---
library_name: transformers
license: apache-2.0
base_model: michiyasunaga/BioLinkBERT-base
tags:
- generated_from_trainer
datasets:
- drugtemist-en-8-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: drugtemist-en-8-ner
      type: drugtemist-en-8-ner
      config: DrugTEMIST English NER
      split: validation
      args: DrugTEMIST English NER
    metrics:
    - name: Precision
      type: precision
      value: 0.9172033118675254
    - name: Recall
      type: recall
      value: 0.9291705498602051
    - name: F1
      type: f1
      value: 0.9231481481481483
    - name: Accuracy
      type: accuracy
      value: 0.9985418469009014
---

<!-- 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-8-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0089
- Precision: 0.9172
- Recall: 0.9292
- F1: 0.9231
- Accuracy: 0.9985

## 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   | 493  | 0.0050          | 0.9288    | 0.9245 | 0.9267 | 0.9987   |
| 0.018         | 2.0   | 986  | 0.0057          | 0.9104    | 0.9189 | 0.9147 | 0.9984   |
| 0.0044        | 3.0   | 1479 | 0.0079          | 0.9362    | 0.9161 | 0.9260 | 0.9985   |
| 0.0023        | 4.0   | 1972 | 0.0057          | 0.9318    | 0.9301 | 0.9310 | 0.9987   |
| 0.0014        | 5.0   | 2465 | 0.0070          | 0.9201    | 0.9226 | 0.9214 | 0.9986   |
| 0.0008        | 6.0   | 2958 | 0.0082          | 0.9118    | 0.9254 | 0.9186 | 0.9985   |
| 0.0006        | 7.0   | 3451 | 0.0074          | 0.9172    | 0.9394 | 0.9282 | 0.9986   |
| 0.0003        | 8.0   | 3944 | 0.0085          | 0.9219    | 0.9245 | 0.9232 | 0.9985   |
| 0.0003        | 9.0   | 4437 | 0.0086          | 0.9149    | 0.9320 | 0.9234 | 0.9985   |
| 0.0002        | 10.0  | 4930 | 0.0089          | 0.9172    | 0.9292 | 0.9231 | 0.9985   |


### Framework versions

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