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# bert-finetuned-ner

This model is a fine-tuned version of [dicta-il/dictabert](https://huggingface.co/dicta-il/dictabert) on the nemo_corpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1102
- Precision: 0.8607
- Recall: 0.8528
- F1: 0.8567
- Accuracy: 0.9786

## 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: 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2884        | 1.0   | 618  | 0.1202          | 0.8182    | 0.8006 | 0.8093 | 0.9733   |
| 0.0896        | 2.0   | 1236 | 0.1081          | 0.8298    | 0.8374 | 0.8336 | 0.9771   |
| 0.0548        | 3.0   | 1854 | 0.1102          | 0.8607    | 0.8528 | 0.8567 | 0.9786   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1+cpu
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["nemo_corpus"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "dicta-il/dictabert", "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "nemo_corpus", "type": "nemo_corpus", "config": "flat_token", "split": "validation", "args": "flat_token"}, "metrics": [{"type": "precision", "value": 0.8606811145510835, "name": "Precision"}, {"type": "recall", "value": 0.852760736196319, "name": "Recall"}, {"type": "f1", "value": 0.8567026194144837, "name": "F1"}, {"type": "accuracy", "value": 0.9786301369863014, "name": "Accuracy"}]}]}]}
transformers tensorboard safetensors bert token-classification generated_from_trainer dataset:nemo_corpus base_model:dicta-il/dictabert license:cc-by-4.0 model-index autotrain_compatible endpoints_compatible region:us