<!-- 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. -->
phrasebank-sentiment-analysis
This model is a fine-tuned version of bert-base-uncased on the financial_phrasebank dataset. It achieves the following results on the evaluation set:
- Loss: 0.6150
- F1: 0.8371
- Accuracy: 0.8521
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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|---|---|---|---|---|---|
| 0.5676 | 0.94 | 100 | 0.4433 | 0.7961 | 0.8253 |
| 0.2504 | 1.89 | 200 | 0.4137 | 0.8321 | 0.8542 |
| 0.1266 | 2.83 | 300 | 0.5256 | 0.8452 | 0.8597 |
| 0.0501 | 3.77 | 400 | 0.6150 | 0.8371 | 0.8521 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
