Bge Large Zh V1.5
The Bge Large Zh V1.5 model is a powerful tool for text retrieval and analysis. It's designed to efficiently handle large amounts of data and provide accurate results. With its ability to support Chinese language inputs, this model is particularly useful for tasks that require understanding and processing of Chinese text. Its efficiency and speed make it a great choice for applications where quick results are needed. The model's unique design allows it to effectively capture the nuances of the Chinese language, making it a valuable resource for anyone working with Chinese text data.
Table of Contents
Model Overview
The FlagEmbedding Model is a powerful tool for natural language processing tasks, designed to help with retrieval-augmented language models. It’s got some amazing features that make it stand out from the rest.
Key Features
- Multi-linguality: Supports 100+ languages
- Multi-granularities: Handles input lengths up to 8192 tokens
- Multi-functionality: Unifies dense, lexical, and multi-vector (ColBERT) retrieval methods
Capabilities
The FlagEmbedding Model is capable of retrieval-augmented language modeling, including tasks such as:
- Dense Retrieval: Retrieves relevant documents from a large corpus based on a query.
- Sparse Retrieval: Retrieves relevant documents from a large corpus based on a query using sparse representations.
- Multi-Vector (ColBERT) Retrieval: Retrieves relevant documents from a large corpus based on a query using multi-vector representations.
- Cross-Lingual Retrieval: Retrieves relevant documents from a large corpus based on a query in a different language.
- Long-Context Language Modeling: Processes and generates text with longer context lengths.
Strengths
The FlagEmbedding Model has several strengths, including:
- Multilinguality: Supports multiple languages, including English and Chinese.
- Multi-Granularity: Handles input lengths up to 8192 tokens.
- Multi-Functionality: Performs multiple tasks, including dense retrieval, sparse retrieval, and multi-vector retrieval.
- State-of-the-Art Performance: Achieves state-of-the-art performance on several benchmarks, including MIRACL and MKQA.
Performance
The FlagEmbedding Model shows remarkable performance in various tasks, especially in retrieval-augmented language models. Let’s dive into its speed, accuracy, and efficiency.
Speed
The FlagEmbedding Model is designed to be efficient, with some models supporting input lengths up to 8192 tokens. This allows for faster processing of large texts.
Accuracy
The FlagEmbedding Model achieves state-of-the-art performances on multi-lingual and cross-lingual benchmarks, such as MIRACL and MKQA.
Efficiency
The FlagEmbedding Model is designed to be efficient, with some models supporting fine-tuning with contrastive learning. This allows for better performance on downstream tasks, such as passage retrieval or semantic similarity.
Comparison with Other Models
Compared to other models, the FlagEmbedding Model offers a range of advantages, including:
- Multi-linguality: Supports multiple languages, making it suitable for tasks that require processing texts in different languages.
- Multi-granularity: Handles input lengths up to 8192 tokens, making it suitable for tasks that require processing large texts.
- Multi-functionality: Performs multiple tasks, including dense retrieval, sparse retrieval, and multi-vector retrieval.
Limitations
The FlagEmbedding Model has several limitations that are important to consider when using it.
Language Limitations
While the FlagEmbedding Model supports multiple languages, its performance may vary across different languages and dialects.
Text Length Limitations
The FlagEmbedding Model has limitations when it comes to handling long texts. It may struggle to capture the nuances and context of longer texts.
Retrieval Methods
The FlagEmbedding Model uses a specific retrieval method that may not be suitable for all use cases.
Similarity Distribution
The FlagEmbedding Model has a similarity distribution issue, where the similarity score between two dissimilar sentences may be higher than expected.
Instruction Requirements
The FlagEmbedding Model requires instructions to be added to queries in certain cases.
Format
The FlagEmbedding Model uses a transformer-based architecture, specifically designed for retrieval-augmented language models. It supports various input formats, including text sequences, and requires pre-processing steps for optimal performance.
Supported Data Formats
- Text sequences
- Tokenized text sequences
Special Requirements
- Input sequences should be pre-processed using a specific tokenizer
- Query instructions are required for certain models
Handling Inputs and Outputs
To use the FlagEmbedding Model, you can follow these examples:
- Using FlagEmbedding:
from FlagEmbedding import FlagModel
sentences = ["example data-1", "example data-2"]
model = FlagModel('BAAI/bge-large-zh-v1.5', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", use_fp16=True)
embeddings = model.encode(sentences)
- Using Sentence-Transformers:
from sentence_transformers import SentenceTransformer
sentences = ["example data-1", "example data-2"]
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
embeddings = model.encode(sentences, normalize_embeddings=True)
