---
base_model: IVN-RIN/bioBIT
tags:
- token-classification
- generated_from_trainer
datasets:
- Rodrigo1771/drugtemist-it-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: Rodrigo1771/drugtemist-it-ner
      type: Rodrigo1771/drugtemist-it-ner
      config: DrugTEMIST Italian NER
      split: validation
      args: DrugTEMIST Italian NER
    metrics:
    - name: Precision
      type: precision
      value: 0.9328214971209213
    - name: Recall
      type: recall
      value: 0.9409486931268151
    - name: F1
      type: f1
      value: 0.936867469879518
    - name: Accuracy
      type: accuracy
      value: 0.9988184887042326
---

<!-- 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-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0067
- Precision: 0.9328
- Recall: 0.9409
- F1: 0.9369
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 425  | 0.0056          | 0.8672    | 0.9226 | 0.8940 | 0.9981   |
| 0.0104        | 2.0   | 850  | 0.0042          | 0.9151    | 0.9284 | 0.9217 | 0.9986   |
| 0.0034        | 3.0   | 1275 | 0.0043          | 0.9182    | 0.9129 | 0.9155 | 0.9985   |
| 0.0022        | 4.0   | 1700 | 0.0044          | 0.9365    | 0.9138 | 0.9250 | 0.9986   |
| 0.0012        | 5.0   | 2125 | 0.0061          | 0.9107    | 0.9284 | 0.9195 | 0.9985   |
| 0.0009        | 6.0   | 2550 | 0.0060          | 0.9104    | 0.9342 | 0.9221 | 0.9987   |
| 0.0009        | 7.0   | 2975 | 0.0065          | 0.9230    | 0.9400 | 0.9314 | 0.9987   |
| 0.0005        | 8.0   | 3400 | 0.0059          | 0.9258    | 0.9303 | 0.9281 | 0.9987   |
| 0.0004        | 9.0   | 3825 | 0.0066          | 0.9255    | 0.9380 | 0.9317 | 0.9987   |
| 0.0001        | 10.0  | 4250 | 0.0067          | 0.9328    | 0.9409 | 0.9369 | 0.9988   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1