FlagEmbedding README

The project's README from github.com/FlagOpen/FlagEmbedding. As one markdown file: README.md.

⚡️BGE: One-Stop Retrieval Toolkit For Search and RAG

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News

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  • 6/7/2024: Release a new benchmark MLVU, the first comprehensive benchmark specifically designed for long video understanding. MLVU features an extensive range of video durations, a diverse collection of video sources, and a set of evaluation tasks uniquely tailored for long-form video understanding. :fire:
  • 5/21/2024: Release a new benchmark AIR-Bench together with Jina AI, Zilliz, HuggingFace, and other partners. AIR-Bench focuses on a fair out-of-distribution evaluation for Neural IR & RAG. It generates the synthetic data for benchmarking w.r.t. diverse domains and languages. It is dynamic and will be updated on regular basis. Leaderboard :fire:
  • 4/30/2024: Release Llama-3-8B-Instruct-80K-QLoRA, extending the context length of Llama-3-8B-Instruct from 8K to 80K via QLoRA training on a few synthesized long-context data. The model achieves remarkable performance on various long-context benchmarks. Code :fire:
  • 3/18/2024: Release new rerankers, built upon powerful M3 and LLM (GEMMA and MiniCPM, not so large actually :smiley:) backbones, supporitng multi-lingual processing and larger inputs, massive improvements of ranking performances on BEIR, C-MTEB/Retrieval, MIRACL, LlamaIndex Evaluation :fire:
  • 3/18/2024: Release Visualized-BGE, equipping BGE with visual capabilities. Visualized-BGE can be utilized to generate embeddings for hybrid image-text data. :fire:
  • 1/30/2024: Release BGE-M3, a new member to BGE model series! M3 stands for Multi-linguality (100+ languages), Multi-granularities (input length up to 8192), Multi-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. Technical Report and Code. :fire:
  • 1/9/2024: Release Activation-Beacon, an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. Technical Report
  • 12/24/2023: Release LLaRA, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. Technical Report and Code
  • 11/23/2023: Release LM-Cocktail, a method to maintain general capabilities during fine-tuning by merging multiple language models. Technical Report
  • 10/12/2023: Release LLM-Embedder, a unified embedding model to support diverse retrieval augmentation needs for LLMs. Technical Report
  • 09/15/2023: The technical report of BGE has been released
  • 09/15/2023: The massive training data of BGE has been released
  • 09/12/2023: New models:
    • New reranker model: release cross-encoder models BAAI/bge-reranker-base and BAAI/bge-reranker-large, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
    • update embedding model: release bge-*-v1.5 embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
  • 09/07/2023: Update fine-tune code: Add script to mine hard negatives and support adding instruction during fine-tuning.
  • 08/09/2023: BGE Models are integrated into Langchain, you can use it like this; C-MTEB leaderboard is available.
  • 08/05/2023: Release base-scale and small-scale models, best performance among the models of the same size 🤗
  • 08/02/2023: Release bge-large-*(short for BAAI General Embedding) Models, rank 1st on MTEB and C-MTEB benchmark! :tada: :tada:
  • 08/01/2023: We release the Chinese Massive Text Embedding Benchmark (C-MTEB), consisting of 31 test dataset.

BGE (BAAI General Embedding) focuses on retrieval-augmented LLMs, consisting of the following projects currently:

projects

Installation

Using pip:

If you do not want to finetune the models, you can install the package without the finetune dependency:

pip install -U FlagEmbedding

If you want to finetune the models, you can install the package with the finetune dependency:

pip install -U FlagEmbedding[finetune]

Install from sources:

Clone the repository and install

git clone https://github.com/FlagOpen/FlagEmbedding.git
cd FlagEmbedding
# If you do not need to finetune the models, you can install the package without the finetune dependency:
pip install  .
# If you want to finetune the models, install the package with the finetune dependency:
# pip install  .[finetune]

For development in editable mode:

# If you do not need to finetune the models, you can install the package without the finetune dependency:
pip install -e .
# If you want to finetune the models, install the package with the finetune dependency:
# pip install -e .[finetune]

Quick Start

First, load one of the BGE embedding model:

from FlagEmbedding import FlagAutoModel

model = FlagAutoModel.from_finetuned('BAAI/bge-base-en-v1.5',
                                      query_instruction_for_retrieval="Represent this sentence for searching relevant passages:",
                                      use_fp16=True)

Then, feed some sentences to the model and get their embeddings:

sentences_1 = ["I love NLP", "I love machine learning"]
sentences_2 = ["I love BGE", "I love text retrieval"]
embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)

Once we get the embeddings, we can compute similarity by inner product:

similarity = embeddings_1 @ embeddings_2.T
print(similarity)

For more details, you can refer to embedder inference, reranker inference, embedder finetune, reranker fintune, evaluation.

If you're unfamiliar with any of related concepts, please check out the tutorial. If it's not there, let us know.

For more interesting topics related to BGE, take a look at research.

Community

We are actively maintaining the community of BGE and FlagEmbedding. Let us know if you have any suggessions or ideas!

Currently we are updating the tutorials, we aim to create a comprehensive and detailed tutorial for beginners on text retrieval and RAG. Stay tuned!

The following contents are releasing in the upcoming weeks:

The whole tutorial roadmap

Model List

bge is short for BAAI general embedding.

