | 3440 | 2.7105 | | 2.3483 | 2.17 | 3520 | 2.7140 | | 2.3493 | 2.22 | 3600 | 2.7144 | | 2.3967 | 2.27 | 3680 | 2.7134 | | 2.3544 | 2.32 | 3760 | 2.7122 | | 2.3192 | 2.36 | 3840 | 2.7175 | | 2.3381 | 2.41 | 3920 | 2.7166 | | 2.3667 | 2.46 | 4000 | 2.7165 | | 2.3997 | 2.51 | 4080 | 2.7106 | | 2.3178 | 2.56 | 4160 | 2.7154 | | 2.4036 | 2.61 | 4240 | 2.7144 | | 2.3797 | 2.66 | 4320 | 2.7129 | | 2.3354 | 2.71 | 4400 | 2.7136 | | 2.4109 | 2.76 | 4480 | 2.7118 | | 2.387 | 2.81 | 4560 | 2.7097 | | 2.3934 | 2.86 | 4640 | 2.7103 | | 2.3956 | 2.91 | 4720 | 2.7103 | | 2.4086 | 2.96 | 4800 | 2.7111 | | 2.4083 | 3.0 | 4880 | 2.7110 | | 2.3121 | 3.05 | 4960 | 2.7230 | | 2.263 | 3.1 | 5040 | 2.7252 | | 2.2722 | 3.15 | 5120 | 2.7296 | | 2.2053 | 3.2 | 5200 | 2.7309 | | 2.1969 | 3.25 | 5280 | 2.7363 | | 2.2684 | 3.3 | 5360 | 2.7396 | | 2.2789 | 3.35 | 5440 | 2.7376 | | 2.2227 | 3.4 | 5520 | 2.7384 | | 2.2886 | 3.45 | 5600 | 2.7390 | | 2.2182 | 3.5 | 5680 | 2.7376 | | 2.2738 | 3.55 | 5760 | 2.7394 | | 2.1687 | 3.6 | 5840 | 2.7386 | | 2.2548 | 3.65 | 5920 | 2.7371 | | 2.2391 | 3.69 | 6000 | 2.7372 | | 2.2031 | 3.74 | 6080 | 2.7391 | | 2.1885 | 3.79 | 6160 | 2.7400 | | 2.216 | 3.84 | 6240 | 2.7406 | | 2.272 | 3.89 | 6320 | 2.7401 | | 2.3455 | 3.94 | 6400 | 2.7395 | | 2.2889 | 3.99 | 6480 | 2.7392 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.12.0 - Tokenizers 0.11.0

AdapterHub/bert-base-uncased-pf-ud_pos

token-classification

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9285 - name: F1 type: f1 value: 0.9284458409041368 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2192 - Accuracy: 0.9285 - F1: 0.9284 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8301 | 1.0 | 250 | 0.3214 | 0.905 | 0.9010 | | 0.2508 | 2.0 | 500 | 0.2192 | 0.9285 | 0.9284 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

AdapterHub/bert-base-uncased-pf-wnut_17

token-classification

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 183.42 +/- 37.73 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

AdapterHub/roberta-base-pf-anli_r3

text-classification

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 - precision model-index: - name: distilbert-base-uncased_emotion_ft_0517 results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9345 - name: F1 type: f1 value: 0.9346851141275695 - name: Precision type: precision value: 0.9087842847016905 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased_emotion_ft_0517 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1479 - Accuracy: 0.9345 - F1: 0.9347 - Precision: 0.9088 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:| | 0.7913 | 1.0 | 250 | 0.2689 | 0.918 | 0.9162 | 0.9016 | | 0.2142 | 2.0 | 500 | 0.1764 | 0.929 | 0.9290 | 0.9109 | | 0.1415 | 3.0 | 750 | 0.1541 | 0.934 | 0.9345 | 0.8995 | | 0.1128 | 4.0 | 1000 | 0.1479 | 0.9345 | 0.9347 | 0.9088 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

AdapterHub/roberta-base-pf-cola

text-classification

--- tags: - raw pretrained stable diffusion model --- # raw pretrained stable diffusion model params

Aleksandar/electra-srb-ner

token-classification

--- license: mit --- --- license: mit --- Base Mode: Llama 7B LoRA is fully Merged with llama7b, so you do not need to merge it to load the model. Llama DEUS v3 is the largest dataset I've trained on yet, including: GPTeacher - General Instruct - Code Instruct - Roleplay Instruct My unreleased Roleplay V2 Instruct GPT4-LLM Uncensored + Unnatural Instructions WizardLM Uncensored CamelAI's 20k Biology, 20k Physics, 20k Chemistry, and 50k Math GPT4 Datasets CodeAlpaca This model was trained for 4 epochs over 1 day of training, it's a rank 128 LORA that targets attention heads, LM_Head, and MLP layers Prompt format: ``` ### Instruction: <prompt> ### Response: ``` or ``` ### Instruction: <prompt> ### Input: <input> ### Response: ```

AlekseyKulnevich/Pegasus-QuestionGeneration

text2text-generation

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 234.19 +/- 25.25 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

aisoftware/Loquela

# Stable Diffusion web UI A browser interface based on Gradio library for Stable Diffusion.  ## Features [Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features): - Original txt2img and img2img modes - One click install and run script (but you still must install python and git) - Outpainting - Inpainting - Color Sketch - Prompt Matrix - Stable Diffusion Upscale - Attention, specify parts of text that the model should pay more attention to - a man in a `((tuxedo))` - will pay more attention to tuxedo - a man in a `(tuxedo:1.21)` - alternative syntax - select text and press `Ctrl+Up` or `Ctrl+Down` to automatically adjust attention to selected text (code contributed by anonymous user) - Loopback, run img2img processing multiple times - X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters - Textual Inversion - have as many embeddings as you want and use any names you like for them - use multiple embeddings with different numbers of vectors per token - works with half precision floating point numbers - train embeddings on 8GB (also reports of 6GB working) - Extras tab with: - GFPGAN, neural network that fixes faces - CodeFormer, face restoration tool as an alternative to GFPGAN - RealESRGAN, neural network upscaler - ESRGAN, neural network upscaler with a lot of third party models - SwinIR and Swin2SR ([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers - LDSR, Latent diffusion super resolution upscaling - Resizing aspect ratio options - Sampling method selection - Adjust sampler eta values (noise multiplier) - More advanced noise setting options - Interrupt processing at any time - 4GB video card support (also reports of 2GB working) - Correct seeds for batches - Live prompt token length validation - Generation parameters - parameters you used to generate images are saved with that image - in PNG chunks for PNG, in EXIF for JPEG - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI - can be disabled in settings - drag and drop an image/text-parameters to promptbox - Read Generation Parameters Button, loads parameters in promptbox to UI - Settings page - Running arbitrary python code from UI (must run with `--allow-code` to enable) - Mouseover hints for most UI elements - Possible to change defaults/mix/max/step values for UI elements via text config - Tiling support, a checkbox to create images that can be tiled like textures - Progress bar and live image generation preview - Can use a separate neural network to produce previews with almost none VRAM or compute requirement - Negative prompt, an extra text field that allows you to list what you don't want to see in generated image - Styles, a way to save part of prompt and easily apply them via dropdown later - Variations, a way to generate same image but with tiny differences - Seed resizing, a way to generate same image but at slightly different resolution - CLIP interrogator, a button that tries to guess prompt from an image - Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway - Batch Processing, process a group of files using img2img - Img2img Alternative, reverse Euler method of cross attention control - Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions - Reloading checkpoints on the fly - Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one - [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community - [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once - separate prompts using uppercase `AND` - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2` - No token limit for prompts (original stable diffusion lets you use up to 75 tokens) - DeepDanbooru integration, creates danbooru style tags for anime prompts - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args) - via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI - Generate forever option - Training tab - hypernetworks and embeddings options - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime) - Clip skip - Hypernetworks - Loras (same as Hypernetworks but more pretty) - A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt - Can select to load a different VAE from settings screen - Estimated completion time in progress bar - API - Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML - via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients)) - [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions - [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions - Now without any bad letters! - Load checkpoints in safetensors format - Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64 - Now with a license! - Reorder elements in the UI from settings screen ## Installation and Running Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. Alternatively, use online services (like Google Colab): - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services) ### Automatic Installation on Windows 1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH". 2. Install [git](https://git-scm.com/download/win). 3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`. 4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user. ### Automatic Installation on Linux 1. Install the dependencies: ```bash # Debian-based: sudo apt install wget git python3 python3-venv # Red Hat-based: sudo dnf install wget git python3 # Arch-based: sudo pacman -S wget git python3 ``` 2. Navigate to the directory you would like the webui to be installed and execute the following command: ```bash bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh) ``` 3. Run `webui.sh`. 4. Check `webui-user.sh` for options. ### Installation on Apple Silicon Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon). ## Contributing Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) ## Documentation The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki). ## Credits Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file. - Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers - k-diffusion - https://github.com/crowsonkb/k-diffusion.git - GFPGAN - https://github.com/TencentARC/GFPGAN.git - CodeFormer - https://github.com/sczhou/CodeFormer - ESRGAN - https://github.com/xinntao/ESRGAN - SwinIR - https://github.com/JingyunLiang/SwinIR - Swin2SR - https://github.com/mv-lab/swin2sr - LDSR - https://github.com/Hafiidz/latent-diffusion - MiDaS - https://github.com/isl-org/MiDaS - Ideas for optimizations - https://github.com/basujindal/stable-diffusion - Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing. - Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion) - Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention) - Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas). - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator - Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch - xformers - https://github.com/facebookresearch/xformers - DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru - Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6) - Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix - Security advice - RyotaK - UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user. - (You)

Andranik/TestPytorchClassification

text-classification

--- license: apache-2.0 pipeline_tag: translation --- TensorFlow saved model version of the original model: https://www.modelscope.cn/models/damo/nlp_csanmt_translation_zh2en/summary

Andrianos/bert-base-greek-punctuation-prediction-finetuned

token-classification

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: distilbert_classifier_newsgroups results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_classifier_newsgroups This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.28.0 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

Andrija/M-bert-NER

token-classification

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 321.58 +/- 6.47 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Andrija/SRoBERTa-base

fill-mask

--- language: - english license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - instagram model - parent model : [chilloutmix] [Please report any unauthorized commercial use!]. ------------ ------------ Work Perfectly on any version Rev_Animated [Checkpoint]! https://civitai.com/models/7371/rev-animated. Also Perfect for Inpainting. Thanks... ## Training SD Clip Skip --> 1 / 2 (Recommended) Weight --> 0,7 - 1.0 Resolution --> Any Combinations (A x Z) = 512 - 1440 Denoizing Strength --> 0,56 - 0,77 (Recommended) ------------ ------------ Examples:     ------------ ------------ ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) ## Big Thanks to Myself - Skyova S.A.R.H.

