| import torch | |
| import pandas as pd | |
| import gradio as gr | |
| from datasets import load_dataset | |
| from sentence_transformers import SentenceTransformer, util, models | |
| q_encoder = SentenceTransformer(modules=[ | |
| models.Transformer(model_name_or_path="checkpoints/q_encoder", max_seq_length=512), | |
| models.Pooling(word_embedding_dimension=768, pooling_mode='cls'), | |
| ]) | |
| doc_embeddings = torch.load('checkpoints/doc_embeddings.pt', map_location=torch.device('cpu')) | |
| docs = pd.DataFrame(load_dataset("maastrichtlawtech/bsard", data_files="articles_fr.csv")['train']) | |
| def search(question): | |
| q_emb = q_encoder.encode(question, convert_to_tensor=True) | |
| hits = util.semantic_search(q_emb, doc_embeddings, top_k=100, score_function=util.cos_sim)[0] | |
| return {docs.loc[h['corpus_id'], 'article'] + '\n\n' + f"- Art. {docs.loc[h['corpus_id'], 'article_no']}, {docs.loc[h['corpus_id'], 'code']}" for h in hits[:5]} | |
| title = "Belgian Legislation Search" | |
| description = "A biencoder model was trained to retrieve relevant statutory articles to legal issues. Ask it a question in French!" | |
| article = """ | |
| The model will return the most semantically relevant laws from a corpus of 22,633 statutory articles collected from 32 Belgian codes. | |
| """ | |
| examples = [ | |
| "Qu'est-ce que je risque si je viole le secret professionnel ?", | |
| "Mon employeur peut-il me licencier alors que je suis malade ?", | |
| "Mon voisin fait beaucoup de bruit, que faire ?", | |
| ] | |
| gr.Interface(fn=search, inputs=['text'], outputs=['textbox']*5, allow_flagging="never", title=title, description=description, article=article, examples=examples).launch() | |