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# PubMedBERT_CRAFT_NER

This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1031
- Precision: 0.8429
- Recall: 0.8679
- F1: 0.8552
- Accuracy: 0.9734

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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   | 347  | 0.1280          | 0.7851    | 0.8360 | 0.8097 | 0.9647   |
| 0.1944        | 2.0   | 695  | 0.1092          | 0.8187    | 0.8615 | 0.8395 | 0.9707   |
| 0.046         | 3.0   | 1041 | 0.1031          | 0.8429    | 0.8679 | 0.8552 | 0.9734   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext", "model-index": [{"name": "PubMedBERT_CRAFT_NER", "results": []}]}
transformers tensorboard safetensors bert token-classification generated_from_trainer base_model:microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext license:mit autotrain_compatible endpoints_compatible region:us