Model Language Description query instruction for retrieval
BAAI/bge-en-icl English A LLM-based embedding model with in-context learning capabilities, which can fully leverage the model's potential based on a few shot examples Provide instructions and few-shot examples freely based on the given task.
BAAI/bge-multilingual-gemma2 Multilingual A LLM-based multilingual embedding model, trained on a diverse range of languages and tasks. Provide instructions based on the given task.
BAAI/bge-m3 Multilingual Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens)
LM-Cocktail English fine-tuned models (Llama and BGE) which can be used to reproduce the results of LM-Cocktail
BAAI/llm-embedder English a unified embedding model to support diverse retrieval augmentation needs for LLMs See README
BAAI/bge-reranker-v2-m3 Multilingual a lightweight cross-encoder model, possesses strong multilingual capabilities, easy to deploy, with fast inference.
BAAI/bge-reranker-v2-gemma Multilingual a cross-encoder model which is suitable for multilingual contexts, performs well in both English proficiency and multilingual capabilities.
BAAI/bge-reranker-v2-minicpm-layerwise Multilingual a cross-encoder model which is suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers for output, facilitating accelerated inference.
BAAI/bge-reranker-v2.5-gemma2-lightweight Multilingual a cross-encoder model which is suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers, compress ratio and compress layers for output, facilitating accelerated inference.
BAAI/bge-reranker-large Chinese and English a cross-encoder model which is more accurate but less efficient
BAAI/bge-reranker-base Chinese and English a cross-encoder model which is more accurate but less efficient
BAAI/bge-large-en-v1.5 English version 1.5 with more reasonable similarity distribution Represent this sentence for searching relevant passages:
BAAI/bge-base-en-v1.5 English version 1.5 with more reasonable similarity distribution Represent this sentence for searching relevant passages:
BAAI/bge-small-en-v1.5 English version 1.5 with more reasonable similarity distribution Represent this sentence for searching relevant passages:
BAAI/bge-large-zh-v1.5 Chinese version 1.5 with more reasonable similarity distribution 为这个句子生成表示以用于检索相关文章：
BAAI/bge-base-zh-v1.5 Chinese version 1.5 with more reasonable similarity distribution 为这个句子生成表示以用于检索相关文章：
BAAI/bge-small-zh-v1.5 Chinese version 1.5 with more reasonable similarity distribution 为这个句子生成表示以用于检索相关文章：
BAAI/bge-large-en English Embedding Model which map text into vector Represent this sentence for searching relevant passages:
BAAI/bge-base-en English a base-scale model but with similar ability to bge-large-en Represent this sentence for searching relevant passages:
BAAI/bge-small-en English a small-scale model but with competitive performance Represent this sentence for searching relevant passages:
BAAI/bge-large-zh Chines

FlagEmbedding (FlagOpen/FlagEmbedding)

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12.3K ·GitHub
FlagEmbedding
Что это такое?FlagEmbedding (BGE) — комплексный набор инструментов от BAAI для поиска и для LLM, которые используют результаты поиска (retrieval-augmented). В него входят модели эмбеддингов, реранкеры, скрипты для дообучения и инструменты для оценки поиска и RAG.
Это коллекция моделей, Python-библиотека или и то и другое?И то и другое: это Python-пакет, который устанавливается через pip и оборачивает код для инференса и дообучения, а также семейство предобученных моделей эмбеддингов и реранкеров, размещённых на Hugging Face в организации BAAI.
Как мне начать с этим работать?Установите через pip (с дополнением finetune или без него) либо клонируйте репозиторий и установите из исходников в режиме editable. Затем загрузите модель эмбеддингов BGE и передайте ей предложения, чтобы получить эмбеддинги.
Нужно ли мне что-то помимо Python, чтобы этим пользоваться?Достаточно Python и pip. Модели загружаются с Hugging Face (репозитории BAAI). Дополнение 'finetune' нужно только если вы планируете дообучать собственный эмбеддер или реранкер.
Какую модель из списка мне выбрать?Вариантов много, и они частично пересекаются: v1, v1.5, M3, ICL и варианты на базе gemma2. Они различаются языками и компромиссами между точностью и скоростью, поэтому выбрать подходящую можно только после изучения таблицы моделей, это не очевидно.
Поддерживаются ли языки, кроме английского и китайского?Да. Некоторые модели, например BGE-M3 и bge-multilingual-gemma2, явно многоязычные и поддерживают более 100 языков. Модели v1.5 ориентированы на английский и китайский.
Чем BGE-M3 отличается от обычных моделей эмбеддингов?BGE-M3 объединяет три метода поиска в одной модели: плотный (dense), разреженный (sparse, лексический) и мультивекторный в духе ColBERT. Она поддерживает более 100 языков и обрабатывает входные данные длиной до 8192 токенов.
Для чего нужен реранкер, в отличие от эмбеддера?Реранкер — это кросс-энкодер, который пересчитывает релевантность top-k документов, уже найденных моделью эмбеддингов. Обычно он даёт более точные, но более медленные результаты, чем сравнение только по эмбеддингам.
Можно ли использовать BGE с LangChain?Да, в README сказано, что модели BGE интегрированы в LangChain, поэтому их можно напрямую использовать как провайдеров эмбеддингов в этом фреймворке.
Что такое Visualized-BGE?Visualized-BGE добавляет BGE визуальные возможности: модель создаёт эмбеддинги для смешанных данных из изображений и текста, что нужно для мультимодального поиска.
What are similar projects?txtai, LLMSearchRecommender, fastembed-rs, sentence-transformers, SkillCorpus, memvid, PageIndex, MSA
Which topics is it in?information-retrieval, retrieval-augmented-generation, vector-search, semantic-search, embeddings, fine-tuning, transformer, multilingual, nlp, rag, huggingface, benchmark
Creator
FlagOpen
Size
52.2 MB
Stars
12.3K
Updated
CommitAug 24Releasev1.4.2Aug 24
Age
3y
Commits · 90 Days