bert-base-uncased-finetuned-ner
This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0712
- Precision: 0.8945
- Recall: 0.9182
- F1: 0.9062
- Accuracy: 0.9793
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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- total_eval_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- training precision: Mixed Precision
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1318 | 1.0 | 219 | 0.0967 | 0.8371 | 0.8714 | 0.8539 | 0.9705 |
| 0.0597 | 2.0 | 438 | 0.0735 | 0.8912 | 0.9052 | 0.8981 | 0.9779 |
| 0.0523 | 3.0 | 657 | 0.0712 | 0.8945 | 0.9182 | 0.9062 | 0.9793 |
Framework versions
- Transformers 4.20.0
- Pytorch 1.10.0+cpu
- Datasets 2.4.0
- Tokenizers 0.12.1