---
library_name: transformers
base_model: IVN-RIN/bioBIT
tags:
- token-classification
- generated_from_trainer
datasets:
- Rodrigo1771/drugtemist-it-fasttext-8-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: Rodrigo1771/drugtemist-it-fasttext-8-ner
      type: Rodrigo1771/drugtemist-it-fasttext-8-ner
      config: DrugTEMIST Italian NER
      split: validation
      args: DrugTEMIST Italian NER
    metrics:
    - name: Precision
      type: precision
      value: 0.9162702188392008
    - name: Recall
      type: recall
      value: 0.9322362052274927
    - name: F1
      type: f1
      value: 0.9241842610364683
    - name: Accuracy
      type: accuracy
      value: 0.9987276032199429
---

<!-- 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 [IVN-RIN/bioBIT](https://huggingface.co/IVN-RIN/bioBIT) on the Rodrigo1771/drugtemist-it-fasttext-8-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0064
- Precision: 0.9163
- Recall: 0.9322
- F1: 0.9242
- 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        | 1.0   | 470  | 0.0044          | 0.9108    | 0.8993 | 0.9050 | 0.9985   |
| 0.0122        | 2.0   | 940  | 0.0051          | 0.9050    | 0.8848 | 0.8948 | 0.9984   |
| 0.0032        | 3.0   | 1410 | 0.0049          | 0.9144    | 0.8993 | 0.9068 | 0.9985   |
| 0.0017        | 4.0   | 1880 | 0.0060          | 0.9213    | 0.9177 | 0.9195 | 0.9986   |
| 0.0011        | 5.0   | 2350 | 0.0071          | 0.9280    | 0.8858 | 0.9064 | 0.9985   |
| 0.0007        | 6.0   | 2820 | 0.0060          | 0.9078    | 0.9245 | 0.9161 | 0.9986   |
| 0.0005        | 7.0   | 3290 | 0.0059          | 0.9260    | 0.9206 | 0.9233 | 0.9988   |
| 0.0004        | 8.0   | 3760 | 0.0064          | 0.9163    | 0.9322 | 0.9242 | 0.9987   |
| 0.0002        | 9.0   | 4230 | 0.0067          | 0.9177    | 0.9284 | 0.9230 | 0.9986   |
| 0.0001        | 10.0  | 4700 | 0.0069          | 0.9152    | 0.9303 | 0.9227 | 0.9987   |


### Framework versions

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