distilbert-finetuned-ner-ontonotes
This model is a fine-tuned version of distilbert-base-cased on the ontonotes5 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1448
- Precision: 0.8535
- Recall: 0.8789
- F1: 0.8660
- Accuracy: 0.9750
Model description
Token classification experiment, NER, on business topics.
Intended uses & limitations
The model can be used on token classification, in particular NER. It is fine tuned on business domain.
Training and evaluation data
The dataset used is ontonotes5
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0937 | 1.0 | 7491 | 0.0998 | 0.8367 | 0.8587 | 0.8475 | 0.9731 |
| 0.0572 | 2.0 | 14982 | 0.1084 | 0.8338 | 0.8759 | 0.8543 | 0.9737 |
| 0.0403 | 3.0 | 22473 | 0.1145 | 0.8521 | 0.8707 | 0.8613 | 0.9748 |
| 0.0265 | 4.0 | 29964 | 0.1222 | 0.8535 | 0.8815 | 0.8672 | 0.9752 |
| 0.0148 | 5.0 | 37455 | 0.1365 | 0.8536 | 0.8770 | 0.8651 | 0.9747 |
| 0.0111 | 6.0 | 44946 | 0.1448 | 0.8535 | 0.8789 | 0.8660 | 0.9750 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1