m3e-base
Maintainer: moka-ai
833
🧠
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model Overview
The m3e-base model is part of the M3E (Moka Massive Mixed Embedding) series of models developed by Moka AI. M3E models are designed to be versatile, supporting a variety of natural language processing tasks such as dense retrieval, multi-vector retrieval, and sparse retrieval. The m3e-base model has 110 million parameters and a hidden size of 768.
M3E models are trained on a massive 2.2 billion+ token corpus, making them well-suited for general-purpose language understanding. The models have demonstrated strong performance on benchmarks like MTEB-zh, outperforming models like openai-ada-002 on tasks like sentence-to-sentence (s2s) accuracy and sentence-to-passage (s2p) nDCG@10.
Similar models in the M3E series include the m3e-small and m3e-large versions, which have different parameter sizes and performance characteristics depending on the task.
Model Inputs and Outputs
Inputs
- Text: The
m3e-basemodel can accept text inputs of varying lengths, up to a maximum of 8,192 tokens.
Outputs
- Embeddings: The model outputs dense vector representations of the input text, which can be used for a variety of downstream tasks such as similarity search, text classification, and retrieval.
Capabilities
The m3e-base model has demonstrated strong performance on a range of natural language processing tasks, including:
- Sentence Similarity: The model can be used to compute the semantic similarity between sentences, which is useful for applications like paraphrase detection and text summarization.
- Text Classification: The embeddings produced by the model can be used as features for training text classification models, such as for sentiment analysis or topic classification.
- Retrieval: The model's dense and sparse retrieval capabilities make it well-suited for building search engines and question-answering systems.
What Can I Use It For?
The versatility of the m3e-base model makes it a valuable tool for a wide range of natural language processing applications. Some potential use cases include:
- Semantic Search: Use the model's dense embeddings to build a semantic search engine, allowing users to find relevant information based on the meaning of their queries rather than just keyword matching.
- Personalized Recommendations: Leverage the model's strong text understanding capabilities to build personalized recommendation systems, such as for content or product recommendations.
- Chatbots and Conversational AI: Integrate the model into chatbot or virtual assistant applications to enable more natural and contextual language understanding and generation.
Things to Try
One interesting aspect of the m3e-base model is its ability to perform both dense and sparse retrieval. This hybrid approach can be beneficial for building more robust and accurate retrieval systems.
To experiment with the model's retrieval capabilities, you can try integrating it with tools like chroma, guidance, and semantic-kernel. These tools provide abstractions and utilities for building search and question-answering applications using large language models like m3e-base.
Additionally, the uniem library provides a convenient interface for fine-tuning the m3e-base model on domain-specific datasets, which can further improve its performance on your specific use case.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🧠
m3e-small
44
The m3e-small model is part of the M3E (Moka Massive Mixed Embedding) series of models developed by moka-ai. M3E models are large-scale Chinese language models trained on over 22 million text samples, with capabilities spanning sentence-to-sentence, sentence-to-passage, and sentence-to-code tasks. The m3e-small model is the smaller version, with 24M parameters, while the m3e-base model has 110M parameters. Both models demonstrate strong performance on various Chinese NLP benchmarks, outperforming models like text2vec and openai-ada-002. Model inputs and outputs The M3E models are sentence embedding models, meaning they take in natural language sentences as input and produce vector representations as output. These vector representations can then be used for a variety of downstream tasks like text similarity, classification, and retrieval. Inputs Natural language sentences in Chinese Outputs Numerical vector representations of the input sentences, which capture the semantic meaning of the text Capabilities The M3E models excel at capturing the semantic and contextual meaning of Chinese text. They have shown strong performance on tasks like natural language inference, sentence similarity, and information retrieval. For example, on the MTEB-zh benchmark, the m3e-base model achieved an average accuracy of 0.6157, outperforming text2vec (0.5755) and openai-ada-002 (0.5956). What can I use it for? The M3E models can be leveraged for a wide range of Chinese NLP applications, such as: Semantic search**: Use the sentence embeddings to perform efficient retrieval of relevant documents or passages from a large corpus. Text classification**: Fine-tune the models on labeled datasets to classify text into different categories. Recommendation systems**: Utilize the sentence representations to compute semantic similarity between items and provide personalized recommendations. Chatbots and dialogue systems**: Incorporate the M3E models to understand user intents and generate relevant responses. sentence-transformers, chroma, guidance, and semantic-kernel are some popular libraries and frameworks that can leverage the M3E models for these types of applications. Things to try One interesting aspect of the M3E models is their ability to be fine-tuned on domain-specific datasets using the uniem library. By fine-tuning the m3e-small model on the STS-B dataset, for example, you can further improve its performance on sentence similarity tasks. This flexibility allows the M3E models to be adapted for a wide range of use cases.
