---
library_name: transformers
base_model: IVN-RIN/bioBIT
tags:
- token-classification
- generated_from_trainer
datasets:
- Rodrigo1771/drugtemist-it-fasttext-9-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: Rodrigo1771/drugtemist-it-fasttext-9-ner
      type: Rodrigo1771/drugtemist-it-fasttext-9-ner
      config: DrugTEMIST Italian NER
      split: validation
      args: DrugTEMIST Italian NER
    metrics:
    - name: Precision
      type: precision
      value: 0.9168241965973535
    - name: Recall
      type: recall
      value: 0.9390125847047435
    - name: F1
      type: f1
      value: 0.9277857484457198
    - name: Accuracy
      type: accuracy
      value: 0.9986691768371851
---

<!-- 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-9-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0051
- Precision: 0.9168
- Recall: 0.9390
- F1: 0.9278
- 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        | 0.9988 | 434  | 0.0046          | 0.8889    | 0.8829 | 0.8859 | 0.9982   |
| 0.011         | 2.0    | 869  | 0.0039          | 0.9147    | 0.9138 | 0.9143 | 0.9985   |
| 0.0034        | 2.9988 | 1303 | 0.0045          | 0.9317    | 0.8848 | 0.9076 | 0.9985   |
| 0.0019        | 4.0    | 1738 | 0.0056          | 0.9309    | 0.9129 | 0.9218 | 0.9986   |
| 0.0013        | 4.9988 | 2172 | 0.0051          | 0.9168    | 0.9390 | 0.9278 | 0.9987   |
| 0.0008        | 6.0    | 2607 | 0.0071          | 0.9325    | 0.9100 | 0.9211 | 0.9986   |
| 0.0005        | 6.9988 | 3041 | 0.0068          | 0.9291    | 0.9264 | 0.9278 | 0.9986   |
| 0.0005        | 8.0    | 3476 | 0.0075          | 0.9226    | 0.9226 | 0.9226 | 0.9986   |
| 0.0003        | 8.9988 | 3910 | 0.0080          | 0.9187    | 0.9293 | 0.9240 | 0.9986   |
| 0.0002        | 9.9885 | 4340 | 0.0083          | 0.9282    | 0.9264 | 0.9273 | 0.9986   |


### Framework versions

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