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bert-italian-finetuned-ner

Token Classification
Transformers
PyTorch
Safetensors
Italian
bert
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
3
bert-italian-finetuned-ner
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---
license: mit
tags:
- generated_from_trainer
datasets:
- wiki_neural
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-italian-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wiki_neural
      type: wiki_neural
      config: it
      split: validation
      args: it
    metrics:
    - name: Precision
      type: precision
      value: 0.9438064759036144
    - name: Recall
      type: recall
      value: 0.954225352112676
    - name: F1
      type: f1
      value: 0.9489873178118493
    - name: Accuracy
      type: accuracy
      value: 0.9917883014379933
---

<!-- 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. -->

# bert-italian-finetuned-ner

This model is a fine-tuned version of [dbmdz/bert-base-italian-cased](https://huggingface.co/dbmdz/bert-base-italian-cased) on the wiki_neural dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0361
- Precision: 0.9438
- Recall: 0.9542
- F1: 0.9490
- Accuracy: 0.9918

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0297        | 1.0   | 11050 | 0.0323          | 0.9324    | 0.9420 | 0.9372 | 0.9908   |
| 0.0173        | 2.0   | 22100 | 0.0324          | 0.9445    | 0.9514 | 0.9479 | 0.9915   |
| 0.0057        | 3.0   | 33150 | 0.0361          | 0.9438    | 0.9542 | 0.9490 | 0.9918   |


### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2