Andrija/SRoBERTa

fill-mask

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: qa_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 5 | 5.8723 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

Andrija/SRoBERTaFastBPE

--- license: openrail datasets: - togethercomputer/RedPajama-Data-1T language: - ae metrics: - bleu library_name: allennlp pipeline_tag: text-to-video tags: - finance - biology - chemistry - art ---

Ann2020/distilbert-base-uncased-finetuned-ner

token-classification

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 258.96 +/- 38.78 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Anonymous/ReasonBERT-RoBERTa

feature-extraction

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: bert_simple_classifier results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # bert_simple_classifier This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3054, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.28.0 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

AnonymousNLP/pretrained-model-1

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

AnonymousSub/AR_bert-base-uncased

feature-extraction

--- datasets: - yyyynnnniiii/WSJ_0518 language: - en metrics: - accuracy pipeline_tag: text-classification tags: - finance ---

AnonymousSub/AR_cline

feature-extraction

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Benign10MGPT2_fromB_BFall_30KGen_toP_0.75 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Benign10MGPT2_fromB_BFall_30KGen_toP_0.75 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1066 - Accuracy: 0.9827 - F1: 0.7997 - Precision: 0.8920 - Recall: 0.7248 - Roc Auc Score: 0.8602 - Tpr At Fpr 0.01: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0859 | 1.0 | 26250 | 0.0749 | 0.9823 | 0.7832 | 0.9388 | 0.6718 | 0.8348 | 0.5556 | | 0.074 | 2.0 | 52500 | 0.0810 | 0.9803 | 0.7718 | 0.8628 | 0.6982 | 0.8463 | 0.5496 | | 0.0534 | 3.0 | 78750 | 0.0735 | 0.9846 | 0.8211 | 0.9211 | 0.7406 | 0.8687 | 0.5882 | | 0.0374 | 4.0 | 105000 | 0.0877 | 0.9830 | 0.8023 | 0.8976 | 0.7254 | 0.8606 | 0.0 | | 0.0267 | 5.0 | 131250 | 0.1066 | 0.9827 | 0.7997 | 0.8920 | 0.7248 | 0.8602 | 0.0 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2

AnonymousSub/AR_consert

feature-extraction

--- license: cc-by-nc-sa-4.0 datasets: - wikipedia - bookcorpus language: - en library_name: transformers pipeline_tag: fill-mask --- This is the pretrained model of MLM. Please refer to our [GitHub](https://github.com/hitachi-nlp/mlm-probe-acl2023) page for more details.

AnonymousSub/AR_rule_based_roberta_bert_quadruplet_epochs_1_shard_1

feature-extraction

--- license: apache-2.0 language: - en tags: - medical --- This repo contains MedLLaMA_13B, which is LLaMA-13b finetuned with some Medical Corpus. The model was trained with the following hyperparameters: * Epochs: 5 * Batch size: 320 * Cutoff length: 2048 * Learning rate: 2e-5 The model can be loaded as follows: ``` import transformers import torch tokenizer = transformers.LlamaTokenizer.from_pretrained('chaoyi-wu/MedLLaMA_13B') model = transformers.LlamaForCausalLM.from_pretrained('chaoyi-wu/MedLLaMA_13B') sentence = 'Hello, doctor' batch = tokenizer( sentence, return_tensors="pt", add_special_tokens=False ) with torch.no_grad(): generated = model.generate(inputs = batch["input_ids"], max_length=200, do_sample=True, top_k=50) print('model predict: ',tokenizer.decode(generated[0])) ```

AnonymousSub/AR_rule_based_roberta_bert_triplet_epochs_1_shard_10

feature-extraction

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers language: - bn metrics: - accuracy --- # {shihab17/bangla-sentence-transformer } This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## How to get sentence similarity ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import pytorch_cos_sim transformer = SentenceTransformer('shihab17/bangla-sentence-transformer') sentences = ['আমি আপেল খেতে পছন্দ করি। ', 'আমার একটি আপেল মোবাইল আছে।','এইবার কমলার ফলনা ভাল হয়নি', 'বাচ্চাটি দেখতে আপেলের মত সুন্দর','আপেলের জুস আমার অনেক প্রিয়'] sentences_embeddings = transformer.encode(sentences) for i in range(len(sentences)): for j in range(i, len(sentences)): sen_1 = sentences[i] sen_2 = sentences[j] sim_score = float(pytorch_cos_sim(sentences_embeddings[i], sentences_embeddings[j])) print(sen_1, '----->', sen_2, sim_score) ``` ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ['আমি আপেল খেতে পছন্দ করি। ', 'আমার একটি আপেল মোবাইল আছে।','আপনি কি এখানে কাছাকাছি থাকেন?', 'আশেপাশে কেউ আছেন?'] model = SentenceTransformer('shihab17/bangla-sentence-transformer ') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['আমি আপেল খেতে পছন্দ করি। ', 'আমার একটি আপেল মোবাইল আছে।','আপনি কি এখানে কাছাকাছি থাকেন?', 'আশেপাশে কেউ আছেন?'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('shihab17/bangla-sentence-transformer') model = AutoModel.from_pretrained('shihab17/bangla-sentence-transformer') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> ## Best MSE: 7.57528096437454 For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 237094 with parameters: ``` {'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MSELoss.MSELoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 500, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 8000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->

AnonymousSub/AR_rule_based_roberta_hier_quadruplet_epochs_1_shard_1

feature-extraction

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # /var/folders/cr/k5wjffrn18lf95kf3ffg7r_r0000gn/T/tmpdxymlp75/DanielaSaavedraL/saleswiz-baseline_is_about_company This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("/var/folders/cr/k5wjffrn18lf95kf3ffg7r_r0000gn/T/tmpdxymlp75/DanielaSaavedraL/saleswiz-baseline_is_about_company") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

AnonymousSub/AR_rule_based_roberta_hier_triplet_epochs_1_shard_10

feature-extraction

--- tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: negative-tweet-identification-indo-indobert results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # negative-tweet-identification-indo-indobert This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0098 - Accuracy: 0.9986 - Precision: 0.9985 - Recall: 0.9987 - F1: 0.9986 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 35 | 0.3341 | 0.8732 | 0.8806 | 0.8773 | 0.8726 | | No log | 2.0 | 70 | 0.1035 | 0.9672 | 0.9684 | 0.9677 | 0.9676 | | No log | 3.0 | 105 | 0.0263 | 0.9922 | 0.9922 | 0.9926 | 0.9924 | | No log | 4.0 | 140 | 0.0182 | 0.9945 | 0.9943 | 0.9948 | 0.9945 | | No log | 5.0 | 175 | 0.0098 | 0.9986 | 0.9985 | 0.9987 | 0.9986 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.12.0+cu102 - Datasets 2.9.0 - Tokenizers 0.12.1

AnonymousSub/AR_rule_based_roberta_only_classfn_twostage_epochs_1_shard_1

feature-extraction

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

AnonymousSub/AR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1

feature-extraction

--- language: - zh license: mit tags: - 1.1.0 - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch neunit results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SpeechT5 TTS Dutch neunit This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.12.1

AnonymousSub/SR_SDR_HF_model_base

feature-extraction

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: jason-expert-uspto-3k-preeval results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # jason-expert-uspto-3k-preeval This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 3000 ### Training results ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.1+rocm5.4.2 - Datasets 2.11.0 - Tokenizers 0.13.3

AnonymousSub/SR_rule_based_bert_quadruplet_epochs_1_shard_1

feature-extraction

--- tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.5862068965517241 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model was trained from scratch on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7576 - Accuracy: 0.5862 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6968 | 0.98 | 32 | 0.7576 | 0.5862 | | 0.6144 | 2.0 | 65 | 0.7457 | 0.5862 | | 0.5981 | 2.95 | 96 | 0.6591 | 0.5862 | ### Framework versions - Transformers 4.29.2 - Pytorch 1.12.1 - Datasets 2.12.0 - Tokenizers 0.13.3

AnonymousSub/SR_rule_based_only_classfn_twostage_epochs_1_shard_1

feature-extraction

Access to model mahimairaja/people-track-x-model is restricted and you are not in the authorized list. Visit https://huggingface.co/mahimairaja/people-track-x-model to ask for access.

AnonymousSub/bert_triplet_epochs_1_shard_10

feature-extraction

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

AnonymousSub/cline

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: A picture of <target> coding tags: - stable-diffusion - stable-diffusion-ppdiffusers - text-to-image - ppdiffusers - lora inference: false --- # LoRA DreamBooth - mortal99/test 本仓库的 LoRA 权重是基于 runwayml/stable-diffusion-v1-5 训练而来的，我们采用[DreamBooth](https://dreambooth.github.io/)的技术并使用 A picture of <target> coding 文本进行了训练。

AnonymousSub/cline_squad2.0

question-answering

--- license: apache-2.0 tags: - generated_from_trainer datasets: - cuad_v1 model-index: - name: distilbert-base-uncased-finetuned-cuad_smaller_4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cuad_smaller_4 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the cuad_v1 dataset. It achieves the following results on the evaluation set: - Loss: 0.1199 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 40 - eval_batch_size: 40 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.1067 | 1.0 | 3769 | 0.0894 | | 0.0858 | 2.0 | 7538 | 0.1010 | | 0.0683 | 3.0 | 11307 | 0.1043 | | 0.0399 | 4.0 | 15076 | 0.1199 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

