Datasets:

ftopal

/

huggingface-models-raw

like 0

text-generation

transformers

# Stark DialoGPT Model

{"tags": ["conversational"]}

ArJakusz/DialoGPT-small-stark

2022-03-02T23:29:04+00:00

{}

ArJakusz/DialoGPT-small-starky

2022-03-02T23:29:04+00:00

{}

Araby/Arabic-TTS

2022-03-02T23:29:04+00:00

{}

Aracatto/Catto

2022-03-02T23:29:04+00:00

{}

Araf/Ummah

2022-03-02T23:29:04+00:00

{}

AragornII/DialoGPT-small-harrypotter

2022-03-02T23:29:04+00:00

text-generation

transformers

# Harry Potter DialoGPT Model

{"tags": ["conversational"]}

Aran/DialoGPT-medium-harrypotter

2022-03-02T23:29:04+00:00

text-generation

transformers

# Harry Potter DialoGPT Model

{"tags": ["conversational"]}

Aran/DialoGPT-small-harrypotter

2022-03-02T23:29:04+00:00

{}

ArashEsk95/bert-base-uncased-finetuned-cola

2022-03-02T23:29:04+00:00

{}

ArashEsk95/bert-base-uncased-finetuned-sst2

2022-03-02T23:29:04+00:00

{}

ArashEsk95/bert-base-uncased-finetuned-stsb

2022-03-02T23:29:04+00:00

{}

Aravinth/test

2022-03-02T23:29:04+00:00

{}

ArcQ/gpt-experiments

2022-03-02T23:29:04+00:00

{}

Arcanos/1

2022-03-02T23:29:04+00:00

{}

Archie/myProject

2022-03-02T23:29:04+00:00

text-generation

transformers

# Rick DialoGPT Model

{"tags": ["conversational"]}

Arcktosh/DialoGPT-small-rick

2022-03-02T23:29:04+00:00

{}

ArenaGrenade/char-cnn

2022-03-02T23:29:04+00:00

{}

Arghyad/Loki_small

2022-03-02T23:29:04+00:00

text-generation

transformers

# Cultured Kumiko DialoGPT Model

{"tags": ["conversational"]}

AriakimTaiyo/DialoGPT-cultured-Kumiko

2022-03-02T23:29:04+00:00

text-generation

# Medium Kumiko DialoGPT Model

{"tags": ["conversational"]}

AriakimTaiyo/DialoGPT-medium-Kumiko

2022-03-02T23:29:04+00:00

text-generation

transformers

# Revised Kumiko DialoGPT Model

{"tags": ["conversational"]}

AriakimTaiyo/DialoGPT-revised-Kumiko

2022-03-02T23:29:04+00:00

text-generation

transformers

# Kumiko DialoGPT Model

{"tags": ["conversational"]}

AriakimTaiyo/DialoGPT-small-Kumiko

2022-03-02T23:29:04+00:00

text-generation

transformers

# Rikka DialoGPT Model

{"tags": ["conversational"]}

AriakimTaiyo/DialoGPT-small-Rikka

2022-03-02T23:29:04+00:00

a

{}

AriakimTaiyo/kumiko

2022-03-02T23:29:04+00:00

text2text-generation

transformers

{}

Aries/T5_question_answering

2022-03-02T23:29:04+00:00

text2text-generation

transformers

{}

Aries/T5_question_generation

2022-03-02T23:29:04+00:00

{}

Arina/Erine

2022-03-02T23:29:04+00:00

{}

ArjunKadya/HuggingFace

2022-03-02T23:29:04+00:00

{}

Arkadiusz/Test-model

2022-03-02T23:29:04+00:00

{}

asaakyan/mbart-poetic-all

2022-03-02T23:29:04+00:00

{}

ArnaudPannatier/MLPMixer

2022-03-02T23:29:04+00:00

{}

Arnold/common_voiceha

2022-03-02T23:29:04+00:00

{}

Arnold/wav2vec2-hausa-demo-colab

2022-03-02T23:29:04+00:00

automatic-speech-recognition

transformers

<!-- 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. --> # wav2vec2-hausa2-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.2032 - Wer: 0.7237 ## 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1683 | 12.49 | 400 | 1.0279 | 0.7211 | | 0.0995 | 24.98 | 800 | 1.2032 | 0.7237 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-hausa2-demo-colab", "results": []}]}

