| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - tweet_eval |
| | metrics: |
| | - precision |
| | - recall |
| | base_model: distilbert-base-cased |
| | model-index: |
| | - name: bert-emotion |
| | results: |
| | - task: |
| | type: text-classification |
| | name: Text Classification |
| | dataset: |
| | name: tweet_eval |
| | type: tweet_eval |
| | config: emotion |
| | split: validation |
| | args: emotion |
| | metrics: |
| | - type: precision |
| | value: 0.7505623807659564 |
| | name: Precision |
| | - type: recall |
| | value: 0.7243031825553111 |
| | name: Recall |
| | --- |
| | |
| | <!-- 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-emotion |
| |
|
| | This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.1413 |
| | - Precision: 0.7506 |
| | - Recall: 0.7243 |
| | - Fscore: 0.7340 |
| | |
| | ## 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: 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: 3 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| |
| | | 0.8556 | 1.0 | 815 | 0.7854 | 0.7461 | 0.5929 | 0.6088 | |
| | | 0.5369 | 2.0 | 1630 | 0.9014 | 0.7549 | 0.7278 | 0.7359 | |
| | | 0.2571 | 3.0 | 2445 | 1.1413 | 0.7506 | 0.7243 | 0.7340 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.29.2 |
| | - Pytorch 2.0.1+cu118 |
| | - Datasets 2.12.0 |
| | - Tokenizers 0.13.3 |
| | |