AnonymousSub/cline_wikiqa

text-classification

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-base-gecfirst-e8-b16 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-base-gecfirst-e8-b16 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2233 - Rouge1: 42.0185 - Rouge2: 34.0704 - Rougel: 42.0403 - Rougelsum: 41.8957 - Gen Len: 18.9865 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.9817 | 0.25 | 74 | 0.4035 | 38.3182 | 28.427 | 38.3215 | 38.2591 | 18.9882 | | 0.6805 | 0.5 | 148 | 0.3467 | 39.9659 | 30.9972 | 40.04 | 39.9619 | 18.9831 | | 0.5885 | 0.75 | 222 | 0.3205 | 40.4848 | 31.33 | 40.4959 | 40.3976 | 18.9797 | | 0.5476 | 1.0 | 296 | 0.2869 | 40.2589 | 31.6407 | 40.3592 | 40.223 | 18.9831 | | 0.4504 | 1.25 | 370 | 0.2754 | 40.7626 | 31.8985 | 40.755 | 40.6406 | 18.9831 | | 0.4463 | 1.49 | 444 | 0.2650 | 40.908 | 32.2358 | 40.9207 | 40.8062 | 18.9831 | | 0.4155 | 1.74 | 518 | 0.2561 | 41.056 | 32.4906 | 41.029 | 40.9401 | 18.9831 | | 0.3948 | 1.99 | 592 | 0.2493 | 41.1813 | 32.7917 | 41.2183 | 41.1256 | 18.9831 | | 0.329 | 2.24 | 666 | 0.2413 | 41.8005 | 33.7235 | 41.88 | 41.7556 | 18.9831 | | 0.3195 | 2.49 | 740 | 0.2390 | 41.5207 | 33.2502 | 41.5599 | 41.4291 | 18.9848 | | 0.3148 | 2.74 | 814 | 0.2344 | 41.5913 | 33.398 | 41.614 | 41.4909 | 18.9831 | | 0.316 | 2.99 | 888 | 0.2266 | 41.6858 | 33.7369 | 41.731 | 41.6293 | 18.9831 | | 0.2498 | 3.24 | 962 | 0.2353 | 41.7077 | 33.3652 | 41.7111 | 41.6256 | 18.9848 | | 0.2534 | 3.49 | 1036 | 0.2299 | 41.8645 | 33.9926 | 41.9435 | 41.8168 | 18.9848 | | 0.2435 | 3.74 | 1110 | 0.2233 | 42.0185 | 34.0704 | 42.0403 | 41.8957 | 18.9865 | | 0.2514 | 3.99 | 1184 | 0.2292 | 41.9069 | 33.8917 | 41.9112 | 41.7937 | 18.9831 | | 0.193 | 4.24 | 1258 | 0.2462 | 41.9671 | 34.0261 | 42.024 | 41.9178 | 18.9831 | | 0.1927 | 4.48 | 1332 | 0.2322 | 42.2226 | 34.6158 | 42.3306 | 42.1946 | 18.9848 | | 0.1984 | 4.73 | 1406 | 0.2278 | 41.9762 | 34.0828 | 41.999 | 41.9107 | 18.9848 | | 0.1929 | 4.98 | 1480 | 0.2299 | 41.8244 | 33.831 | 41.8673 | 41.7786 | 18.9848 | | 0.1522 | 5.23 | 1554 | 0.2432 | 41.9142 | 33.9634 | 41.9635 | 41.859 | 18.9848 | | 0.1509 | 5.48 | 1628 | 0.2408 | 41.707 | 33.6909 | 41.7144 | 41.6345 | 18.9831 | | 0.1457 | 5.73 | 1702 | 0.2426 | 42.1729 | 34.2971 | 42.2318 | 42.119 | 18.9848 | | 0.1497 | 5.98 | 1776 | 0.2386 | 42.1408 | 34.3303 | 42.148 | 42.0599 | 18.9865 | | 0.1195 | 6.23 | 1850 | 0.2627 | 41.897 | 34.0092 | 41.9336 | 41.8262 | 18.9865 | | 0.1145 | 6.48 | 1924 | 0.2560 | 41.8456 | 33.7951 | 41.8578 | 41.7709 | 18.9865 | | 0.1198 | 6.73 | 1998 | 0.2525 | 41.8393 | 33.6033 | 41.8313 | 41.7505 | 18.9831 | | 0.114 | 6.98 | 2072 | 0.2524 | 41.8194 | 33.7992 | 41.8752 | 41.775 | 18.9848 | | 0.1 | 7.23 | 2146 | 0.2690 | 41.9724 | 33.9339 | 42.0248 | 41.9444 | 18.9848 | | 0.0948 | 7.47 | 2220 | 0.2715 | 41.8806 | 33.9232 | 41.9392 | 41.8432 | 18.9848 | | 0.0947 | 7.72 | 2294 | 0.2722 | 41.8981 | 33.8642 | 41.9622 | 41.877 | 18.9848 | | 0.0904 | 7.97 | 2368 | 0.2705 | 41.8596 | 33.9216 | 41.915 | 41.8342 | 18.9848 | ### Framework versions - Transformers 4.28.1 - Pytorch 1.11.0a0+b6df043 - Datasets 2.12.0 - Tokenizers 0.13.3

AnonymousSub/consert-s10-SR

text-classification

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- # Model Card for MNKAD ## Model Description - **Developed by:** BADMONK - **Model type:** Dreambooth Model + Extracted LoRA - **Language(s) (NLP):** EN - **License:** Creativeml-Openrail-M - **Parent Model:** ChilloutMix # How to Get Started with the Model Use the code below to get started with the model. ### MNKAD ###

AnonymousSub/dummy_1

text-classification

--- datasets: - food101 language: - en metrics: - accuracy library_name: transformers --- # Image Classification Classifies food images using a subset of the food101 dataset.<br> Uses PyTorch for the preprocessing, training, and inference. ``` output_dir="cats_vs_dogs_model" remove_unused_columns=False evaluation_strategy="epoch" save_strategy="epoch" learning_rate=5e-5 per_device_train_batch_size=16 gradient_accumulation_steps=4 per_device_eval_batch_size=16 num_train_epochs=3 warmup_ratio=0.1 logging_steps=10 load_best_model_at_end=True metric_for_best_model="accuracy" push_to_hub=True ```

AnonymousSub/rule_based_roberta_hier_quadruplet_0.1_epochs_1_shard_1

feature-extraction

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: zero-shot-learning-arabert results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # zero-shot-learning-arabert This model is a fine-tuned version of [aubmindlab/bert-large-arabertv02-twitter](https://huggingface.co/aubmindlab/bert-large-arabertv02-twitter) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6305 - Macro F1: 0.7841 - Accuracy: 0.7826 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Macro F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| | No log | 1.0 | 418 | 0.6115 | 0.7669 | 0.7670 | | 0.6523 | 2.0 | 836 | 0.6305 | 0.7841 | 0.7826 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_10

feature-extraction

--- license: other datasets: - ehartford/wizard_vicuna_70k_unfiltered language: - en tags: - uncensored --- # Wizard-Vicuna-7B-Uncensored GPTQ This is GPTQ format quantised 4bit models of [Eric Hartford's 'uncensored' training of Wizard-Vicuna 7B](https://huggingface.co/ehartford/Wizard-Vicuna-7B-Uncensored). It is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). ## Repositories available * [4bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ). * [4bit and 5bit GGML models for CPU inference](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GGML). * [float16 HF format model for GPU inference and further conversions](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-HF). ## How to easily download and use this model in text-generation-webui Open the text-generation-webui UI as normal. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ`. 3. Click **Download**. 4. Wait until it says it's finished downloading. 5. Click the **Refresh** icon next to **Model** in the top left. 6. In the **Model drop-down**: choose the model you just downloaded, `Wizard-Vicuna-7B-Uncensored-GPTQ`. 7. If you see an error in the bottom right, ignore it - it's temporary. 8. Fill out the `GPTQ parameters` on the right: `Bits = 4`, `Groupsize = 128`, `model_type = Llama` 9. Click **Save settings for this model** in the top right. 10. Click **Reload the Model** in the top right. 11. Once it says it's loaded, click the **Text Generation tab** and enter a prompt! ## Provided files **Compatible file - Wizard-Vicuna-7B-Uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors** In the `main` branch - the default one - you will find `Wizard-Vicuna-7B-Uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors` This will work with all versions of GPTQ-for-LLaMa. It has maximum compatibility It was created without the `--act-order` parameter. It may have slightly lower inference quality compared to the other file, but is guaranteed to work on all versions of GPTQ-for-LLaMa and text-generation-webui. * `Wizard-Vicuna-7B-Uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors` * Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches * Works with AutoGPTQ. * Works with text-generation-webui one-click-installers * Parameters: Groupsize = 128g. No act-order. * Command used to create the GPTQ: ``` python llama.py ehartford_Wizard-Vicuna-7B-Uncensored wikitext2 --wbits 4 --groupsize 128 --true-sequential --save_safetensors Wizard-Vicuna-7B-Uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors ``` # Original model card This is [wizard-vicuna-13b](https://huggingface.co/junelee/wizard-vicuna-13b) trained against LLaMA-7B with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA. Shout out to the open source AI/ML community, and everyone who helped me out. Note: An uncensored model has no guardrails. You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car. Publishing anything this model generates is the same as publishing it yourself. You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it.

AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_1_squad2.0

question-answering

--- license: other datasets: - ehartford/wizard_vicuna_70k_unfiltered language: - en tags: - uncensored inference: false --- # Wizard-Vicuna-7B-Uncensored GGML This is GGML format quantised 4bit and 5bit models of [Eric Hartford's 'uncensored' training of Wizard-Vicuna 13B](https://huggingface.co/ehartford/Wizard-Vicuna-7B-Uncensored). This repo is the result of quantising to 4bit and 5bit GGML for CPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp). ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ). * [4-bit, 5-bit and 8-bit GGML models for CPU (+CUDA) inference](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GGML). * [float16 HF format model for GPU inference and further conversions](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-HF). ## THE FILES IN MAIN BRANCH REQUIRES LATEST LLAMA.CPP (May 19th 2023 - commit 2d5db48)! llama.cpp recently made another breaking change to its quantisation methods - https://github.com/ggerganov/llama.cpp/pull/1508 I have quantised the GGML files in this repo with the latest version. Therefore you will require llama.cpp compiled on May 19th or later (commit `2d5db48` or later) to use them. For files compatible with the previous version of llama.cpp, please see branch `previous_llama_ggmlv2`. ## Provided files | Name | Quant method | Bits | Size | RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | `Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_0.bin` | q4_0 | 4bit | 4.21GB | 7.0GB | 4-bit. | `Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_1.bin` | q4_1 | 4bit | 4.63GB | 7.5GB | 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. | `Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_0.bin` | q5_0 | 5bit | 4.63GB | 7.5GB | 5-bit. Higher accuracy, higher resource usage and slower inference. | `Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_1.bin` | q5_1 | 5bit | 5.06GB | 7.5GB | 5-bit. Even higher accuracy, and higher resource usage and slower inference. | `Wizard-Vicuna-7B-Uncensored.ggmlv3.q8_0.bin` | q8_0 | 8bit | 7.58GB | 9.0GB | 8-bit. Almost indistinguishable from float16. Huge resource use and slow. Not recommended for normal use. | ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./main -t 8 -m Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: write a story about llamas ### Response:" ``` Change `-t 8` to the number of physical CPU cores you have. ## How to run in `text-generation-webui` GGML models can be loaded into text-generation-webui by installing the llama.cpp module, then placing the ggml model file in a model folder as usual. Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md). Note: at this time text-generation-webui may not support the new May 19th llama.cpp quantisation methods for q4_0, q4_1 and q8_0 files. # Original model card This is [wizard-vicuna-13b](https://huggingface.co/junelee/wizard-vicuna-13b) trained against LLaMA-7B with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA. Shout out to the open source AI/ML community, and everyone who helped me out. Note: An uncensored model has no guardrails. You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car. Publishing anything this model generates is the same as publishing it yourself. You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it.

AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_10

feature-extraction

--- datasets: - Travad98/SOGC-archive-trademarks-1883-2001 language: - de - it - fr metrics: - accuracy library_name: transformers pipeline_tag: image-to-text tags: - trademarks - document-parsing ---

Anthos23/FS-distilroberta-fine-tuned

text-classification

--- language: - en pipeline_tag: text-generation --- ### Description: This is a llama 13b 4 bit LoRA ### Objective for this project: To create a model that upholds a logical thread, regardless of whether the output is verbose or concise. Training has been performed on a version of the pile of sets, reduced to 40% of its original size, to expedite training iterations. I personally utilize this model as an aid for storytelling and writing. While it serves this purpose adequately, I still perceive this version as a prototype. ### Prompt format: Stanford Alpaca The prompt should start on a new line after "### Response:" - For examples with a non-empty input field: ``` Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Input: {input} ### Response: ``` - For examples with an empty input field: ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Response: ``` ### Perplexity Benchmarks: - wikitext: 4.66796875 ### Training information: - 2 Epochs - 64 / 32 R / A - 1024 Cutoff - 19 hours on an A6000 ### Data used in training: All cleaned and scrubbed in various ways then culled to various degrees. - Camel biology, physics, chemistry, math, and AI society - Alpaca evol instruct - GPTeacher Instruct - Alpaca GPT4 - Dolly Databricks ### Plans for the future, a brief overview: - Pivot to a conversational format going forward - Train another 13b LoRA against the entirety of my pile of sets rather than just a portion of it for Mk2 - Train 30b on the Mk2 pile of sets - Expand the story generation capabilities and likely more for Mk3 ### Model used for training: https://huggingface.co/PocketDoc/llama-13b-gptq-4bit-128g ### Disclaimer: It has not been aligned and no warranty is given for the quality or safety of its outputs.

AntonClaesson/finetuning_test

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-ft results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-ft This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3824 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000166 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 16 | 2.2852 | | No log | 2.0 | 32 | 2.2098 | | No log | 3.0 | 48 | 2.2370 | | No log | 4.0 | 64 | 2.3000 | | No log | 5.0 | 80 | 2.3898 | | No log | 6.0 | 96 | 2.4586 | | No log | 7.0 | 112 | 2.5484 | | No log | 8.0 | 128 | 2.6572 | | No log | 9.0 | 144 | 2.7703 | | No log | 10.0 | 160 | 2.9010 | | No log | 11.0 | 176 | 2.9734 | | No log | 12.0 | 192 | 3.0461 | | No log | 13.0 | 208 | 3.1837 | | No log | 14.0 | 224 | 3.2359 | | No log | 15.0 | 240 | 3.2506 | | No log | 16.0 | 256 | 3.2979 | | No log | 17.0 | 272 | 3.3512 | | No log | 18.0 | 288 | 3.3811 | | No log | 19.0 | 304 | 3.3787 | | No log | 20.0 | 320 | 3.3824 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

Aplinxy9plin/toxic-detection-rus

--- datasets: - oscar-corpus/OSCAR-2301 language: - it tags: - ipt-125m --- # IPT-125m (WIP) IPT-125m is a decoder-style transformer pretrained from scratch on 4.36 billion tokens of Italian text from the [OSCAR-2301](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301) dataset. If you like this project, consider supporting me with a cup of coffee! 🤖✨🌞 [](https://bmc.link/edoardofederici) ## How to Use This model is best used with the Hugging Face `transformers` library for training and finetuning. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("efederici/ipt-125m", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("efederici/ipt-125m") ``` ## Model Description The architecture is a modification of a standard decoder-only transformer. The model has been modified from a standard transformer in the following ways: * It can use [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) * It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings * It does not use biases | Hyperparameter | Value | |----------------|-------| |n_parameters | 125M | |n_layers | 12 | | n_heads | 12 | | d_model | 768 | | vocab size | 50432 | | sequence length | 2048 |

Apoorva/k2t-test

text2text-generation

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 260.73 +/- 14.85 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

ArBert/albert-base-v2-finetuned-ner-gmm-twitter

token-classification

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 25.40 +/- 30.69 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

ArBert/albert-base-v2-finetuned-ner

token-classification

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 258.43 +/- 22.06 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

ArBert/bert-base-uncased-finetuned-ner-kmeans-twitter

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distillbert-base-uncased-finetuned-clinc results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distillbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7720 - Accuracy: 0.9181 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 318 | 3.2887 | 0.7419 | | 3.7868 | 2.0 | 636 | 1.8753 | 0.8371 | | 3.7868 | 3.0 | 954 | 1.1570 | 0.8961 | | 1.6927 | 4.0 | 1272 | 0.8573 | 0.9129 | | 0.9056 | 5.0 | 1590 | 0.7720 | 0.9181 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Tokenizers 0.13.3

ArBert/roberta-base-finetuned-ner-agglo-twitter

token-classification

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="Auruur/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

ArBert/roberta-base-finetuned-ner-gmm

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.48 +/- 2.74 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="aliakyurek/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

ArBert/roberta-base-finetuned-ner-kmeans-twitter

token-classification

--- language: - en - es --- # Model Card for Carpincho-13b <!-- Provide a quick summary of what the model is/does. --> This is Carpincho-13B an Instruction-tuned LLM based on LLama-13B. It is trained to answer in colloquial spanish Argentine language. It's based on LLama-13b (https://huggingface.co/decapoda-research/llama-13b-hf). ## Model Details The model is provided in ggml format, for use with the llama.cpp CPU-only LLM inference (https://github.com/ggerganov/llama.cpp) ## Usage Clone the llama.cpp repository: git clone https://github.com/ggerganov/llama.cpp Compile the tool: make Download the file carpincho-13b-ggml-model-q4_0.bin into the llama.cpp directory and run this command: ./main -m ./carpincho-13b-ggml-model-q4_0.bin -i -ins -t 4 Change -t 4 to the number of physical CPU cores you have. This model requires at least 8GB of free RAM. No GPU is needed to run llama.cpp. ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** Alfredo Ortega (@ortegaalfredo) - **Model type:** 13B LLM - **Language(s):** (NLP): English and colloquial Argentine Spanish - **License:** Free for non-commercial use, but I'm not the police. - **Finetuned from model:** https://huggingface.co/decapoda-research/llama-13b-hf ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** https://huggingface.co/decapoda-research/llama-13b-hf - **Paper [optional]:** https://arxiv.org/abs/2302.13971 ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> This is a generic LLM chatbot that can be used to interact directly with humans. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> This bot is uncensored and may provide shocking answers. Also it contains bias present in the training material. ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. ## Model Card Contact Contact the creator at @ortegaalfredo on twitter/github

ArJakusz/DialoGPT-small-starky

--- language: - sw license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Swahili results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 sw type: mozilla-foundation/common_voice_11_0 config: sw split: test args: sw metrics: - name: Wer type: wer value: 23.724554196406032 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Swahili This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 sw dataset. It achieves the following results on the evaluation set: - Loss: 0.6442 - Wer: 23.7246 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2694 | 1.07 | 1000 | 0.5438 | 26.8354 | | 0.2306 | 3.02 | 2000 | 0.5081 | 23.9231 | | 0.0467 | 4.09 | 3000 | 0.5648 | 24.4085 | | 0.0239 | 6.03 | 4000 | 0.5994 | 23.8634 | | 0.0123 | 7.1 | 5000 | 0.6442 | 23.7246 | ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.1.dev0 - Tokenizers 0.13.3

Araby/Arabic-TTS

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 96.20 +/- 42.21 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': True 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 2000000 'learning_rate': 0.0003 'num_envs': 16 'num_steps': 1024 'anneal_lr': True 'gae': True 'gamma': 0.999 'gae_lambda': 0.98 'num_minibatches': 32 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'andyleow/cleanRL-PPO-LunarLander-v2' 'batch_size': 16384 'minibatch_size': 512} ```

