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: 1.1060
- Precision: 0.7250
- Recall: 0.7243
- Fscore: 0.7225
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 |
---|---|---|---|---|---|---|
0.8399 | 1.0 | 815 | 0.6333 | 0.7495 | 0.7146 | 0.7265 |
0.5379 | 2.0 | 1630 | 0.8113 | 0.7660 | 0.7188 | 0.7357 |
0.2567 | 3.0 | 2445 | 1.1060 | 0.7250 | 0.7243 | 0.7225 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0
- Downloads last month
- 0
Finetuned from
Dataset used to train brian-dlee/bert-emotion
Evaluation results
- Precision on tweet_evalvalidation set self-reported0.725
- Recall on tweet_evalvalidation set self-reported0.724