transformers pytorch bert token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
---
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: New_BioRED_model_1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# New_BioRED_model_1

This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4105
- Precision: 0.4596
- Recall: 0.2902
- F1: 0.3558
- Accuracy: 0.8605

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 13   | 0.7199          | 0.0       | 0.0    | 0.0    | 0.8273   |
| No log        | 2.0   | 26   | 0.6140          | 0.1663    | 0.0112 | 0.0210 | 0.8316   |
| No log        | 3.0   | 39   | 0.5403          | 0.3494    | 0.1336 | 0.1933 | 0.8462   |
| No log        | 4.0   | 52   | 0.4823          | 0.3732    | 0.1895 | 0.2514 | 0.8501   |
| No log        | 5.0   | 65   | 0.4644          | 0.3951    | 0.2304 | 0.2911 | 0.8534   |
| No log        | 6.0   | 78   | 0.4450          | 0.4086    | 0.2515 | 0.3114 | 0.8553   |
| No log        | 7.0   | 91   | 0.4324          | 0.4293    | 0.2667 | 0.3290 | 0.8570   |
| No log        | 8.0   | 104  | 0.4242          | 0.4413    | 0.2684 | 0.3338 | 0.8583   |
| No log        | 9.0   | 117  | 0.4209          | 0.4452    | 0.2773 | 0.3417 | 0.8587   |
| No log        | 10.0  | 130  | 0.4170          | 0.4499    | 0.2854 | 0.3493 | 0.8593   |
| No log        | 11.0  | 143  | 0.4131          | 0.4568    | 0.2891 | 0.3541 | 0.8600   |
| No log        | 12.0  | 156  | 0.4140          | 0.4478    | 0.2962 | 0.3566 | 0.8588   |
| No log        | 13.0  | 169  | 0.4120          | 0.4660    | 0.2889 | 0.3567 | 0.8608   |
| No log        | 14.0  | 182  | 0.4116          | 0.4560    | 0.2911 | 0.3554 | 0.8600   |
| No log        | 15.0  | 195  | 0.4105          | 0.4596    | 0.2902 | 0.3558 | 0.8605   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.11.0