ArcQ/gpt-experiments

--- language: en license: apache-2.0 library_name: pytorch tags: - deep-reinforcement-learning - reinforcement-learning - DI-engine - PongNoFrameskip-v4 benchmark_name: OpenAI/Gym/Atari task_name: PongNoFrameskip-v4 pipeline_tag: reinforcement-learning model-index: - name: C51 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: OpenAI/Gym/Atari-PongNoFrameskip-v4 type: OpenAI/Gym/Atari-PongNoFrameskip-v4 metrics: - type: mean_reward value: 20.25 +/- 0.83 name: mean_reward --- # Play **PongNoFrameskip-v4** with **C51** Policy ## Model Description <!-- Provide a longer summary of what this model is. --> This is a simple **C51** implementation to OpenAI/Gym/Atari **PongNoFrameskip-v4** using the [DI-engine library](https://github.com/opendilab/di-engine) and the [DI-zoo](https://github.com/opendilab/DI-engine/tree/main/dizoo). **DI-engine** is a python library for solving general decision intelligence problems, which is based on implementations of reinforcement learning framework using PyTorch or JAX. This library aims to standardize the reinforcement learning framework across different algorithms, benchmarks, environments, and to support both academic researches and prototype applications. Besides, self-customized training pipelines and applications are supported by reusing different abstraction levels of DI-engine reinforcement learning framework. ## Model Usage ### Install the Dependencies <details close> <summary>(Click for Details)</summary> ```shell # install huggingface_ding git clone https://github.com/opendilab/huggingface_ding.git pip3 install -e ./huggingface_ding/ # install environment dependencies if needed pip3 install DI-engine[common_env] ``` </details> ### Git Clone from Huggingface and Run the Model <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` **run.py** ```python from ding.bonus import C51Agent from ding.config import Config from easydict import EasyDict import torch # Pull model from files which are git cloned from huggingface policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu")) cfg = EasyDict(Config.file_to_dict("policy_config.py")) # Instantiate the agent agent = C51Agent( env="PongNoFrameskip", exp_name="PongNoFrameskip-v4-C51", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ### Run Model by Using Huggingface_ding <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` **run.py** ```python from ding.bonus import C51Agent from huggingface_ding import pull_model_from_hub # Pull model from Hugggingface hub policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/PongNoFrameskip-v4-C51") # Instantiate the agent agent = C51Agent( env="PongNoFrameskip", exp_name="PongNoFrameskip-v4-C51", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ## Model Training ### Train the Model and Push to Huggingface_hub <details close> <summary>(Click for Details)</summary> ```shell #Training Your Own Agent python3 -u train.py ``` **train.py** ```python from ding.bonus import C51Agent from huggingface_ding import push_model_to_hub # Instantiate the agent agent = C51Agent(env="PongNoFrameskip", exp_name="PongNoFrameskip-v4-C51") # Train the agent return_ = agent.train(step=int(20000000)) # Push model to huggingface hub push_model_to_hub( agent=agent.best, env_name="OpenAI/Gym/Atari", task_name="PongNoFrameskip-v4", algo_name="C51", wandb_url=return_.wandb_url, github_repo_url="https://github.com/opendilab/DI-engine", github_doc_model_url="https://di-engine-docs.readthedocs.io/en/latest/12_policies/c51.html", github_doc_env_url="https://di-engine-docs.readthedocs.io/en/latest/13_envs/atari.html", installation_guide="pip3 install DI-engine[common_env]", usage_file_by_git_clone="./c51/pong_c51_deploy.py", usage_file_by_huggingface_ding="./c51/pong_c51_download.py", train_file="./c51/pong_c51.py", repo_id="OpenDILabCommunity/PongNoFrameskip-v4-C51" ) ``` </details> **Configuration** <details close> <summary>(Click for Details)</summary> ```python exp_config = { 'env': { 'manager': { 'episode_num': float("inf"), 'max_retry': 1, 'retry_type': 'reset', 'auto_reset': True, 'step_timeout': None, 'reset_timeout': None, 'retry_waiting_time': 0.1, 'cfg_type': 'BaseEnvManagerDict' }, 'stop_value': 20, 'n_evaluator_episode': 8, 'collector_env_num': 8, 'evaluator_env_num': 8, 'env_id': 'PongNoFrameskip-v4', 'frame_stack': 4 }, 'policy': { 'model': { 'encoder_hidden_size_list': [128, 128, 512], 'v_min': -10, 'v_max': 10, 'n_atom': 51, 'obs_shape': [4, 84, 84], 'action_shape': 6 }, 'learn': { 'learner': { 'train_iterations': 1000000000, 'dataloader': { 'num_workers': 0 }, 'log_policy': True, 'hook': { 'load_ckpt_before_run': '', 'log_show_after_iter': 100, 'save_ckpt_after_iter': 10000, 'save_ckpt_after_run': True }, 'cfg_type': 'BaseLearnerDict' }, 'update_per_collect': 10, 'batch_size': 32, 'learning_rate': 0.0001, 'target_update_freq': 500, 'target_theta': 0.005, 'ignore_done': False }, 'collect': { 'collector': {}, 'n_sample': 100, 'unroll_len': 1 }, 'eval': { 'evaluator': { 'eval_freq': 4000, 'render': { 'render_freq': -1, 'mode': 'train_iter' }, 'cfg_type': 'InteractionSerialEvaluatorDict', 'stop_value': 20, 'n_episode': 8 } }, 'other': { 'replay_buffer': { 'replay_buffer_size': 100000 }, 'eps': { 'type': 'exp', 'start': 1.0, 'end': 0.05, 'decay': 250000 } }, 'on_policy': False, 'cuda': True, 'multi_gpu': False, 'bp_update_sync': True, 'traj_len_inf': False, 'type': 'c51', 'priority': False, 'priority_IS_weight': False, 'discount_factor': 0.99, 'nstep': 3, 'cfg_type': 'C51PolicyDict' }, 'exp_name': 'PongNoFrameskip-v4-C51', 'seed': 0, 'wandb_logger': { 'gradient_logger': True, 'video_logger': True, 'plot_logger': True, 'action_logger': True, 'return_logger': False } } ``` </details> **Training Procedure** <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> - **Weights & Biases (wandb):** [monitor link](https://wandb.ai/zjowowen/PongNoFrameskip-v4-C51) ## Model Information <!-- Provide the basic links for the model. --> - **Github Repository:** [repo link](https://github.com/opendilab/DI-engine) - **Doc**: [DI-engine-docs Algorithm link](https://di-engine-docs.readthedocs.io/en/latest/12_policies/c51.html) - **Configuration:** [config link](https://huggingface.co/OpenDILabCommunity/PongNoFrameskip-v4-C51/blob/main/policy_config.py) - **Demo:** [video](https://huggingface.co/OpenDILabCommunity/PongNoFrameskip-v4-C51/blob/main/replay.mp4) <!-- Provide the size information for the model. --> - **Parameters total size:** 55276.2 KB - **Last Update Date:** 2023-05-18 ## Environments <!-- Address questions around what environment the model is intended to be trained and deployed at, including the necessary information needed to be provided for future users. --> - **Benchmark:** OpenAI/Gym/Atari - **Task:** PongNoFrameskip-v4 - **Gym version:** 0.25.1 - **DI-engine version:** v0.4.7 - **PyTorch version:** 1.7.1 - **Doc**: [DI-engine-docs Environments link](https://di-engine-docs.readthedocs.io/en/latest/13_envs/atari.html)

Arcanos/1

--- license: creativeml-openrail-m tags: - stablediffusionapi.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # API Inference  ## Get API Key Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed. Replace Key in below code, change **model_id** to "3AmvPjC7O5hWtaFsl8bf6dkGx" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs) Model link: [View model](https://stablediffusionapi.com/models/3AmvPjC7O5hWtaFsl8bf6dkGx) Credits: [View credits](https://civitai.com/?query=model_search) View all models: [View Models](https://stablediffusionapi.com/models) import requests import json url = "https://stablediffusionapi.com/api/v3/dreambooth" payload = json.dumps({ "key": "", "model_id": "3AmvPjC7O5hWtaFsl8bf6dkGx", "prompt": "actual 8K portrait photo of gareth person, portrait, happy colors, bright eyes, clear eyes, warm smile, smooth soft skin, big dreamy eyes, beautiful intricate colored hair, symmetrical, anime wide eyes, soft lighting, detailed face, by makoto shinkai, stanley artgerm lau, wlop, rossdraws, concept art, digital painting, looking into camera", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**

Archie/myProject

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: mbert_zhth results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mbert_zhth This model is a fine-tuned version of [../mbert_zhth/](https://huggingface.co/../mbert_zhth/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4626 - Accuracy: 0.7125 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.12.0 - Tokenizers 0.13.3

AriakimTaiyo/DialoGPT-cultured-Kumiko

conversational

--- license: openrail datasets: - databricks/databricks-dolly-15k language: - aa metrics: - accuracy ---

AriakimTaiyo/DialoGPT-medium-Kumiko

conversational

Access to model sinadi/LowRa is restricted and you are not in the authorized list. Visit https://huggingface.co/sinadi/LowRa to ask for access.

AriakimTaiyo/DialoGPT-small-Kumiko

conversational

--- tags: - generated_from_keras_callback model-index: - name: T5-Text2Code results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # T5-Text2Code This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

AriakimTaiyo/DialoGPT-small-Rikka

conversational

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers license: mit datasets: - unicamp-dl/mmarco language: - it library_name: sentence-transformers --- # MMARCO-bert-base-italian-uncased This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer, util query = "Quante persone vivono a Londra?" docs = ["A Londra vivono circa 9 milioni di persone", "Londra è conosciuta per il suo quartiere finanziario"] #Load the model model = SentenceTransformer('nickprock/mmarco-bert-base-italian-uncased') #Encode query and documents query_emb = model.encode(query) doc_emb = model.encode(docs) #Compute dot score between query and all document embeddings scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist() #Combine docs & scores doc_score_pairs = list(zip(docs, scores)) #Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) #Output passages & scores for doc, score in doc_score_pairs: print(score, doc) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output.last_hidden_state input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) #Encode text def encode(texts): # Tokenize sentences encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input, return_dict=True) # Perform pooling embeddings = mean_pooling(model_output, encoded_input['attention_mask']) return embeddings # Sentences we want sentence embeddings for query = "Quante persone vivono a Londra?" docs = ["A Londra vivono circa 9 milioni di persone", "Londra è conosciuta per il suo quartiere finanziario"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained("nickprock/mmarco-bert-base-italian-uncased") model = AutoModel.from_pretrained("nickprock/mmarco-bert-base-italian-uncased") #Encode query and docs query_emb = encode(query) doc_emb = encode(docs) #Compute dot score between query and all document embeddings scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist() #Combine docs & scores doc_score_pairs = list(zip(docs, scores)) #Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) #Output passages & scores print("Query:", query) for doc, score in doc_score_pairs: print(score, doc) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 6250 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 500, "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 1500, "warmup_steps": 6250, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->

Aries/T5_question_answering

text2text-generation

--- license: creativeml-openrail-m tags: - stablediffusionapi.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # API Inference  ## Get API Key Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed. Replace Key in below code, change **model_id** to "UMNDtl4qaHXQRyZxYI6h8g32p" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs) Model link: [View model](https://stablediffusionapi.com/models/UMNDtl4qaHXQRyZxYI6h8g32p) Credits: [View credits](https://civitai.com/?query=model_search) View all models: [View Models](https://stablediffusionapi.com/models) import requests import json url = "https://stablediffusionapi.com/api/v3/dreambooth" payload = json.dumps({ "key": "", "model_id": "UMNDtl4qaHXQRyZxYI6h8g32p", "prompt": "actual 8K portrait photo of gareth person, portrait, happy colors, bright eyes, clear eyes, warm smile, smooth soft skin, big dreamy eyes, beautiful intricate colored hair, symmetrical, anime wide eyes, soft lighting, detailed face, by makoto shinkai, stanley artgerm lau, wlop, rossdraws, concept art, digital painting, looking into camera", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**

ArnaudPannatier/MLPMixer

--- datasets: - cats_vs_dogs language: - en metrics: - accuracy library_name: transformers --- # Image Classification Classifies cat and dog images using a subset of the cats_vs_dogs dataset.<br> Uses PyTorch for the preprocessing, training, and inference. ``` output_dir="cats_vs_dogs_model" remove_unused_columns=False evaluation_strategy="epoch" save_strategy="epoch" learning_rate=5e-5 per_device_train_batch_size=16 gradient_accumulation_steps=4 per_device_eval_batch_size=16 num_train_epochs=3 warmup_ratio=0.1 logging_steps=10 load_best_model_at_end=True metric_for_best_model="accuracy" push_to_hub=True ``` Note: during the training, I tried adjusting some of the above hyperparameters (like making the learning rate 0.1 as we have seen in class). But then the model could only classify cats and not dogs.

