| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - tweet_eval | |
| metrics: | |
| - accuracy | |
| - f1 | |
| base_model: google/bert_uncased_L-2_H-128_A-2 | |
| model-index: | |
| - name: tiny-vanilla-target-tweet | |
| results: | |
| - task: | |
| type: text-classification | |
| name: Text Classification | |
| dataset: | |
| name: tweet_eval | |
| type: tweet_eval | |
| config: emotion | |
| split: train | |
| args: emotion | |
| metrics: | |
| - type: accuracy | |
| value: 0.7032085561497327 | |
| name: Accuracy | |
| - type: f1 | |
| value: 0.704229444708009 | |
| name: F1 | |
| <!-- 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. --> | |
| # tiny-vanilla-target-tweet | |
| This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the tweet_eval dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.9887 | |
| - Accuracy: 0.7032 | |
| - F1: 0.7042 | |
| ## 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: 32 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: constant | |
| - num_epochs: 200 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | |
| | 1.1604 | 4.9 | 500 | 0.9784 | 0.6604 | 0.6290 | | |
| | 0.7656 | 9.8 | 1000 | 0.8273 | 0.7139 | 0.6905 | | |
| | 0.534 | 14.71 | 1500 | 0.8138 | 0.7219 | 0.7143 | | |
| | 0.3832 | 19.61 | 2000 | 0.8591 | 0.7086 | 0.7050 | | |
| | 0.2722 | 24.51 | 2500 | 0.9250 | 0.7112 | 0.7118 | | |
| | 0.1858 | 29.41 | 3000 | 0.9887 | 0.7032 | 0.7042 | | |
| ### Framework versions | |
| - Transformers 4.25.1 | |
| - Pytorch 1.12.1 | |
| - Datasets 2.7.1 | |
| - Tokenizers 0.13.2 | |