metadata
license: mit
tags:
- generated_from_trainer
datasets:
- tner/wikiann
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
widget:
- text: 'Ciao, sono Giacomo. Vivo a Milano e lavoro da Armani. '
example_title: Example 1
- text: 'Domenica andrò allo stadio con Giovanna a guardare la Fiorentina. '
example_title: Example 2
language:
- it
bert-italian-finetuned-ner
This model is a fine-tuned version of 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
Token classification for italian language experiment, NER.
Intended uses & limitations
The model can be used on token classification, in particular NER. It is fine tuned on italian language.
Training and evaluation data
The dataset used is wikiann
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