Arnold/wav2vec2-large-xlsr-hausa2-demo-colab

automatic-speech-recognition

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - RenauxLouis/monet-test-1000steps-116-realsize-v3 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the real-size-116 dataset. You can find some example images in the following.    

Arnold/wav2vec2-large-xlsr-turkish-demo-colab

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter-PLE-v0_1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 7.80 +/- 5.58 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

ArseniyBolotin/bert-multi-PAD-ner

token-classification

Access to model zhuxiang/yunduanshiyong is restricted and you are not in the authorized list. Visit https://huggingface.co/zhuxiang/yunduanshiyong to ask for access.

Aruden/DialoGPT-medium-harrypotterall

conversational

--- tags: - spacy - token-classification language: - en model-index: - name: en_Spacy_Custom_ner results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.9911054638 - name: NER Recall type: recall value: 0.9961685824 - name: NER F Score type: f_score value: 0.9936305732 --- | Feature | Description | | --- | --- | | **Name** | `en_Spacy_Custom_ner` | | **Version** | `0.0.0` | | **spaCy** | `>=3.5.3,<3.6.0` | | **Default Pipeline** | `tok2vec`, `ner` | | **Components** | `tok2vec`, `ner` | | **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) | | **Sources** | n/a | | **License** | n/a | | **Author** | [n/a]() | ### Label Scheme <details> <summary>View label scheme (14 labels for 1 components)</summary> | Component | Labels | | --- | --- | | **`ner`** | `BOOK`, `COMODITY`, `CONTAINER COUNT`, `CONTAINER SIZE`, `CONTAINER SIZE-COUNT`, `DESTINATION`, `ENQUIRY`, `HELP`, `INCOTERM`, `KYC`, `ORIGIN`, `SEARCH RATES`, `SHIP`, `SHIPMENT TYPE` | </details> ### Accuracy | Type | Score | | --- | --- | | `ENTS_F` | 99.36 | | `ENTS_P` | 99.11 | | `ENTS_R` | 99.62 | | `TOK2VEC_LOSS` | 2568.71 | | `NER_LOSS` | 72512.12 |

ArvinZhuang/BiTAG-t5-large

text2text-generation

--- license: creativeml-openrail-m inference: true language: - en library_name: diffusers pipeline_tag: text-to-image tags: - stable-diffusion - text-to-image ---

AshiNLP/Bert_model

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter-PLE-v0_2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 21.50 +/- 15.52 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

Atampy26/GPT-Glacier

text-generation

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

Augustvember/wokka

text-generation

--- license: apache-2.0 tags: - generated_from_trainer datasets: - pokemon-classification metrics: - accuracy model-index: - name: my_awesome_pokemon_model results: - task: name: Image Classification type: image-classification dataset: name: pokemon-classification type: pokemon-classification config: full split: validation args: full metrics: - name: Accuracy type: accuracy value: 0.07553956834532374 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_pokemon_model This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pokemon-classification dataset. It achieves the following results on the evaluation set: - Loss: 7.3838 - Accuracy: 0.0755 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.926 | 1.0 | 76 | 5.4705 | 0.0007 | | 3.7521 | 1.99 | 152 | 5.9651 | 0.0129 | | 1.9692 | 2.99 | 228 | 5.8631 | 0.0144 | | 0.7605 | 4.0 | 305 | 5.9688 | 0.0482 | | 0.4163 | 5.0 | 381 | 6.1329 | 0.0655 | | 0.3085 | 5.99 | 457 | 6.2311 | 0.0806 | | 0.2155 | 6.99 | 533 | 6.4040 | 0.0683 | | 0.2188 | 8.0 | 610 | 6.4869 | 0.0748 | | 0.2241 | 9.0 | 686 | 6.6527 | 0.0763 | | 0.1505 | 9.99 | 762 | 6.7076 | 0.0755 | | 0.1429 | 10.99 | 838 | 6.7627 | 0.0719 | | 0.1378 | 12.0 | 915 | 6.8740 | 0.0712 | | 0.1335 | 13.0 | 991 | 6.9456 | 0.0741 | | 0.1335 | 13.99 | 1067 | 6.8821 | 0.0748 | | 0.1131 | 14.99 | 1143 | 6.9655 | 0.0763 | | 0.1041 | 16.0 | 1220 | 7.0660 | 0.0763 | | 0.0844 | 17.0 | 1296 | 7.1479 | 0.0770 | | 0.086 | 17.99 | 1372 | 7.1182 | 0.0748 | | 0.1028 | 18.99 | 1448 | 7.1395 | 0.0734 | | 0.0456 | 20.0 | 1525 | 7.2099 | 0.0748 | | 0.0617 | 21.0 | 1601 | 7.2512 | 0.0734 | | 0.0711 | 21.99 | 1677 | 7.3157 | 0.0813 | | 0.0623 | 22.99 | 1753 | 7.2590 | 0.0791 | | 0.0419 | 24.0 | 1830 | 7.3413 | 0.0712 | | 0.0924 | 25.0 | 1906 | 7.3051 | 0.0784 | | 0.0471 | 25.99 | 1982 | 7.3136 | 0.0763 | | 0.0654 | 26.99 | 2058 | 7.3667 | 0.0734 | | 0.0836 | 28.0 | 2135 | 7.4039 | 0.0770 | | 0.06 | 29.0 | 2211 | 7.3998 | 0.0799 | | 0.0694 | 29.9 | 2280 | 7.3838 | 0.0755 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

Aybars/ModelOnTquad

question-answering

--- license: openrail --- details on https://followfoxai.substack.com/

Aybars/ModelOnWhole

question-answering

--- datasets: - IlyaGusev/gpt_roleplay_realm language: - en pipeline_tag: text-generation --- LLaMA 7B fine-tuned on the English part of the `gpt_roleplay_realm` dataset. Code example: ``` from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig MODEL_NAME = "IlyaGusev/rpr_7b" DEFAULT_MESSAGE_TEMPLATE = "<s>{role}\n{content}</s>\n" class Conversation: def __init__( self, system_prompt, message_template=DEFAULT_MESSAGE_TEMPLATE, start_token_id=1, bot_token_id=9225 ): self.message_template = message_template self.start_token_id = start_token_id self.bot_token_id = bot_token_id self.messages = [{ "role": "system", "content": system_prompt }] def get_start_token_id(self): return self.start_token_id def get_bot_token_id(self): return self.bot_token_id def add_user_message(self, message): self.messages.append({ "role": "user", "content": message }) def add_bot_message(self, message): self.messages.append({ "role": "bot", "content": message }) def get_prompt(self, tokenizer): final_text = "" for message in self.messages: message_text = self.message_template.format(**message) final_text += message_text final_text += tokenizer.decode([self.start_token_id, self.bot_token_id]) return final_text.strip() def generate(model, tokenizer, prompt, generation_config): data = tokenizer(prompt, return_tensors="pt") data = {k: v.to(model.device) for k, v in data.items()} output_ids = model.generate(**data,generation_config=generation_config)[0] output_ids = output_ids[len(data["input_ids"][0]):] output = tokenizer.decode(output_ids, skip_special_tokens=True) return output.strip() config = PeftConfig.from_pretrained(MODEL_NAME) model = AutoModelForCausalLM.from_pretrained( config.base_model_name_or_path, load_in_8bit=True, torch_dtype=torch.float16, device_map="auto" ) model = PeftModel.from_pretrained( model, MODEL_NAME, torch_dtype=torch.float16 ) model.eval() tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) generation_config = GenerationConfig.from_pretrained(MODEL_NAME) print(generation_config) system_prompt = "You are Chiharu Yamada. Chiharu Yamada is a young, computer engineer-nerd with a knack for problem solving and a passion for technology." conversation = Conversation(system_prompt=system_prompt) for inp in inputs: inp = input() conversation.add_user_message(inp) prompt = conversation.get_prompt(tokenizer) output = generate(model, tokenizer, prompt, generation_config) conversation.add_bot_message(output) print(output) ```

Ayjayo/DialoGPT-medium-AyjayoAI

conversational

--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 2109.47 +/- 78.71 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

AyushPJ/ai-club-inductions-21-nlp-roBERTa-base-squad-v2

question-answering

--- license: openrail library_name: diffusers pipeline_tag: text-to-image ---

AyushPJ/test-squad-trained-finetuned-squad

question-answering

--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: roberta-base_mod_squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base_mod_squad This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.0100 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.9367 | 1.0 | 10950 | 0.9221 | | 0.6845 | 2.0 | 21900 | 0.9035 | | 0.4838 | 3.0 | 32850 | 1.0100 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

BSen/wav2vec2-large-xls-r-300m-turkish-colab

automatic-speech-recognition

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: jason-expert-uspto-1.5k-preeval results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # jason-expert-uspto-1.5k-preeval This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1500 ### Training results ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.1+rocm5.4.2 - Datasets 2.11.0 - Tokenizers 0.13.3

