You can deploy this model and start earning money today!

bfornet/
XLMRoberta-Juris-GPL

Name: XLMRoberta-Juris-GPL
Availability: InStock

sentence-transformers

text-embeddings-inference

endpoints_compatible

Views

No views yet

{XLMRoberta-Juris-GPL}

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Fine-tuning of SentenceTransformer('paraphrase-multilingual-mpnet-base-v2') using 256 max_seq_length using Generative Pseudo-Labeling method for domain adaptation on 50000+ selected decisions of french Cassation Court ranging from 2005-2022 made by : bruno.fornet@gmail.com and nicolas.ferras@icloud.com for semantic search in private law jurisprudence texts.

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use efficiently the model like this:

python

from sentence_transformers import SentenceTransformer
import torch

model = SentenceTransformer('bfornet/XLMRoberta-Juris-GPL')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

d = 768
model = SentenceTransformer('bfornet/XLMRoberta-Juris-GPL')
query_list=["délai d'action pour demander un rappel de salaire?",\
            "évaluation de l'indemnité d'éviction dans un bail commercial"\
                      ]
embeddings = model.encode(query_list, convert_to_tensor=True).cpu().numpy().astype('float32').reshape(-1, d)
print(embeddings)

Usage (HuggingFace Transformers)

Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

python

from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)

Evaluation Results

For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net

Training

The model was trained with the parameters:

DataLoader:

torch.utils.data.dataloader.DataLoader of length 4272 with parameters:

{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

Loss:

sentence_transformers.losses.MarginMSELoss.MarginMSELoss

Parameters of the fit()-Method:

{
    "epochs": 1,
    "evaluation_steps": 0,
    "evaluator": "NoneType",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 427,
    "weight_decay": 0.01
}

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

Citing & Authors

https://www.pinecone.io/learn/series/nlp/gpl/