---
library_name: transformers
license: apache-2.0
base_model: michiyasunaga/BioLinkBERT-base
tags:
- generated_from_trainer
datasets:
- drugtemist-en-75-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: drugtemist-en-75-ner
      type: drugtemist-en-75-ner
      config: DrugTEMIST English NER
      split: validation
      args: DrugTEMIST English NER
    metrics:
    - name: Precision
      type: precision
      value: 0.921028466483012
    - name: Recall
      type: recall
      value: 0.934762348555452
    - name: F1
      type: f1
      value: 0.9278445883441258
    - name: Accuracy
      type: accuracy
      value: 0.9986883598917199
---

<!-- 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 drugtemist-en-75-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0083
- Precision: 0.9210
- Recall: 0.9348
- F1: 0.9278
- Accuracy: 0.9987

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0189        | 1.0   | 504  | 0.0052          | 0.8712    | 0.9394 | 0.9040 | 0.9984   |
| 0.0047        | 2.0   | 1008 | 0.0048          | 0.9253    | 0.9236 | 0.9244 | 0.9987   |
| 0.0027        | 3.0   | 1512 | 0.0059          | 0.9252    | 0.9226 | 0.9239 | 0.9986   |
| 0.0015        | 4.0   | 2016 | 0.0065          | 0.9342    | 0.9264 | 0.9303 | 0.9987   |
| 0.0011        | 5.0   | 2520 | 0.0073          | 0.9073    | 0.9394 | 0.9231 | 0.9986   |
| 0.0005        | 6.0   | 3024 | 0.0090          | 0.9191    | 0.9217 | 0.9204 | 0.9984   |
| 0.0007        | 7.0   | 3528 | 0.0084          | 0.9074    | 0.9310 | 0.9190 | 0.9986   |
| 0.0004        | 8.0   | 4032 | 0.0085          | 0.9093    | 0.9338 | 0.9214 | 0.9986   |
| 0.0003        | 9.0   | 4536 | 0.0080          | 0.9186    | 0.9357 | 0.9271 | 0.9987   |
| 0.0002        | 10.0  | 5040 | 0.0083          | 0.9210    | 0.9348 | 0.9278 | 0.9987   |


### Framework versions

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