---
library_name: transformers
base_model: IVN-RIN/bioBIT
tags:
- token-classification
- generated_from_trainer
datasets:
- Rodrigo1771/drugtemist-it-85-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: Rodrigo1771/drugtemist-it-85-ner
      type: Rodrigo1771/drugtemist-it-85-ner
      config: DrugTEMIST Italian NER
      split: validation
      args: DrugTEMIST Italian NER
    metrics:
    - name: Precision
      type: precision
      value: 0.9193083573487032
    - name: Recall
      type: recall
      value: 0.9264278799612778
    - name: F1
      type: f1
      value: 0.922854387656702
    - name: Accuracy
      type: accuracy
      value: 0.9985847831732018
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# output

This model is a fine-tuned version of [IVN-RIN/bioBIT](https://huggingface.co/IVN-RIN/bioBIT) on the Rodrigo1771/drugtemist-it-85-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0061
- Precision: 0.9193
- Recall: 0.9264
- F1: 0.9229
- Accuracy: 0.9986

## 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   | 479  | 0.0052          | 0.8498    | 0.9313 | 0.8887 | 0.9983   |
| 0.0127        | 2.0   | 958  | 0.0056          | 0.9063    | 0.9080 | 0.9072 | 0.9984   |
| 0.0035        | 3.0   | 1437 | 0.0047          | 0.9211    | 0.9158 | 0.9184 | 0.9985   |
| 0.002         | 4.0   | 1916 | 0.0065          | 0.9028    | 0.9080 | 0.9054 | 0.9984   |
| 0.0014        | 5.0   | 2395 | 0.0061          | 0.9193    | 0.9264 | 0.9229 | 0.9986   |
| 0.0007        | 6.0   | 2874 | 0.0069          | 0.9246    | 0.8906 | 0.9073 | 0.9984   |
| 0.0004        | 7.0   | 3353 | 0.0071          | 0.8990    | 0.9216 | 0.9101 | 0.9985   |
| 0.0003        | 8.0   | 3832 | 0.0076          | 0.9135    | 0.9303 | 0.9218 | 0.9986   |
| 0.0001        | 9.0   | 4311 | 0.0080          | 0.9130    | 0.9245 | 0.9187 | 0.9986   |
| 0.0001        | 10.0  | 4790 | 0.0080          | 0.9107    | 0.9284 | 0.9195 | 0.9986   |


### Framework versions

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