Arnold/wav2vec2-hausa2-demo-colab

2022-03-02T23:29:04+00:00

automatic-speech-recognition

transformers

<!-- 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. --> # wav2vec2-large-xlsr-hausa2-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.2993 - Wer: 0.4826 ## 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: 9.6e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 13 - gradient_accumulation_steps: 3 - total_train_batch_size: 36 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.1549 | 12.5 | 400 | 2.7289 | 1.0 | | 2.0566 | 25.0 | 800 | 0.4582 | 0.6768 | | 0.4423 | 37.5 | 1200 | 0.3037 | 0.5138 | | 0.2991 | 50.0 | 1600 | 0.2993 | 0.4826 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-hausa2-demo-colab", "results": []}]}

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

2022-03-02T23:29:04+00:00

{}

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

2022-03-02T23:29:04+00:00

text-classification

transformers

<!-- 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: 0.2295 - Accuracy: 0.92 - F1: 0.9202 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8187 | 1.0 | 250 | 0.3137 | 0.902 | 0.8983 | | 0.2514 | 2.0 | 500 | 0.2295 | 0.92 | 0.9202 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.92, "name": "Accuracy"}, {"type": "f1", "value": 0.9201604193183255, "name": "F1"}]}]}]}

Aron/distilbert-base-uncased-finetuned-emotion

2022-03-02T23:29:04+00:00

question-answering

transformers

{}

ArpanZS/debug_squad

2022-03-02T23:29:04+00:00

{}

ArpanZS/search_model

2022-03-02T23:29:04+00:00

text2text-generation

transformers

{}

Arpita/opus-mt-en-ro-finetuned-syn-to-react

2022-03-02T23:29:04+00:00

{}

Arpita/opus-mt-en-ro-finetuned-synthon-to-reactant

2022-03-02T23:29:04+00:00

token-classification

transformers

{}

ArseniyBolotin/bert-multi-PAD-ner

2022-03-02T23:29:04+00:00

{}

ArshdeepSekhon050/DialoGPT-medium-RickAndMorty

2022-03-02T23:29:04+00:00

text-generation

transformers

#Okarin Bot

{"tags": ["conversational"]}

ArtemisZealot/DialoGTP-small-Qkarin

2022-03-02T23:29:04+00:00

{}

ArthurBaia/bert-base-portuguese-cased-finetuned-squad

2022-03-02T23:29:04+00:00

{}

ArthurcJP/DialoGPT-small-YODA

2022-03-02T23:29:04+00:00

text-generation

transformers

# Harry Potter DialoGPT Model

{"tags": ["conversational"]}

Aruden/DialoGPT-medium-harrypotterall

2022-03-02T23:29:04+00:00

text2text-generation

transformers

``` from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model = AutoModelForSeq2SeqLM.from_pretrained("ArvinZhuang/BiTAG-t5-large") tokenizer = AutoTokenizer.from_pretrained("ArvinZhuang/BiTAG-t5-large") text = "abstract: [your abstract]" # use 'title:' as the prefix for title_to_abs task. input_ids = tokenizer.encode(text, return_tensors='pt') outputs = model.generate( input_ids, do_sample=True, max_length=500, top_p=0.9, top_k=20, temperature=1, num_return_sequences=10, ) print("Output:\n" + 100 * '-') for i, output in enumerate(outputs): print("{}: {}".format(i+1, tokenizer.decode(output, skip_special_tokens=True))) ``` GitHub: https://github.com/ArvinZhuang/BiTAG

{"inference": {"parameters": {"do_sample": true, "max_length": 500, "top_p": 0.9, "top_k": 20, "temperature": 1, "num_return_sequences": 10}}, "widget": [{"text": "abstract: We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).", "example_title": "BERT abstract"}]}

ielabgroup/BiTAG-t5-large

2022-03-02T23:29:04+00:00

text2text-generation

transformers

