README.md

---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- Italian

---

# ItaLegalEmb_v2 🇮🇹
ItaLegalEmb_v2 is the second version of the ItaLegalEmb family embedding models. As his predecessor, it is a specialized embedding model specifically trained on 
a corpus of Italian legal documents.

ItalegalEmb_v2 is based on **BAAI/bge-m3**, a SOTA embedding model with outstanding multilingual skills.

Features:
  Dimensions: 1024
  Sequence Lenght: 8192

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## Evaluation Results
In our evaluations on the specific domain, **ItaLegalEmb_v2** **scores** **93%**, while OpenAI stops at 79% and **ItaLegalEmb** at **85%**.

As llama.cpp team has just released (early August 2024) a version which supports **XLMRoberta** embedding models (ItaLegalEmb_v2 belongs to this), a gguf Q8 version
of the model is also included here 😉.


This is a [sentence-transformers](https://www.SBERT.net) model: It can be used for tasks like clustering or semantic search.

<!--- Describe your model here -->

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```





<!--- Describe how your model was evaluated -->


**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 190 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
  ```
  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
  ```

Parameters of the fit()-Method:
```
{
    "epochs": 3,
    "evaluation_steps": 50,
    "evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 57,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  (2): Normalize()
)
```

## Citing & Authors

@misc{ItaLegalEmb,  
  title = {Kleva-ai/ItaLegalEmb_v2: An embedding model fine-tuned on Italian legal documents.},  
  author = {Obiactum},  
  year = {2024},  
  publisher = {Kleva-ai},  
  journal = {HuggingFace repository},  
  howpublished = {\url{https://huggingface.co/Kleva-ai/ItaLegalEmb_v2}},  
}