| | --- |
| | language: |
| | - en |
| | license: mit |
| | --- |
| | |
| | This model was trained with [Neural-Cherche](https://github.com/raphaelsty/neural-cherche). You can find details on how to fine-tune it in the [Neural-Cherche](https://github.com/raphaelsty/neural-cherche) repository. |
| |
|
| |
|
| | ```sh |
| | pip install neural-cherche |
| | ``` |
| |
|
| | ## Retriever |
| |
|
| | ```python |
| | from neural_cherche import models, retrieve |
| | import torch |
| | |
| | device = "cuda" if torch.cuda.is_available() else "cpu" |
| | batch_size = 32 |
| | |
| | documents = [ |
| | {"id": 0, "document": "Food"}, |
| | {"id": 1, "document": "Sports"}, |
| | {"id": 2, "document": "Cinema"}, |
| | ] |
| | |
| | queries = ["Food", "Sports", "Cinema"] |
| | |
| | model = models.SparseEmbed( |
| | model_name_or_path="raphaelsty/neural-cherche-sparse-embed", |
| | device=device, |
| | ) |
| | |
| | retriever = retrieve.SparseEmbed( |
| | key="id", |
| | on=["document"], |
| | model=model, |
| | ) |
| | |
| | documents_embeddings = retriever.encode_documents( |
| | documents=documents, |
| | batch_size=batch_size, |
| | ) |
| | |
| | retriever = retriever.add( |
| | documents_embeddings=documents_embeddings, |
| | ) |
| | |
| | queries_embeddings = retriever.encode_queries( |
| | queries=queries, |
| | batch_size=batch_size, |
| | ) |
| | |
| | scores = retriever( |
| | queries_embeddings=queries_embeddings, |
| | batch_size=batch_size, |
| | k=100, |
| | ) |
| | |
| | scores |
| | ``` |
| |
|