---
license: apache-2.0
base_model: michiyasunaga/BioLinkBERT-base
tags:
- generated_from_trainer
datasets:
- sem_eval_2024_task_2
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: run1
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: sem_eval_2024_task_2
      type: sem_eval_2024_task_2
      config: sem_eval_2024_task_2_source
      split: validation
      args: sem_eval_2024_task_2_source
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.64
    - name: Precision
      type: precision
      value: 0.6582994120307553
    - name: Recall
      type: recall
      value: 0.64
    - name: F1
      type: f1
      value: 0.6292863762743282
---

<!-- 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. -->

# run1

This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on the sem_eval_2024_task_2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2153
- Accuracy: 0.64
- Precision: 0.6583
- Recall: 0.64
- F1: 0.6293

## 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: 64
- 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: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 0.99  | 53   | 0.6971          | 0.515    | 0.5272    | 0.515  | 0.4537 |
| 0.7029        | 2.0   | 107  | 0.6899          | 0.535    | 0.5413    | 0.535  | 0.5166 |
| 0.7029        | 2.99  | 160  | 0.6855          | 0.535    | 0.5399    | 0.5350 | 0.5203 |
| 0.6955        | 4.0   | 214  | 0.6698          | 0.565    | 0.5686    | 0.5650 | 0.5592 |
| 0.6955        | 4.99  | 267  | 0.6722          | 0.57     | 0.5703    | 0.5700 | 0.5696 |
| 0.6581        | 6.0   | 321  | 0.6367          | 0.61     | 0.6104    | 0.61   | 0.6096 |
| 0.6581        | 6.99  | 374  | 0.6973          | 0.58     | 0.5905    | 0.58   | 0.5675 |
| 0.5796        | 8.0   | 428  | 0.6925          | 0.625    | 0.6348    | 0.625  | 0.6180 |
| 0.5796        | 8.99  | 481  | 0.7539          | 0.61     | 0.6364    | 0.61   | 0.5902 |
| 0.4636        | 10.0  | 535  | 0.9313          | 0.575    | 0.6043    | 0.575  | 0.5429 |
| 0.4636        | 10.99 | 588  | 0.9028          | 0.615    | 0.6227    | 0.615  | 0.6089 |
| 0.3577        | 12.0  | 642  | 0.8694          | 0.615    | 0.6227    | 0.615  | 0.6089 |
| 0.3577        | 12.99 | 695  | 0.9201          | 0.635    | 0.6494    | 0.635  | 0.6260 |
| 0.3041        | 14.0  | 749  | 0.9186          | 0.645    | 0.6583    | 0.645  | 0.6374 |
| 0.3041        | 14.99 | 802  | 1.1683          | 0.63     | 0.6578    | 0.63   | 0.6129 |
| 0.2344        | 16.0  | 856  | 1.1405          | 0.625    | 0.6383    | 0.625  | 0.6158 |
| 0.2344        | 16.99 | 909  | 1.2451          | 0.625    | 0.6474    | 0.625  | 0.6102 |
| 0.208         | 18.0  | 963  | 1.1640          | 0.65     | 0.6671    | 0.65   | 0.6408 |
| 0.208         | 18.99 | 1016 | 1.2081          | 0.64     | 0.6583    | 0.64   | 0.6293 |
| 0.1757        | 19.81 | 1060 | 1.2153          | 0.64     | 0.6583    | 0.64   | 0.6293 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0