| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - emotion | |
| metrics: | |
| - accuracy | |
| base_model: distilbert-base-uncased | |
| model-index: | |
| - name: text-emotion | |
| results: | |
| - task: | |
| type: text-classification | |
| name: Text Classification | |
| dataset: | |
| name: emotion | |
| type: emotion | |
| config: default | |
| split: train | |
| args: default | |
| metrics: | |
| - type: accuracy | |
| value: 0.93675 | |
| name: Accuracy | |
| <!-- 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. --> | |
| # text-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: 0.1414 | |
| - Accuracy: 0.9367 | |
| ## 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: 256 | |
| - eval_batch_size: 512 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - num_epochs: 5 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 1.0232 | 1.0 | 63 | 0.2424 | 0.917 | | |
| | 0.1925 | 2.0 | 126 | 0.1600 | 0.934 | | |
| | 0.1134 | 3.0 | 189 | 0.1418 | 0.935 | | |
| | 0.076 | 4.0 | 252 | 0.1461 | 0.931 | | |
| | 0.0604 | 5.0 | 315 | 0.1414 | 0.9367 | | |
| ### Framework versions | |
| - Transformers 4.24.0 | |
| - Pytorch 1.12.1+cu113 | |
| - Datasets 2.6.1 | |
| - Tokenizers 0.13.2 | |