transformers tensorboard safetensors bert token-classification generated_from_trainer base_model:monologg/biobert_v1.1_pubmed base_model:finetune:monologg/biobert_v1.1_pubmed autotrain_compatible endpoints_compatible region:us
---
base_model: monologg/biobert_v1.1_pubmed
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: NLP-HIBA_DisTEMIST_fine_tuned_biobert
  results: []
---

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# NLP-HIBA_DisTEMIST_fine_tuned_biobert

This model is a fine-tuned version of [monologg/biobert_v1.1_pubmed](https://huggingface.co/monologg/biobert_v1.1_pubmed) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2325
- Precision: 0.5394
- Recall: 0.5010
- F1: 0.5195
- Accuracy: 0.9434

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 71   | 0.2200          | 0.2689    | 0.2813 | 0.2750 | 0.9147   |
| No log        | 2.0   | 142  | 0.1895          | 0.4126    | 0.3834 | 0.3975 | 0.9308   |
| No log        | 3.0   | 213  | 0.1813          | 0.5319    | 0.3667 | 0.4341 | 0.9389   |
| No log        | 4.0   | 284  | 0.1746          | 0.5085    | 0.4395 | 0.4715 | 0.9410   |
| No log        | 5.0   | 355  | 0.1942          | 0.4868    | 0.4697 | 0.4781 | 0.9382   |
| No log        | 6.0   | 426  | 0.1975          | 0.5246    | 0.4860 | 0.5046 | 0.9413   |
| No log        | 7.0   | 497  | 0.2066          | 0.5207    | 0.4897 | 0.5047 | 0.9415   |
| 0.122         | 8.0   | 568  | 0.2201          | 0.5232    | 0.4956 | 0.5090 | 0.9420   |
| 0.122         | 9.0   | 639  | 0.2358          | 0.5178    | 0.5048 | 0.5112 | 0.9418   |
| 0.122         | 10.0  | 710  | 0.2325          | 0.5394    | 0.5010 | 0.5195 | 0.9434   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
