---
license: apache-2.0
base_model: michiyasunaga/BioLinkBERT-base
tags:
- token-classification
- generated_from_trainer
datasets:
- Rodrigo1771/drugtemist-en-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: Rodrigo1771/drugtemist-en-ner
      type: Rodrigo1771/drugtemist-en-ner
      config: DrugTEMIST English NER
      split: validation
      args: DrugTEMIST English NER
    metrics:
    - name: Precision
      type: precision
      value: 0.9327102803738317
    - name: Recall
      type: recall
      value: 0.9301025163094129
    - name: F1
      type: f1
      value: 0.9314045730284647
    - name: Accuracy
      type: accuracy
      value: 0.9986953367008066
---

<!-- 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-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0056
- Precision: 0.9327
- Recall: 0.9301
- F1: 0.9314
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 434  | 0.0057          | 0.8938    | 0.8938 | 0.8938 | 0.9981   |
| 0.0182        | 2.0   | 868  | 0.0044          | 0.9024    | 0.9301 | 0.9160 | 0.9985   |
| 0.0039        | 3.0   | 1302 | 0.0045          | 0.9129    | 0.9282 | 0.9205 | 0.9987   |
| 0.0024        | 4.0   | 1736 | 0.0051          | 0.8821    | 0.9348 | 0.9077 | 0.9983   |
| 0.0017        | 5.0   | 2170 | 0.0057          | 0.9251    | 0.9320 | 0.9285 | 0.9986   |
| 0.0012        | 6.0   | 2604 | 0.0061          | 0.9001    | 0.9236 | 0.9117 | 0.9984   |
| 0.0009        | 7.0   | 3038 | 0.0056          | 0.9327    | 0.9301 | 0.9314 | 0.9987   |
| 0.0009        | 8.0   | 3472 | 0.0068          | 0.9118    | 0.9348 | 0.9231 | 0.9986   |
| 0.0006        | 9.0   | 3906 | 0.0072          | 0.9267    | 0.9310 | 0.9289 | 0.9987   |
| 0.0004        | 10.0  | 4340 | 0.0073          | 0.9192    | 0.9329 | 0.9260 | 0.9986   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1