|
--- |
|
library_name: transformers |
|
license: mit |
|
base_model: DeepMount00/Italian-ModernBERT-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: modernbert-italian-finetuned-ner |
|
results: [] |
|
datasets: |
|
- tner/wikiann |
|
language: |
|
- it |
|
pipeline_tag: token-classification |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# modernbert-italian-finetuned-ner |
|
|
|
This model is a fine-tuned version of [DeepMount00/Italian-ModernBERT-base](https://huggingface.co/DeepMount00/Italian-ModernBERT-base) on [tner/wikiann](https://huggingface.co/datasets/tner/wikiann) dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0422 |
|
- Precision: 0.9339 |
|
- Recall: 0.9452 |
|
- F1: 0.9395 |
|
- Accuracy: 0.9909 |
|
|
|
## Model description |
|
|
|
Token classification for italian language experiment, NER. |
|
|
|
### Example |
|
|
|
```python |
|
from transformers import pipeline |
|
ner_pipeline = pipeline("ner", model="nickprock/modernbert-italian-finetuned-ner", aggregation_strategy="simple") |
|
text = "La sede storica della Olivetti è ad Ivrea" |
|
output = ner_pipeline(text) |
|
``` |
|
|
|
## 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](https://huggingface.co/datasets/tner/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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
- lr_scheduler_type: linear |
|
- num_epochs: 3 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.0277 | 1.0 | 11050 | 0.0324 | 0.9233 | 0.9362 | 0.9297 | 0.9899 | |
|
| 0.0139 | 2.0 | 22100 | 0.0341 | 0.9327 | 0.9428 | 0.9377 | 0.9907 | |
|
| 0.0052 | 3.0 | 33150 | 0.0422 | 0.9339 | 0.9452 | 0.9395 | 0.9909 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.48.3 |
|
- Pytorch 2.5.1+cu124 |
|
- Datasets 3.3.2 |
|
- Tokenizers 0.21.0 |