# Model Trained Using AutoNLP - Model: Google's Pegasus (https://huggingface.co/google/pegasus-xsum) - Problem type: Summarization - Model ID: 34558227 - CO2 Emissions (in grams): 137.60574081887984 - Spaces: https://huggingface.co/spaces/TitleGenerators/ArxivTitleGenerator - Dataset: arXiv Dataset (https://www.kaggle.com/Cornell-University/arxiv) - Data subset used: https://huggingface.co/datasets/AryanLala/autonlp-data-Scientific_Title_Generator ## Validation Metrics - Loss: 2.578599214553833 - Rouge1: 44.8482 - Rouge2: 24.4052 - RougeL: 40.1716 - RougeLsum: 40.1396 - Gen Len: 11.4675 ## Social - LinkedIn: https://www.linkedin.com/in/aryanlala/ - Twitter: https://twitter.com/AryanLala20 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' /static-proxy?url=https%3A%2F%2Fapi-inference.huggingface.co%2FAryanLala%2Fautonlp-Scientific_Title_Generator-34558227 ```

{"language": "en", "tags": "autonlp", "datasets": ["AryanLala/autonlp-data-Scientific_Title_Generator"], "widget": [{"text": "The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community library for contemporary NLP designed to support this ecosystem. Datasets aims to standardize end-user interfaces, versioning, and documentation, while providing a lightweight front-end that behaves similarly for small datasets as for internet-scale corpora. The design of the library incorporates a distributed, community-driven approach to adding datasets and documenting usage. After a year of development, the library now includes more than 650 unique datasets, has more than 250 contributors, and has helped support a variety of novel cross-dataset research projects and shared tasks. The library is available at https://github.com/huggingface/datasets."}], "co2_eq_emissions": 137.60574081887984}

AryanLala/autonlp-Scientific_Title_Generator-34558227

2022-03-02T23:29:04+00:00

{}

AshLukass/AshLukass

2022-03-02T23:29:04+00:00

{}

Ashagi/Ashvx

2022-03-02T23:29:04+00:00

{}

AshiNLP/Bert_model

2022-03-02T23:29:04+00:00

{}

Ashim/dga-transformer

2022-03-02T23:29:04+00:00

fill-mask

transformers

<!-- 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. --> # bert-base-parsbert-uncased-finetuned This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.2045 ## 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: 8 - 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 | |:-------------:|:-----:|:----:|:---------------:| | 3.5596 | 1.0 | 515 | 3.2097 | ### Framework versions - Transformers 4.10.0 - Pytorch 1.9.0+cu102 - Datasets 1.11.0 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"]}

Ashkanmh/bert-base-parsbert-uncased-finetuned

2022-03-02T23:29:04+00:00

{}

Ashl3y/model_name

2022-03-02T23:29:04+00:00

{}

Ashok/my-new-tokenizer

2022-03-02T23:29:04+00:00

{}

AshtonBenson/DialoGPT-small-quentin-coldwater

2022-03-02T23:29:04+00:00

{}

AshtonBenson/DialoGPT-small-quentin

2022-03-02T23:29:04+00:00

text-generation

transformers

A discord chatbot trained on the whole LiS script to simulate character speech

{"tags": ["conversational"]}

Aspect11/DialoGPT-Medium-LiSBot

2022-03-02T23:29:04+00:00

text-generation

transformers

# RinTohsaka bot

{"tags": ["conversational"]}

Asuramaru/DialoGPT-small-rintohsaka

2022-03-02T23:29:04+00:00

{}

At3ee/wav2vec2-base-timit-demo-colab

2022-03-02T23:29:04+00:00

text-generation

transformers

GPT-Glacier, a GPT-Neo 125M model finetuned on the Glacier2 Modding Discord server.

{}

Atampy26/GPT-Glacier

2022-03-02T23:29:04+00:00

{}

Atarax/rick

2022-03-02T23:29:04+00:00

{}

Atchuth/DialoGPT-small-MBOT

2022-03-02T23:29:04+00:00

text-generation

transformers

# Michael Scott DialoGPT Model

{"tags": ["conversational"]}

Atchuth/DialoGPT-small-MichaelBot

2022-03-02T23:29:04+00:00

{}

Atchuth/MBOT

2022-03-02T23:29:04+00:00

text-classification

transformers

{}

Ateeb/EmotionDetector

2022-03-02T23:29:04+00:00

text-classification

transformers

{}

Ateeb/FullEmotionDetector

2022-03-02T23:29:04+00:00

question-answering

transformers

{}

Ateeb/QA

2022-03-02T23:29:04+00:00

{}

Ateeb/SquadQA

2022-03-02T23:29:04+00:00

{}

Ateeb/asd

2022-03-02T23:29:04+00:00

{"license": "artistic-2.0"}

Atiqah/Atiqah

2022-03-02T23:29:04+00:00

Placeholder

{}

Atlasky/Turkish-Negator

2022-03-02T23:29:04+00:00

{}

Atlasky/turkish-negator-nn

2022-03-02T23:29:04+00:00

{}

