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# pretoxtm-ner

This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1823
- Study Test: {'precision': 0.7779111644657863, 'recall': 0.9012517385257302, 'f1': 0.8350515463917525, 'number': 719}
- Manifestation: {'precision': 0.8337950138504155, 'recall': 0.9148936170212766, 'f1': 0.872463768115942, 'number': 329}
- Finding: {'precision': 0.7869535045107564, 'recall': 0.7935619314205739, 'f1': 0.7902439024390244, 'number': 1429}
- Specimen: {'precision': 0.7981530343007915, 'recall': 0.8344827586206897, 'f1': 0.8159136884693189, 'number': 725}
- Dose: {'precision': 0.8948247078464107, 'recall': 0.9403508771929825, 'f1': 0.9170230966638152, 'number': 570}
- Dose Qualification: {'precision': 0.696969696969697, 'recall': 0.8070175438596491, 'f1': 0.7479674796747967, 'number': 57}
- Sex: {'precision': 0.945, 'recall': 0.9356435643564357, 'f1': 0.9402985074626865, 'number': 202}
- Group: {'precision': 0.6666666666666666, 'recall': 0.8571428571428571, 'f1': 0.75, 'number': 112}

## 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: 5e-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 | Study Test                                                                                               | Manifestation                                                                                            | Finding                                                                                                   | Specimen                                                                                                 | Dose                                                                                                     | Dose Qualification                                                                                       | Sex                                                                                                      | Group                                                                                                    |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|
| No log        | 1.0   | 257  | 0.2018          | {'precision': 0.7382075471698113, 'recall': 0.8706536856745479, 'f1': 0.7989789406509252, 'number': 719} | {'precision': 0.8271954674220963, 'recall': 0.8875379939209727, 'f1': 0.8563049853372434, 'number': 329} | {'precision': 0.7308461025982678, 'recall': 0.7676696990902729, 'f1': 0.7488054607508532, 'number': 1429} | {'precision': 0.7324290998766955, 'recall': 0.8193103448275862, 'f1': 0.7734375, 'number': 725}          | {'precision': 0.8511705685618729, 'recall': 0.8929824561403509, 'f1': 0.8715753424657534, 'number': 570} | {'precision': 0.5555555555555556, 'recall': 0.43859649122807015, 'f1': 0.4901960784313725, 'number': 57} | {'precision': 0.9285714285714286, 'recall': 0.900990099009901, 'f1': 0.914572864321608, 'number': 202}   | {'precision': 0.5214723926380368, 'recall': 0.7589285714285714, 'f1': 0.6181818181818183, 'number': 112} |
| 0.2702        | 2.0   | 514  | 0.1916          | {'precision': 0.7688622754491018, 'recall': 0.8929068150208623, 'f1': 0.8262548262548264, 'number': 719} | {'precision': 0.7885117493472585, 'recall': 0.9179331306990881, 'f1': 0.848314606741573, 'number': 329}  | {'precision': 0.7945707997065297, 'recall': 0.7578726382085375, 'f1': 0.7757879656160458, 'number': 1429} | {'precision': 0.7962466487935657, 'recall': 0.8193103448275862, 'f1': 0.8076138681169274, 'number': 725} | {'precision': 0.867430441898527, 'recall': 0.9298245614035088, 'f1': 0.8975444538526672, 'number': 570}  | {'precision': 0.71875, 'recall': 0.8070175438596491, 'f1': 0.7603305785123967, 'number': 57}             | {'precision': 0.9633507853403142, 'recall': 0.9108910891089109, 'f1': 0.9363867684478371, 'number': 202} | {'precision': 0.6217948717948718, 'recall': 0.8660714285714286, 'f1': 0.7238805970149254, 'number': 112} |
| 0.2702        | 3.0   | 771  | 0.1823          | {'precision': 0.7779111644657863, 'recall': 0.9012517385257302, 'f1': 0.8350515463917525, 'number': 719} | {'precision': 0.8337950138504155, 'recall': 0.9148936170212766, 'f1': 0.872463768115942, 'number': 329}  | {'precision': 0.7869535045107564, 'recall': 0.7935619314205739, 'f1': 0.7902439024390244, 'number': 1429} | {'precision': 0.7981530343007915, 'recall': 0.8344827586206897, 'f1': 0.8159136884693189, 'number': 725} | {'precision': 0.8948247078464107, 'recall': 0.9403508771929825, 'f1': 0.9170230966638152, 'number': 570} | {'precision': 0.696969696969697, 'recall': 0.8070175438596491, 'f1': 0.7479674796747967, 'number': 57}   | {'precision': 0.945, 'recall': 0.9356435643564357, 'f1': 0.9402985074626865, 'number': 202}              | {'precision': 0.6666666666666666, 'recall': 0.8571428571428571, 'f1': 0.75, 'number': 112}               |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

{"tags": ["generated_from_trainer"], "base_model": "dmis-lab/biobert-v1.1", "model-index": [{"name": "pretoxtm-ner", "results": []}]}
transformers pytorch tensorboard safetensors bert token-classification generated_from_trainer base_model:dmis-lab/biobert-v1.1 autotrain_compatible endpoints_compatible region:us