metadata
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
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
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