---
library_name: transformers
base_model: IVN-RIN/bioBIT
tags:
- token-classification
- generated_from_trainer
datasets:
- Rodrigo1771/drugtemist-it-fasttext-75-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: Rodrigo1771/drugtemist-it-fasttext-75-ner
      type: Rodrigo1771/drugtemist-it-fasttext-75-ner
      config: DrugTEMIST Italian NER
      split: validation
      args: DrugTEMIST Italian NER
    metrics:
    - name: Precision
      type: precision
      value: 0.9169054441260746
    - name: Recall
      type: recall
      value: 0.9293320425943853
    - name: F1
      type: f1
      value: 0.9230769230769231
    - 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-75-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0094
- Precision: 0.9169
- Recall: 0.9293
- F1: 0.9231
- 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 | 485  | 0.0043          | 0.9146    | 0.9119 | 0.9132 | 0.9984   |
| 0.0127        | 2.0    | 971  | 0.0054          | 0.9162    | 0.9100 | 0.9131 | 0.9985   |
| 0.0033        | 2.9990 | 1456 | 0.0059          | 0.9210    | 0.9138 | 0.9174 | 0.9986   |
| 0.0016        | 4.0    | 1942 | 0.0062          | 0.9019    | 0.9255 | 0.9135 | 0.9986   |
| 0.001         | 4.9990 | 2427 | 0.0064          | 0.9181    | 0.9226 | 0.9203 | 0.9986   |
| 0.0006        | 6.0    | 2913 | 0.0081          | 0.9202    | 0.9158 | 0.9180 | 0.9986   |
| 0.0004        | 6.9990 | 3398 | 0.0085          | 0.9181    | 0.9226 | 0.9203 | 0.9986   |
| 0.0003        | 8.0    | 3884 | 0.0087          | 0.9215    | 0.9206 | 0.9211 | 0.9986   |
| 0.0002        | 8.9990 | 4369 | 0.0090          | 0.9093    | 0.9322 | 0.9207 | 0.9985   |
| 0.0001        | 9.9897 | 4850 | 0.0094          | 0.9169    | 0.9293 | 0.9231 | 0.9986   |


### Framework versions

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