| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - emotion | |
| metrics: | |
| - accuracy | |
| - f1 | |
| base_model: distilbert-base-uncased | |
| model-index: | |
| - name: distilbert-base-uncased-finetuned-emotion | |
| results: | |
| - task: | |
| type: text-classification | |
| name: Text Classification | |
| dataset: | |
| name: emotion | |
| type: emotion | |
| config: split | |
| split: validation | |
| args: split | |
| metrics: | |
| - type: accuracy | |
| value: 0.9235 | |
| name: Accuracy | |
| - type: f1 | |
| value: 0.9234262976270148 | |
| 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. --> | |
| # 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: 0.2277 | |
| - Accuracy: 0.9235 | |
| - F1: 0.9234 | |
| ## 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | |
| | 0.8525 | 1.0 | 250 | 0.3323 | 0.9025 | 0.8987 | | |
| | 0.2616 | 2.0 | 500 | 0.2277 | 0.9235 | 0.9234 | | |
| ### Framework versions | |
| - Transformers 4.29.2 | |
| - Pytorch 2.0.1 | |
| - Datasets 2.12.0 | |
| - Tokenizers 0.13.3 | |