<div align="center">
<h1>Neural-Cherche</h1>
<p>Neural Search</p>
</div>
<p align="center"><img width=500 src="docs/img/logo.png"/></p>
<div align="center">
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Neural-Cherche is a library designed to fine-tune neural search models such as Splade, ColBERT, and SparseEmbed on a specific dataset. Neural-Cherche also provide classes to run efficient inference on a fine-tuned retriever or ranker. Neural-Cherche aims to offer a straightforward and effective method for fine-tuning and utilizing neural search models in both offline and online settings. It also enables users to save all computed embeddings to prevent redundant computations.
## Installation
We can install neural_cherche using:
```
pip install neural-cherche
```
If we plan to evaluate our model while training install:
```
pip install "neural-cherche[eval]"
```
## Documentation
The complete documentation is available [here](https://neural-cherche.readthedocs.io/en/latest/).
## Quick Start
Your training dataset must be made out of triples `(anchor, positive, negative)` where anchor is a query, positive is a document that is directly linked to the anchor and negative is a document that is not relevant for the anchor.
```python
X = [
("anchor 1", "positive 1", "negative 1"),
("anchor 2", "positive 2", "negative 2"),
("anchor 3", "positive 3", "negative 3"),
]
```
And here is how to fine-tune ColBERT using neural-cherche:
```python
import torch
from neural_cherche import models, utils, train
model = models.ColBERT(
model_name_or_path="sentence-transformers/all-mpnet-base-v2",
device="cuda" if torch.cuda.is_available() else "cpu"
)
optimizer = torch.optim.AdamW(model.parameters(), lr=3e-5)
X = [
("query", "positive document", "negative document"),
("query", "positive document", "negative document"),
("query", "positive document", "negative document"),
]
for anchor, positive, negative in utils.iter(
X,
epochs=1,
batch_size=32,
shuffle=True
):
loss = train.train_colbert(
model=model,
optimizer=optimizer,
anchor=anchor,
positive=positive,
negative=negative,
)
model.save_pretrained("checkpoint")
```
## References
- *[SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking](https://arxiv.org/abs/2107.05720)* authored by Thibault Formal, Benjamin Piwowarski, Stéphane Clinchant, SIGIR 2021.
- *[SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval](https://arxiv.org/abs/2109.10086)* authored by Thibault Formal, Carlos Lassance, Benjamin Piwowarski, Stéphane Clinchant, SIGIR 2022.
- *[SparseEmbed: Learning Sparse Lexical Representations with Contextual Embeddings for Retrieval](https://research.google/pubs/pub52289/)* authored by Weize Kong, Jeffrey M. Dudek, Cheng Li, Mingyang Zhang, and Mike Bendersky, SIGIR 2023.
- *[ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://arxiv.org/abs/2004.12832)* authored by Omar Khattab, Matei Zaharia, SIGIR 2020.
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"description": "<div align=\"center\">\n <h1>Neural-Cherche</h1>\n <p>Neural Search</p>\n</div>\n\n<p align=\"center\"><img width=500 src=\"docs/img/logo.png\"/></p>\n\n<div align=\"center\">\n <!-- Documentation -->\n <a href=\"https://raphaelsty.github.io/neural-cherche/\"><img src=\"https://img.shields.io/website?label=Documentation&style=flat-square&url=https%3A%2F%2Fraphaelsty.github.io/neural-cherche/%2F\" alt=\"documentation\"></a>\n <!-- License -->\n <a href=\"https://opensource.org/licenses/MIT\"><img src=\"https://img.shields.io/badge/License-MIT-blue.svg?style=flat-square\" alt=\"license\"></a>\n</div>\n\nNeural-Cherche is a library designed to fine-tune neural search models such as Splade, ColBERT, and SparseEmbed on a specific dataset. Neural-Cherche also provide classes to run efficient inference on a fine-tuned retriever or ranker. Neural-Cherche aims to offer a straightforward and effective method for fine-tuning and utilizing neural search models in both offline and online settings. It also enables users to save all computed embeddings to prevent redundant computations.\n\n## Installation\n\nWe can install neural_cherche using:\n\n```\npip install neural-cherche\n```\n\nIf we plan to evaluate our model while training install:\n\n```\npip install \"neural-cherche[eval]\"\n```\n\n## Documentation\n\nThe complete documentation is available [here](https://neural-cherche.readthedocs.io/en/latest/).\n\n## Quick Start\n\nYour training dataset must be made out of triples `(anchor, positive, negative)` where anchor is a query, positive is a document that is directly linked to the anchor and negative is a document that is not relevant for the anchor.\n\n```python\nX = [\n (\"anchor 1\", \"positive 1\", \"negative 1\"),\n (\"anchor 2\", \"positive 2\", \"negative 2\"),\n (\"anchor 3\", \"positive 3\", \"negative 3\"),\n]\n```\n\nAnd here is how to fine-tune ColBERT using neural-cherche:\n\n```python\nimport torch\n\nfrom neural_cherche import models, utils, train\n\nmodel = models.ColBERT(\n model_name_or_path=\"sentence-transformers/all-mpnet-base-v2\",\n device=\"cuda\" if torch.cuda.is_available() else \"cpu\"\n)\n\noptimizer = torch.optim.AdamW(model.parameters(), lr=3e-5)\n\nX = [\n (\"query\", \"positive document\", \"negative document\"),\n (\"query\", \"positive document\", \"negative document\"),\n (\"query\", \"positive document\", \"negative document\"),\n]\n\nfor anchor, positive, negative in utils.iter(\n X,\n epochs=1,\n batch_size=32,\n shuffle=True\n ):\n\n loss = train.train_colbert(\n model=model,\n optimizer=optimizer,\n anchor=anchor,\n positive=positive,\n negative=negative,\n )\n\nmodel.save_pretrained(\"checkpoint\")\n```\n\n## References\n\n- *[SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking](https://arxiv.org/abs/2107.05720)* authored by Thibault Formal, Benjamin Piwowarski, St\u00e9phane Clinchant, SIGIR 2021.\n\n- *[SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval](https://arxiv.org/abs/2109.10086)* authored by Thibault Formal, Carlos Lassance, Benjamin Piwowarski, St\u00e9phane Clinchant, SIGIR 2022.\n\n- *[SparseEmbed: Learning Sparse Lexical Representations with Contextual Embeddings for Retrieval](https://research.google/pubs/pub52289/)* authored by Weize Kong, Jeffrey M. Dudek, Cheng Li, Mingyang Zhang, and Mike Bendersky, SIGIR 2023.\n\n- *[ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://arxiv.org/abs/2004.12832)* authored by Omar Khattab, Matei Zaharia, SIGIR 2020.\n",
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