bert-emotion
This model is a fine-tuned version of distilbert-base-cased on the tweet_eval dataset. It achieves the following results on the evaluation set:
- Loss: 1.2007
- Precision: 0.7413
- Recall: 0.7200
- Fscore: 0.7268
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 |
|---|---|---|---|---|---|---|
| 0.8416 | 1.0 | 815 | 0.7683 | 0.7000 | 0.7141 | 0.7062 |
| 0.5465 | 2.0 | 1630 | 0.8561 | 0.7640 | 0.6735 | 0.6979 |
| 0.2747 | 3.0 | 2445 | 1.2007 | 0.7413 | 0.7200 | 0.7268 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for zhangpn/bert-emotion
Base model
distilbert/distilbert-base-casedDataset used to train zhangpn/bert-emotion
Evaluation results
- Precision on tweet_evalvalidation set self-reported0.741
- Recall on tweet_evalvalidation set self-reported0.720