sentiment-bert-base-uncased
This model is a fine-tuned version of google-bert/bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3179
- Precision: 0.8880
- Recall: 0.8902
- F1: 0.8891
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
---|---|---|---|---|---|---|
0.3727 | 0.9990 | 512 | 0.3047 | 0.8582 | 0.8804 | 0.8546 |
0.2802 | 2.0 | 1025 | 0.3083 | 0.8914 | 0.8780 | 0.8837 |
0.1985 | 2.9971 | 1536 | 0.3179 | 0.8880 | 0.8902 | 0.8891 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.15.0
- Tokenizers 0.19.1
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