---
library_name: transformers
license: apache-2.0
base_model: michiyasunaga/BioLinkBERT-base
tags:
- token-classification
- generated_from_trainer
datasets:
- Rodrigo1771/drugtemist-en-9-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: Rodrigo1771/drugtemist-en-9-ner
      type: Rodrigo1771/drugtemist-en-9-ner
      config: DrugTEMIST English NER
      split: validation
      args: DrugTEMIST English NER
    metrics:
    - name: Precision
      type: precision
      value: 0.9297597042513863
    - name: Recall
      type: recall
      value: 0.9375582479030755
    - name: F1
      type: f1
      value: 0.9336426914153132
    - name: Accuracy
      type: accuracy
      value: 0.9987999888371054
---

<!-- 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. -->

# output

This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on the Rodrigo1771/drugtemist-en-9-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0046
- Precision: 0.9298
- Recall: 0.9376
- F1: 0.9336
- Accuracy: 0.9988

## 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: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 437  | 0.0047          | 0.8995    | 0.9254 | 0.9123 | 0.9985   |
| 0.0144        | 2.0   | 874  | 0.0053          | 0.8960    | 0.9310 | 0.9132 | 0.9985   |
| 0.0038        | 3.0   | 1311 | 0.0046          | 0.9298    | 0.9376 | 0.9336 | 0.9988   |
| 0.0022        | 4.0   | 1748 | 0.0055          | 0.9202    | 0.9245 | 0.9224 | 0.9986   |
| 0.0019        | 5.0   | 2185 | 0.0053          | 0.9118    | 0.9348 | 0.9231 | 0.9986   |
| 0.0014        | 6.0   | 2622 | 0.0054          | 0.9194    | 0.9254 | 0.9224 | 0.9986   |
| 0.0009        | 7.0   | 3059 | 0.0073          | 0.9324    | 0.9254 | 0.9289 | 0.9986   |
| 0.0009        | 8.0   | 3496 | 0.0065          | 0.9341    | 0.9254 | 0.9298 | 0.9987   |
| 0.0005        | 9.0   | 3933 | 0.0069          | 0.9326    | 0.9292 | 0.9309 | 0.9987   |
| 0.0004        | 10.0  | 4370 | 0.0071          | 0.9249    | 0.9292 | 0.9270 | 0.9987   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1