---
library_name: transformers
base_model: IVN-RIN/bioBIT
tags:
- token-classification
- generated_from_trainer
datasets:
- Rodrigo1771/drugtemist-it-75-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: Rodrigo1771/drugtemist-it-75-ner
      type: Rodrigo1771/drugtemist-it-75-ner
      config: DrugTEMIST Italian NER
      split: validation
      args: DrugTEMIST Italian NER
    metrics:
    - name: Precision
      type: precision
      value: 0.914505283381364
    - name: Recall
      type: recall
      value: 0.9215876089060987
    - name: F1
      type: f1
      value: 0.9180327868852458
    - name: Accuracy
      type: accuracy
      value: 0.9985847831732018
---

<!-- 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-75-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0080
- Precision: 0.9145
- Recall: 0.9216
- F1: 0.9180
- 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        | 0.9990 | 498  | 0.0059          | 0.8588    | 0.9129 | 0.8850 | 0.9981   |
| 0.0135        | 2.0    | 997  | 0.0052          | 0.8778    | 0.9245 | 0.9005 | 0.9985   |
| 0.0036        | 2.9990 | 1495 | 0.0061          | 0.8868    | 0.9177 | 0.9020 | 0.9984   |
| 0.0022        | 4.0    | 1994 | 0.0059          | 0.8842    | 0.9313 | 0.9071 | 0.9985   |
| 0.0012        | 4.9990 | 2492 | 0.0077          | 0.8930    | 0.9206 | 0.9066 | 0.9985   |
| 0.0006        | 6.0    | 2991 | 0.0074          | 0.8813    | 0.9274 | 0.9038 | 0.9984   |
| 0.0005        | 6.9990 | 3489 | 0.0080          | 0.8949    | 0.9235 | 0.9090 | 0.9985   |
| 0.0002        | 8.0    | 3988 | 0.0080          | 0.9145    | 0.9216 | 0.9180 | 0.9986   |
| 0.0002        | 8.9990 | 4486 | 0.0087          | 0.9002    | 0.9255 | 0.9126 | 0.9986   |
| 0.0001        | 9.9900 | 4980 | 0.0089          | 0.9065    | 0.9197 | 0.9130 | 0.9985   |


### Framework versions

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