Babelscape/rebel-large

text2text-generation

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -37.96 +/- 96.58 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 500000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'irow/ppo-LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```

Bagus/wav2vec2-xlsr-japanese-speech-emotion-recognition

audio-classification

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: tabert-4k-naamapadam results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tabert-4k-naamapadam This model is a fine-tuned version of [livinNector/tabert-4k](https://huggingface.co/livinNector/tabert-4k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2805 - Precision: 0.7758 - Recall: 0.8034 - F1: 0.7894 - Accuracy: 0.9077 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.4467 | 0.05 | 400 | 0.3882 | 0.7144 | 0.6655 | 0.6891 | 0.8755 | | 0.3775 | 0.1 | 800 | 0.3540 | 0.7122 | 0.7155 | 0.7138 | 0.8845 | | 0.3571 | 0.15 | 1200 | 0.3432 | 0.7329 | 0.7266 | 0.7297 | 0.8872 | | 0.3461 | 0.21 | 1600 | 0.3360 | 0.7252 | 0.7368 | 0.7309 | 0.8893 | | 0.3456 | 0.26 | 2000 | 0.3359 | 0.7388 | 0.7470 | 0.7428 | 0.8896 | | 0.3318 | 0.31 | 2400 | 0.3298 | 0.7460 | 0.7435 | 0.7447 | 0.8908 | | 0.326 | 0.36 | 2800 | 0.3255 | 0.7490 | 0.7391 | 0.7440 | 0.8940 | | 0.3264 | 0.41 | 3200 | 0.3243 | 0.7493 | 0.7605 | 0.7549 | 0.8953 | | 0.3189 | 0.46 | 3600 | 0.3231 | 0.7305 | 0.7715 | 0.7504 | 0.8936 | | 0.3119 | 0.51 | 4000 | 0.3125 | 0.7645 | 0.7525 | 0.7584 | 0.8985 | | 0.3111 | 0.57 | 4400 | 0.3100 | 0.7479 | 0.7729 | 0.7602 | 0.8970 | | 0.3088 | 0.62 | 4800 | 0.3148 | 0.7510 | 0.7749 | 0.7628 | 0.8966 | | 0.3047 | 0.67 | 5200 | 0.3089 | 0.7581 | 0.7728 | 0.7654 | 0.8981 | | 0.3054 | 0.72 | 5600 | 0.3073 | 0.7615 | 0.7709 | 0.7662 | 0.8990 | | 0.3028 | 0.77 | 6000 | 0.3066 | 0.7466 | 0.7835 | 0.7646 | 0.8984 | | 0.3007 | 0.82 | 6400 | 0.3035 | 0.7555 | 0.7791 | 0.7671 | 0.8995 | | 0.2923 | 0.87 | 6800 | 0.3004 | 0.7647 | 0.7829 | 0.7737 | 0.9008 | | 0.2927 | 0.93 | 7200 | 0.3050 | 0.7700 | 0.7646 | 0.7673 | 0.9002 | | 0.2949 | 0.98 | 7600 | 0.2979 | 0.7686 | 0.7723 | 0.7704 | 0.9014 | | 0.2758 | 1.03 | 8000 | 0.3013 | 0.7713 | 0.7783 | 0.7748 | 0.9030 | | 0.2699 | 1.08 | 8400 | 0.3019 | 0.7503 | 0.7997 | 0.7742 | 0.9017 | | 0.2688 | 1.13 | 8800 | 0.3002 | 0.7593 | 0.7940 | 0.7762 | 0.9017 | | 0.2625 | 1.18 | 9200 | 0.2926 | 0.7590 | 0.7941 | 0.7762 | 0.9033 | | 0.2671 | 1.23 | 9600 | 0.2922 | 0.7640 | 0.8019 | 0.7825 | 0.9043 | | 0.267 | 1.29 | 10000 | 0.2895 | 0.7719 | 0.7877 | 0.7797 | 0.9044 | | 0.2611 | 1.34 | 10400 | 0.2897 | 0.7704 | 0.7978 | 0.7839 | 0.9053 | | 0.2666 | 1.39 | 10800 | 0.2896 | 0.7688 | 0.7887 | 0.7786 | 0.9042 | | 0.2563 | 1.44 | 11200 | 0.2894 | 0.7672 | 0.7981 | 0.7823 | 0.9045 | | 0.2598 | 1.49 | 11600 | 0.2841 | 0.7705 | 0.7960 | 0.7831 | 0.9058 | | 0.2549 | 1.54 | 12000 | 0.2854 | 0.7695 | 0.7975 | 0.7832 | 0.9065 | | 0.2558 | 1.59 | 12400 | 0.2873 | 0.7619 | 0.8108 | 0.7856 | 0.9045 | | 0.2564 | 1.65 | 12800 | 0.2863 | 0.7757 | 0.7897 | 0.7826 | 0.9062 | | 0.2618 | 1.7 | 13200 | 0.2860 | 0.7778 | 0.7899 | 0.7838 | 0.9066 | | 0.2659 | 1.75 | 13600 | 0.2831 | 0.7748 | 0.8013 | 0.7879 | 0.9073 | | 0.254 | 1.8 | 14000 | 0.2811 | 0.7761 | 0.7978 | 0.7868 | 0.9079 | | 0.2628 | 1.85 | 14400 | 0.2807 | 0.7713 | 0.8028 | 0.7868 | 0.9069 | | 0.2552 | 1.9 | 14800 | 0.2806 | 0.7756 | 0.7990 | 0.7872 | 0.9077 | | 0.2568 | 1.95 | 15200 | 0.2805 | 0.7758 | 0.8034 | 0.7894 | 0.9077 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0 - Datasets 2.12.0 - Tokenizers 0.13.3

