---
library_name: transformers
license: apache-2.0
base_model: michiyasunaga/BioLinkBERT-base
tags:
- token-classification
- generated_from_trainer
datasets:
- Rodrigo1771/drugtemist-en-85-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: Rodrigo1771/drugtemist-en-85-ner
      type: Rodrigo1771/drugtemist-en-85-ner
      config: DrugTEMIST English NER
      split: validation
      args: DrugTEMIST English NER
    metrics:
    - name: Precision
      type: precision
      value: 0.9302325581395349
    - name: Recall
      type: recall
      value: 0.9319664492078286
    - name: F1
      type: f1
      value: 0.931098696461825
    - name: Accuracy
      type: accuracy
      value: 0.9986534758462869
---

<!-- 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-85-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0083
- Precision: 0.9302
- Recall: 0.9320
- F1: 0.9311
- 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        | 0.9989 | 457  | 0.0056          | 0.8730    | 0.9292 | 0.9002 | 0.9983   |
| 0.0158        | 2.0    | 915  | 0.0066          | 0.8625    | 0.9357 | 0.8976 | 0.9981   |
| 0.0037        | 2.9989 | 1372 | 0.0056          | 0.9247    | 0.9161 | 0.9204 | 0.9986   |
| 0.0025        | 4.0    | 1830 | 0.0064          | 0.9234    | 0.9096 | 0.9164 | 0.9985   |
| 0.0015        | 4.9989 | 2287 | 0.0061          | 0.9193    | 0.9236 | 0.9214 | 0.9985   |
| 0.0008        | 6.0    | 2745 | 0.0074          | 0.9282    | 0.9273 | 0.9277 | 0.9986   |
| 0.0006        | 6.9989 | 3202 | 0.0077          | 0.9305    | 0.9226 | 0.9265 | 0.9986   |
| 0.0003        | 8.0    | 3660 | 0.0082          | 0.9282    | 0.9282 | 0.9282 | 0.9986   |
| 0.0004        | 8.9989 | 4117 | 0.0083          | 0.9290    | 0.9264 | 0.9277 | 0.9986   |
| 0.0002        | 9.9891 | 4570 | 0.0083          | 0.9302    | 0.9320 | 0.9311 | 0.9987   |


### Framework versions

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