---
library_name: transformers
license: apache-2.0
base_model: michiyasunaga/BioLinkBERT-base
tags:
- token-classification
- generated_from_trainer
datasets:
- Rodrigo1771/drugtemist-en-fasttext-9-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: Rodrigo1771/drugtemist-en-fasttext-9-ner
      type: Rodrigo1771/drugtemist-en-fasttext-9-ner
      config: DrugTEMIST English NER
      split: validation
      args: DrugTEMIST English NER
    metrics:
    - name: Precision
      type: precision
      value: 0.9311627906976744
    - name: Recall
      type: recall
      value: 0.9328984156570364
    - name: F1
      type: f1
      value: 0.9320297951582869
    - name: Accuracy
      type: accuracy
      value: 0.998772081600759
---

<!-- 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-fasttext-9-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0071
- Precision: 0.9312
- Recall: 0.9329
- F1: 0.9320
- 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        | 0.9989 | 435  | 0.0060          | 0.8714    | 0.9217 | 0.8958 | 0.9981   |
| 0.0156        | 2.0    | 871  | 0.0044          | 0.9183    | 0.9217 | 0.92   | 0.9987   |
| 0.0038        | 2.9989 | 1306 | 0.0040          | 0.8969    | 0.9404 | 0.9181 | 0.9987   |
| 0.0025        | 4.0    | 1742 | 0.0045          | 0.9078    | 0.9357 | 0.9215 | 0.9986   |
| 0.0016        | 4.9989 | 2177 | 0.0054          | 0.9182    | 0.9096 | 0.9139 | 0.9986   |
| 0.0011        | 6.0    | 2613 | 0.0053          | 0.9152    | 0.9254 | 0.9203 | 0.9986   |
| 0.0009        | 6.9989 | 3048 | 0.0060          | 0.9263    | 0.9366 | 0.9314 | 0.9987   |
| 0.0009        | 8.0    | 3484 | 0.0059          | 0.9181    | 0.9404 | 0.9291 | 0.9988   |
| 0.0005        | 8.9989 | 3919 | 0.0067          | 0.9258    | 0.9301 | 0.9279 | 0.9988   |
| 0.0003        | 9.9885 | 4350 | 0.0071          | 0.9312    | 0.9329 | 0.9320 | 0.9988   |


### Framework versions

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