---
library_name: transformers
base_model: IVN-RIN/bioBIT
tags:
- token-classification
- generated_from_trainer
datasets:
- Rodrigo1771/drugtemist-it-fasttext-85-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: Rodrigo1771/drugtemist-it-fasttext-85-ner
      type: Rodrigo1771/drugtemist-it-fasttext-85-ner
      config: DrugTEMIST Italian NER
      split: validation
      args: DrugTEMIST Italian NER
    metrics:
    - name: Precision
      type: precision
      value: 0.9211538461538461
    - name: Recall
      type: recall
      value: 0.9273959341723137
    - name: F1
      type: f1
      value: 0.9242643511818619
    - name: Accuracy
      type: accuracy
      value: 0.9986302259153467
---

<!-- 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-85-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0080
- Precision: 0.9212
- Recall: 0.9274
- F1: 0.9243
- 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        | 0.9989 | 451  | 0.0051          | 0.9326    | 0.8703 | 0.9004 | 0.9984   |
| 0.0116        | 2.0    | 903  | 0.0049          | 0.9066    | 0.9206 | 0.9135 | 0.9985   |
| 0.0034        | 2.9989 | 1354 | 0.0056          | 0.8990    | 0.9216 | 0.9101 | 0.9984   |
| 0.0018        | 4.0    | 1806 | 0.0066          | 0.9094    | 0.9235 | 0.9164 | 0.9985   |
| 0.0011        | 4.9989 | 2257 | 0.0056          | 0.9082    | 0.9293 | 0.9187 | 0.9986   |
| 0.0007        | 6.0    | 2709 | 0.0068          | 0.9145    | 0.9109 | 0.9127 | 0.9985   |
| 0.0005        | 6.9989 | 3160 | 0.0076          | 0.8880    | 0.9284 | 0.9077 | 0.9984   |
| 0.0003        | 8.0    | 3612 | 0.0080          | 0.9094    | 0.9235 | 0.9164 | 0.9986   |
| 0.0002        | 8.9989 | 4063 | 0.0078          | 0.9162    | 0.9206 | 0.9184 | 0.9986   |
| 0.0001        | 9.9889 | 4510 | 0.0080          | 0.9212    | 0.9274 | 0.9243 | 0.9986   |


### Framework versions

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