---
library_name: transformers
license: apache-2.0
base_model: michiyasunaga/BioLinkBERT-base
tags:
- token-classification
- generated_from_trainer
datasets:
- Rodrigo1771/drugtemist-en-fasttext-8-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: Rodrigo1771/drugtemist-en-fasttext-8-ner
      type: Rodrigo1771/drugtemist-en-fasttext-8-ner
      config: DrugTEMIST English NER
      split: validation
      args: DrugTEMIST English NER
    metrics:
    - name: Precision
      type: precision
      value: 0.9271889400921659
    - name: Recall
      type: recall
      value: 0.9375582479030755
    - name: F1
      type: f1
      value: 0.9323447636700648
    - name: Accuracy
      type: accuracy
      value: 0.9987162671280663
---

<!-- 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-8-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0080
- Precision: 0.9272
- Recall: 0.9376
- F1: 0.9323
- 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        | 0.9990 | 481  | 0.0042          | 0.9173    | 0.9413 | 0.9292 | 0.9987   |
| 0.0156        | 2.0    | 963  | 0.0049          | 0.9134    | 0.9245 | 0.9189 | 0.9986   |
| 0.0039        | 2.9990 | 1444 | 0.0053          | 0.8914    | 0.9487 | 0.9192 | 0.9986   |
| 0.0024        | 4.0    | 1926 | 0.0061          | 0.8820    | 0.9543 | 0.9167 | 0.9985   |
| 0.0017        | 4.9990 | 2407 | 0.0074          | 0.9199    | 0.9310 | 0.9254 | 0.9986   |
| 0.0011        | 6.0    | 2889 | 0.0079          | 0.9170    | 0.9366 | 0.9267 | 0.9986   |
| 0.0007        | 6.9990 | 3370 | 0.0067          | 0.9092    | 0.9422 | 0.9254 | 0.9987   |
| 0.0005        | 8.0    | 3852 | 0.0073          | 0.9249    | 0.9301 | 0.9275 | 0.9987   |
| 0.0004        | 8.9990 | 4333 | 0.0080          | 0.9272    | 0.9376 | 0.9323 | 0.9987   |
| 0.0002        | 9.9896 | 4810 | 0.0079          | 0.9247    | 0.9385 | 0.9315 | 0.9987   |


### Framework versions

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