Augustab/distilbert-base-uncased-finetuned-cola

2022-03-02T23:29:04+00:00

text-generation

transformers

#MyAwesomeModel

{"tags": ["conversational"]}

Augustvember/WOKKAWOKKA

2022-03-02T23:29:04+00:00

{}

Augustvember/WokkaBot

2022-03-02T23:29:04+00:00

{}

Augustvember/WokkaBot2

2022-03-02T23:29:04+00:00

text-generation

{"tags": ["conversational"]}

Augustvember/WokkaBot3

2022-03-02T23:29:04+00:00

{}

Augustvember/WokkaBot4

2022-03-02T23:29:04+00:00

{}

Augustvember/WokkaBot5

2022-03-02T23:29:04+00:00

{}

Augustvember/WokkaBot6

2022-03-02T23:29:04+00:00

{}

Augustvember/WokkaBot7

2022-03-02T23:29:04+00:00

{}

Augustvember/WokkaBot8

2022-03-02T23:29:04+00:00

{}

Augustvember/WokkaBot9

2022-03-02T23:29:04+00:00

{}

Augustvember/WokkaBot99

2022-03-02T23:29:04+00:00

{}

Augustvember/WokkaBotF

2022-03-02T23:29:04+00:00

text-generation

transformers

#MyAwesomeModel

{"tags": ["conversational"]}

Augustvember/test

2022-03-02T23:29:04+00:00

text-generation

transformers

{}

Augustvember/wokka

2022-03-02T23:29:04+00:00

text-generation

transformers

{"tags": ["conversational"]}

Augustvember/wokka2

2022-03-02T23:29:04+00:00

text-generation

{"tags": ["conversational"]}

Augustvember/wokka4

2022-03-02T23:29:04+00:00

text-generation

transformers

#MyAwesomeModel

{"tags": ["conversational"]}

Augustvember/wokka5

2022-03-02T23:29:04+00:00

text-generation

transformers

#MyAwesomeModel

{"tags": ["conversational"]}

Augustvember/wokkabottest2

2022-03-02T23:29:04+00:00

{}

Augustvember/your-model-name

2022-03-02T23:29:04+00:00

https://www.geogebra.org/m/bbuczchu https://www.geogebra.org/m/xwyasqje https://www.geogebra.org/m/mx2cqkwr https://www.geogebra.org/m/tkqqqthm https://www.geogebra.org/m/asdaf9mj https://www.geogebra.org/m/ywuaj7p5 https://www.geogebra.org/m/jkfkayj3 https://www.geogebra.org/m/hptnn7ar https://www.geogebra.org/m/de9cwmrf https://www.geogebra.org/m/yjc5hdep https://www.geogebra.org/m/nm8r56w5 https://www.geogebra.org/m/j7wfcpxj

{}

Aurora/asdawd

2022-03-02T23:29:04+00:00

https://community.afpglobal.org/network/members/profile?UserKey=b0b38adc-86c7-4d30-85c6-ac7d15c5eeb0 https://community.afpglobal.org/network/members/profile?UserKey=f4ddef89-b508-4695-9d1e-3d4d1a583279 https://community.afpglobal.org/network/members/profile?UserKey=36081479-5e7b-41ba-8370-ecf72989107a https://community.afpglobal.org/network/members/profile?UserKey=e1a88332-be7f-4997-af4e-9fcb7bb366da https://community.afpglobal.org/network/members/profile?UserKey=4738b405-2017-4025-9e5f-eadbf7674840 https://community.afpglobal.org/network/members/profile?UserKey=eb96d91c-31ae-46e1-8297-a3c8551f2e6a https://u.mpi.org/network/members/profile?UserKey=9867e2d9-d22a-4dab-8bcf-3da5c2f30745 https://u.mpi.org/network/members/profile?UserKey=5af232f2-a66e-438f-a5ab-9768321f791d https://community.afpglobal.org/network/members/profile?UserKey=481305df-48ea-4c50-bca4-a82008efb427 https://u.mpi.org/network/members/profile?UserKey=039fbb91-52c6-40aa-b58d-432fb4081e32 https://www.geogebra.org/m/jkfkayj3 https://www.geogebra.org/m/hptnn7ar https://www.geogebra.org/m/de9cwmrf https://www.geogebra.org/m/yjc5hdep https://www.geogebra.org/m/nm8r56w5 https://www.geogebra.org/m/j7wfcpxj https://www.geogebra.org/m/bbuczchu https://www.geogebra.org/m/xwyasqje https://www.geogebra.org/m/mx2cqkwr https://www.geogebra.org/m/tkqqqthm https://www.geogebra.org/m/asdaf9mj https://www.geogebra.org/m/ywuaj7p5

{}

Aurora/community.afpglobal

2022-03-02T23:29:04+00:00

text-generation

transformers

# Blitzo DialoGPT Model

{"tags": ["conversational"]}

AvatarXD/DialoGPT-medium-Blitzo

2022-03-02T23:29:04+00:00

# w2v with news

{}

Aviora/news2vec

2022-03-02T23:29:04+00:00

{}

Aviora/phobert-ner

2022-03-02T23:29:04+00:00

text-generation

transformers

# Eren Yeager DialoGPT Model

{"tags": ["conversational"]}

Awsaf/DialoGPT-medium-eren

2022-03-02T23:29:04+00:00