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Rodrigo1771
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BioLinkBERT-base-drugtemist-en-fasttext-8-ner

Token Classification
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BioLinkBERT-base-drugtemist-en-fasttext-8-ner
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---
library_name: transformers
license: apache-2.0
base_model: michiyasunaga/BioLinkBERT-base
tags:
- token-classification
- generated_from_trainer
datasets:
- Rodrigo1771/drugtemist-en-fasttext-75-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: Rodrigo1771/drugtemist-en-fasttext-75-ner
      type: Rodrigo1771/drugtemist-en-fasttext-75-ner
      config: DrugTEMIST English NER
      split: validation
      args: DrugTEMIST English NER
    metrics:
    - name: Precision
      type: precision
      value: 0.9249771271729186
    - name: Recall
      type: recall
      value: 0.9422180801491147
    - name: F1
      type: f1
      value: 0.9335180055401663
    - 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-75-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0076
- Precision: 0.9250
- Recall: 0.9422
- F1: 0.9335
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0183        | 1.0   | 507  | 0.0055          | 0.8974    | 0.9376 | 0.9170 | 0.9985   |
| 0.0043        | 2.0   | 1014 | 0.0059          | 0.9099    | 0.9320 | 0.9208 | 0.9986   |
| 0.0022        | 3.0   | 1521 | 0.0057          | 0.9015    | 0.9301 | 0.9156 | 0.9985   |
| 0.0018        | 4.0   | 2028 | 0.0072          | 0.9275    | 0.9180 | 0.9227 | 0.9986   |
| 0.0009        | 5.0   | 2535 | 0.0064          | 0.9078    | 0.9357 | 0.9215 | 0.9987   |
| 0.0007        | 6.0   | 3042 | 0.0064          | 0.9194    | 0.9357 | 0.9275 | 0.9987   |
| 0.0004        | 7.0   | 3549 | 0.0072          | 0.9289    | 0.9376 | 0.9332 | 0.9988   |
| 0.0004        | 8.0   | 4056 | 0.0076          | 0.9250    | 0.9422 | 0.9335 | 0.9988   |
| 0.0003        | 9.0   | 4563 | 0.0077          | 0.9161    | 0.9366 | 0.9263 | 0.9987   |
| 0.0002        | 10.0  | 5070 | 0.0077          | 0.9195    | 0.9366 | 0.9280 | 0.9988   |


### Framework versions

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