distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of distilbert-base-uncased on the fin dataset. It achieves the following results on the evaluation set:
- Loss: 0.0785
- Precision: 0.8899
- Recall: 0.6953
- F1: 0.7807
- Accuracy: 0.9772
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: 32
- eval_batch_size: 32
- 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 | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 32 | 0.2231 | 0.0 | 0.0 | 0.0 | 0.9372 |
| No log | 2.0 | 64 | 0.0968 | 0.9652 | 0.6953 | 0.8083 | 0.9772 |
| No log | 3.0 | 96 | 0.0785 | 0.8899 | 0.6953 | 0.7807 | 0.9772 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1