---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- source_data
metrics:
- precision
- recall
- f1
model-index:
- name: SourceData_GP-CHEM-ROLES_v_2-0-3_BioLinkBERT_base
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: source_data
      type: source_data
      args: ROLES_MULTI
    metrics:
    - name: Precision
      type: precision
      value: 0.9701754385964912
    - name: Recall
      type: recall
      value: 0.9765142150803461
    - name: F1
      type: f1
      value: 0.973334506732377
---

<!-- 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. -->

# SourceData_GP-CHEM-ROLES_v_2-0-3_BioLinkBERT_base

This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co./michiyasunaga/BioLinkBERT-base) on the source_data dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0048
- Accuracy Score: 0.9983
- Precision: 0.9702
- Recall: 0.9765
- F1: 0.9733

## 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: 0.0001
- train_batch_size: 128
- eval_batch_size: 256
- seed: 42
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 1.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy Score | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:|
| 0.0063        | 1.0   | 471  | 0.0048          | 0.9983         | 0.9702    | 0.9765 | 0.9733 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0a0+bfe5ad2
- Datasets 2.10.1
- Tokenizers 0.12.1