---
base_model: dmis-lab/biobert-v1.1
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
model-index:
- name: sentence-classifiert
  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. -->

# sentence-classifiert

This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3410
- Precision: 0.9085
- Recall: 0.9068
- Accuracy: 0.9072
- F1: 0.9072

## 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: 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:------:|
| No log        | 1.0   | 154  | 0.4158          | 0.8549    | 0.8445 | 0.8438   | 0.8443 |
| No log        | 2.0   | 308  | 0.3426          | 0.8875    | 0.8804 | 0.8796   | 0.8787 |
| No log        | 3.0   | 462  | 0.3594          | 0.8945    | 0.8856 | 0.8869   | 0.8868 |
| 0.3638        | 4.0   | 616  | 0.3302          | 0.9034    | 0.9008 | 0.9015   | 0.9014 |
| 0.3638        | 5.0   | 770  | 0.3410          | 0.9085    | 0.9068 | 0.9072   | 0.9072 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2