---
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: 0.62
    - name: Precision
      type: precision
      value: 0.6273344651952462
    - name: Recall
      type: recall
      value: 0.62
    - name: F1
      type: f1
      value: 0.614448051948052
---

<!-- 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: 0.6923
- Accuracy: 0.62
- Precision: 0.6273
- Recall: 0.62
- F1: 0.6144

## 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 0.99  | 53   | 0.6893          | 0.55     | 0.5565    | 0.55   | 0.5366 |
| 0.7034        | 2.0   | 107  | 0.6771          | 0.595    | 0.5986    | 0.595  | 0.5913 |
| 0.7034        | 2.99  | 160  | 0.6680          | 0.585    | 0.5882    | 0.585  | 0.5812 |
| 0.6769        | 4.0   | 214  | 0.6448          | 0.625    | 0.6271    | 0.625  | 0.6234 |
| 0.6769        | 4.99  | 267  | 0.6465          | 0.625    | 0.6503    | 0.625  | 0.6085 |
| 0.5962        | 6.0   | 321  | 0.6457          | 0.635    | 0.6456    | 0.635  | 0.6282 |
| 0.5962        | 6.99  | 374  | 0.6595          | 0.63     | 0.6366    | 0.63   | 0.6255 |
| 0.4977        | 8.0   | 428  | 0.6763          | 0.62     | 0.6273    | 0.62   | 0.6144 |
| 0.4977        | 8.99  | 481  | 0.6831          | 0.63     | 0.6379    | 0.63   | 0.6246 |
| 0.4268        | 9.91  | 530  | 0.6923          | 0.62     | 0.6273    | 0.62   | 0.6144 |


### Framework versions

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