Banshee/dialoGPT-luke-small

--- license: apache-2.0 tags: - Composer - MosaicML - llm-foundry - StreamingDatasets datasets: - mc4 - c4 - togethercomputer/RedPajama-Data-1T - bigcode/the-stack - allenai/s2orc inference: false --- # MPT-7B GGML This is GGML format quantised 4-bit, 5-bit and 8-bit GGML models of [MosaicML's MPT-7B](https://huggingface.co/mosaicml/mpt-7b). This repo is the result of converting to GGML and quantising. Please note that these MPT GGMLs are **not compatbile with llama.cpp**. Please see below for a list of tools known to work with these model files. ## Repositories available * [MPT-7B: 4-bit, 5-bit and 8-bit GGML models for CPU (+CUDA) inference](https://huggingface.co/TheBloke/MPT-7B-GGML). * [MPT-7B-Instruct: 4-bit, 5-bit and 8-bit GGML models for CPU (+CUDA) inference](https://huggingface.co/TheBloke/MPT-7B-Instruct-GGML). * [MPT-7B-Storywriter: 4-bit, 5-bit and 8-bit GGML models for CPU (+CUDA) inference](https://huggingface.co/TheBloke/MPT-7B-Storywriter-GGML). ## Provided files | Name | Quant method | Bits | Size | RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | `mpt-7b.ggmlv3.q4_0.bin` | q4_0 | 4bit | 4.16GB | 6.2GB | 4-bit. | `mpt-7b.ggmlv3.q4_1.bin` | q4_0 | 4bit | 4.99GB | 7.2GB | 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. | `mpt-7b.ggmlv3.q5_0.bin` | q5_0 | 5bit | 4.57GB | 6.8GB | 5-bit. Higher accuracy, higher resource usage and slower inference. | `mpt-7b.ggmlv3.q5_1.bin` | q5_1 | 5bit | 4,99GB | 7.2GB | 5-bit. Even higher accuracy, and higher resource usage and slower inference. | `mpt-7b.ggmlv3.q8_0.bin` | q8_0 | 8bit | 7.48GB | 9.6GB | 8-bit. Almost indistinguishable from float16. Huge resource use and slow. Not recommended for normal use. | `mpt-7b.ggmlv3.fp16.bin` | fp16 | 16bit | 13.3GB | 15.5GB | Full 16-bit. | ## Compatibilty These files are **not** compatible with llama.cpp. Currently they can be used with: * The ctransformers Python library, which includes LangChain support: [ctransformers](https://github.com/marella/ctransformers) * The GPT4All-UI which uses ctransformers: [GPT4All-UI](https://github.com/ParisNeo/gpt4all-ui) * [rustformers' llm](https://github.com/rustformers/llm) * The example `mpt` binary provided with [ggml](https://github.com/ggerganov/ggml) As other options become available I will endeavour to update them here (do let me know in the Community tab if I've missed something!) ## How to build, and an example of using the ggml `mpt` binary (command line only): ``` git clone https://github.com/ggerganov/ggml cd ggml mkdir build cd build cmake .. cmake --build . --config Release bin/mpt -m /path/to/mpt-7b.ggmlv3.q4_0.bin -t 8 -n 512 -p "Write a story about llamas" ``` Please see the ggml repo for other build options. # Original model card: MPT-7B MPT-7B is a decoder-style transformer pretrained from scratch on 1T tokens of English text and code. This model was trained by [MosaicML](https://www.mosaicml.com). MPT-7B is part of the family of MosaicPretrainedTransformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference. These architectural changes include performance-optimized layer implementations and the elimination of context length limits by replacing positional embeddings with Attention with Linear Biases ([ALiBi](https://arxiv.org/abs/2108.12409)). Thanks to these modifications, MPT models can be trained with high throughput efficiency and stable convergence. MPT models can also be served efficiently with both standard HuggingFace pipelines and NVIDIA's [FasterTransformer](https://github.com/NVIDIA/FasterTransformer). This model uses the MosaicML LLM codebase, which can be found in the [llm-foundry repository](https://github.com/mosaicml/llm-foundry). It was trained by MosaicML’s NLP team on the [MosaicML platform](https://www.mosaicml.com/training) for LLM pretraining, finetuning, and inference. ### How is this model different? MPT-7B is * **Licensed for the possibility of commercial use** (unlike [LLaMA](https://arxiv.org/abs/2302.13971)). * **Trained on a large amount of data** (1T tokens like [LLaMA](https://arxiv.org/abs/2302.13971) vs. 300B for [Pythia](https://github.com/EleutherAI/pythia), 300B for [OpenLLaMA](https://github.com/openlm-research/open_llama), and 800B for [StableLM](https://github.com/Stability-AI/StableLM)). * **Prepared to handle extremely long inputs** thanks to [ALiBi](https://arxiv.org/abs/2108.12409) (we finetuned [MPT-7B-StoryWriter-65k+](https://huggingface.co/mosaicml/mpt-7b-storywriter) on up to 65k inputs and can handle up to 84k vs. 2k-4k for other open source models). * **Capable of fast training and inference** (via [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) and [FasterTransformer](https://github.com/NVIDIA/FasterTransformer)) * **Equipped with highly efficient open-source training code** via the [llm-foundry repository](https://github.com/mosaicml/llm-foundry) ### Models finetuned off MPT-7B: The following models are finetuned on MPT-7B: * [MPT-7B-StoryWriter-65k+](https://huggingface.co/mosaicml/mpt-7b-storywriter): a model designed to read and write fictional stories with super long context lengths. Built by finetuning MPT-7B with a context length of 65k tokens on a filtered fiction subset of the [books3 dataset](https://huggingface.co/datasets/the_pile_books3). At inference time, thanks to [ALiBi](https://arxiv.org/abs/2108.12409), MPT-7B-StoryWriter-65k+ can extrapolate even beyond 65k tokens. We demonstrate generations as long as 80k tokens on a single A100-80GB GPU in our [blogpost](www.mosaicml.com/blog/mpt-7b). * License: Apache 2.0 * [MPT-7B-Instruct](https://huggingface.co/mosaicml/mpt-7b-instruct): a model for short-form instruction following. Built by finetuning MPT-7B on a [dataset](https://huggingface.co/datasets/mosaicml/dolly_hhrlhf) we also release, derived from the [Databricks Dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) and the [Anthropic Helpful and Harmless (HH-RLHF)](https://huggingface.co/datasets/Anthropic/hh-rlhf) datasets. * License: _CC-By-SA-3.0_ * [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-7b-instruct) * [MPT-7B-Chat](https://huggingface.co/mosaicml/mpt-7b-chat): a chatbot-like model for dialogue generation. Built by finetuning MPT-7B on the [ShareGPT-Vicuna](https://huggingface.co/datasets/jeffwan/sharegpt_vicuna), [HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3), [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca), [HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf), and [Evol-Instruct](https://huggingface.co/datasets/victor123/evol_instruct_70k) datasets. * License: _CC-By-NC-SA-4.0_ * [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-7b-chat) ## Model Date May 5, 2023 ## Model License Apache-2.0 ## Documentation * [Blog post: Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs](https://www.mosaicml.com/blog/mpt-7b) * [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/) * Questions: Feel free to contact us via the [MosaicML Community Slack](https://join.slack.com/t/mosaicml-community/shared_invite/zt-1btms90mc-GipE2ufuPkKY0QBrmF3LSA)! ## How to Use This model is best used with the MosaicML [llm-foundry repository](https://github.com/mosaicml/llm-foundry) for training and finetuning. ```python import transformers model = transformers.AutoModelForCausalLM.from_pretrained( 'mosaicml/mpt-7b', trust_remote_code=True ) ``` Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package. `MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more. To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model with `attn_impl='triton'` and move the model to `bfloat16`: ```python config = transformers.AutoConfig.from_pretrained( 'mosaicml/mpt-7b', trust_remote_code=True ) config.attn_config['attn_impl'] = 'triton' model = transformers.AutoModelForCausalLM.from_pretrained( 'mosaicml/mpt-7b', config=config, torch_dtype=torch.bfloat16, trust_remote_code=True ) model.to(device='cuda:0') ``` Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example: ```python config = transformers.AutoConfig.from_pretrained( 'mosaicml/mpt-7b', trust_remote_code=True ) config.update({"max_seq_len": 4096}) model = transformers.AutoModelForCausalLM.from_pretrained( 'mosaicml/mpt-7b', config=config, trust_remote_code=True ) ``` This model was trained with the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b") ``` ## Model Description The architecture is a modification of a standard decoder-only transformer. The model has been modified from a standard transformer in the following ways: * It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) * It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings * It does not use biases | Hyperparameter | Value | |----------------|-------| |n_parameters | 6.7B | |n_layers | 32 | | n_heads | 32 | | d_model | 4096 | | vocab size | 50432 | | sequence length | 2048 | ## Training Data ### Streaming Datasets Data was formatted using the MosaicML [StreamingDataset](https://github.com/mosaicml/streaming) library to host our data in object storage and efficiently stream it to our compute cluster during training. StreamingDataset obviates the need to download the whole dataset before starting training, and allows instant resumption of training from any point in the dataset. ### Data Mix The model was trained for 1T tokens (with batch size 1760 and sequence length 2048). It was trained on the following data mix: | Data Source | Number of Tokens in Source | Proportion | Effective Number of Tokens | Epochs | |-------------|----------------------------|------------|----------------------------|--------| | mC4 3.1.0 - English | 417.99 B | 0.33 | 330 B | 0.14 | | C4 - English - SemDedup 80% | 100.42 B | 0.299 | 299 B | 2.98 | | RedPajama - CommonCrawl | 878.45 B | 0.1 | 100 B | 0.11 | | The Stack - Selected Languages | 463.78 B | 0.1 | 100 B | 0.22 | | RedPajama - Wikipedia - En | 4.87 B | 0.04 | 40 B | 8.21 | | The Stack - Markdown | 107.07 B | 0.035 | 35 B | 0.33 | | S2ORC | 48.85 B | 0.033 | 33 B | 0.68 | | RedPajama - Books | 26.02 B | 0.03 | 30B | 1.15 | | RedPajama - arXiv | 28.10 B | 0.019 | 19 B | 0.68 | | RedPajama - StackExchange | 20.54 B | 0.014 | 14 B |0.68 | Samples for each batch were selected from one of the datasets with the probability specified above. The examples were shuffled within each dataset, and each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length. The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. This BPE tokenizer has a number of desirable characteristics, most of which are relevant for tokenizing code: (1) It was trained on a diverse mix of data that includes code (The Pile) (2) It applies consistent space delimitation, unlike the GPT2 tokenizer which tokenizes inconsistently depending on the presence of prefix spaces (3) It contains tokens for repeated space characters, which allows superior compression of text with large amounts of repeated space characters. The model vocabulary size of 50432 was set to be a multiple of 128 (as in [MEGATRON-LM](https://arxiv.org/abs/1909.08053)), model flop utilization (MFU) increased by up to four percentage points. ### Training Configuration This model was trained on 440 A100-40GBs for about 9.5 days using the [MosaicML Platform](https://www.mosaicml.com/platform). The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the [LION](https://arxiv.org/abs/2302.06675) optimizer. ## Limitations and Biases _The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_ MPT-7B (Base) is **not** intended for deployment without finetuning. It should not be used for human-facing interactions without further guardrails and user consent. MPT-7B can produce factually incorrect output, and should not be relied on to produce factually accurate information. MPT-7B was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. ## MosaicML Platform If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b). ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes. ## Citation Please cite this model using the following format: ``` @online{MosaicML2023Introducing, author = {MosaicML NLP Team}, title = {Introducing MPT-7B: A New Standard for Open-Source, ly Usable LLMs}, year = {2023}, url = {www.mosaicml.com/blog/mpt-7b}, note = {Accessed: 2023-03-28}, % change this date urldate = {2023-03-28} % change this date } ```

Barbarameerr/Barbara

Access to model Aryan2003/roberta_job is restricted and you are not in the authorized list. Visit https://huggingface.co/Aryan2003/roberta_job to ask for access.

Barleysack/AERoberta

question-answering

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # /var/folders/cr/k5wjffrn18lf95kf3ffg7r_r0000gn/T/tmpjq7bmyjw/DanielaSaavedraL/saleswiz-baseline_is_positive This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("/var/folders/cr/k5wjffrn18lf95kf3ffg7r_r0000gn/T/tmpjq7bmyjw/DanielaSaavedraL/saleswiz-baseline_is_positive") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

Barleysack/klue-roberta-LSTM

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Onlyphish_10K_fromP_BFall_10KGen_topP_0.75 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Onlyphish_10K_fromP_BFall_10KGen_topP_0.75 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0759 - Accuracy: 0.9929 - F1: 0.9193 - Precision: 1.0 - Recall: 0.8506 - Roc Auc Score: 0.9253 - Tpr At Fpr 0.01: 0.8776 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0055 | 1.0 | 13125 | 0.0436 | 0.9901 | 0.8844 | 0.9933 | 0.797 | 0.8984 | 0.7488 | | 0.0032 | 2.0 | 26250 | 0.1145 | 0.9853 | 0.8171 | 0.9994 | 0.691 | 0.8455 | 0.756 | | 0.0025 | 3.0 | 39375 | 0.0705 | 0.9919 | 0.9076 | 0.9978 | 0.8324 | 0.9162 | 0.8332 | | 0.0018 | 4.0 | 52500 | 0.0848 | 0.9919 | 0.9065 | 0.9998 | 0.8292 | 0.9146 | 0.8506 | | 0.0008 | 5.0 | 65625 | 0.0759 | 0.9929 | 0.9193 | 1.0 | 0.8506 | 0.9253 | 0.8776 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2

Barytes/hellohf

fill-mask

Access to model lin12343/1111 is restricted and you are not in the authorized list. Visit https://huggingface.co/lin12343/1111 to ask for access.

Batsy24/DialoGPT-medium-Twilight_BellaBot

conversational

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-large-qa results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-large-qa This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1241 - Rouge1: 77.7328 - Rouge2: 66.0005 - Rougel: 77.1753 - Rougelsum: 77.1453 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 306 | 0.1274 | 77.6174 | 66.4422 | 77.0276 | 77.0702 | 19.0 | | 0.1965 | 2.0 | 612 | 0.1241 | 77.7328 | 66.0005 | 77.1753 | 77.1453 | 19.0 | | 0.1965 | 3.0 | 918 | 0.1310 | 77.8688 | 67.4016 | 77.5375 | 77.5445 | 19.0 | | 0.083 | 4.0 | 1224 | 0.1385 | 78.1193 | 67.0951 | 77.5954 | 77.63 | 19.0 | | 0.0474 | 5.0 | 1530 | 0.1464 | 78.1002 | 67.0309 | 77.5527 | 77.5764 | 19.0 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3