Updated Invalid Date
🛸
m3e-large
185
The m3e-large model is part of the M3E (Moka Massive Mixed Embedding) series of text embedding models developed by the Moka AI team. The M3E models are large-scale multilingual text embedding models that can be used for a variety of natural language processing tasks. The m3e-large model is the largest in the series, with 340 million parameters and a 768-dimensional embedding size. The M3E models are designed to provide strong performance on a range of benchmarks, including the MTEB-zh Chinese language benchmark. Compared to similar models like multilingual-e5-large, bge-large-en-v1.5, and moe-llava, the M3E models leverage a massive, mixed-domain training dataset to learn rich and generalizable text representations. The m3e-base model in this series has also shown strong performance, outperforming OpenAI's text-embedding-ada-002 model on several MTEB-zh tasks. Model inputs and outputs Inputs Text sequences**: The m3e-large model can accept single sentences or longer text passages as input. Outputs Text embeddings**: The model outputs fixed-length vector representations (embeddings) of the input text. These embeddings can be used for a variety of downstream tasks, such as semantic search, text classification, and clustering. Capabilities The m3e-large model demonstrates strong performance on a variety of text-based tasks, especially those involving semantic understanding and retrieval. For example, it has achieved a 0.6231 accuracy score on the sentence-to-sentence (s2s) task and a 0.7974 NDCG@10 score on the sentence-to-passage (s2p) task in the MTEB-zh benchmark. What can I use it for? The m3e-large model can be used for a wide range of natural language processing applications, such as: Semantic search**: The rich text embeddings produced by the model can be used to build powerful semantic search engines, allowing users to find relevant information based on the meaning of their queries rather than just keyword matching. Text classification**: The model's embeddings can be used as features for training high-performance text classification models, such as those for sentiment analysis, topic categorization, or intent detection. Recommendation systems**: The semantic understanding of the m3e-large model can be leveraged to build advanced recommendation systems that suggest relevant content or products based on user preferences and behavior. Things to try One interesting aspect of the m3e-large model is its potential for domain-specific fine-tuning. By further training the model on task-specific data using tools like the uniem library, you can likely achieve even stronger performance on specialized applications. Additionally, the model's large size and diverse training data make it a promising starting point for exploring few-shot and zero-shot learning approaches, where the model can leverage its broad knowledge to quickly adapt to new tasks with limited additional training.
Updated Invalid Date
🧪
gte-multilingual-base
80
The gte-multilingual-base model is the latest in the GTE (General Text Embedding) family of models from Alibaba-NLP. It achieves state-of-the-art results in multilingual retrieval tasks and multi-task representation model evaluations compared to models of similar size. Unlike previous GTE models based on decode-only LLM architecture (e.g., gte-qwen2-1.5b-instruct), this encoder-only transformers model has lower hardware requirements for inference, offering a 10x increase in speed. It supports text lengths up to 8192 tokens and over 70 languages. Model inputs and outputs The gte-multilingual-base model takes in text as input and outputs dense embeddings. It can also generate sparse vectors in addition to the dense representations. The elastic dense embedding output helps reduce storage costs and improve execution efficiency while maintaining effectiveness on downstream tasks. Inputs Text sequences up to 8192 tokens in length Outputs Dense vector embeddings of size 768 Sparse vector embeddings Capabilities The gte-multilingual-base model excels at multilingual text retrieval and representation tasks. It achieves state-of-the-art performance on the MTEB benchmark compared to models of similar size. The model's ability to handle long-form text up to 8192 tokens makes it suitable for applications that require processing lengthy documents or passages. What can I use it for? The gte-multilingual-base model is well-suited for a variety of text-based applications that require effective cross-lingual representations, such as: Multilingual information retrieval**: The model's high performance on multilingual retrieval tasks makes it useful for building search engines or recommender systems that need to handle queries and documents in multiple languages. Semantic text similarity**: The model's dense embeddings can be used to measure the semantic similarity between text, enabling applications like paraphrase detection, document clustering, or content-based recommendation. Text reranking**: The model's effectiveness on reranking tasks makes it applicable for improving the ranking of search results or other text-based content. Things to try One interesting aspect of the gte-multilingual-base model is its ability to generate sparse vector embeddings in addition to the dense representations. Sparse vectors can be more efficient to store and transmit, which could be beneficial for applications with storage or bandwidth constraints. Exploring the use of the sparse embeddings and comparing their performance to the dense ones could yield valuable insights.
Updated Invalid Date
📊
Baichuan-13B-Base
185
Baichuan-13B-Base is a large language model developed by Baichuan Intelligence, following their previous model Baichuan-7B. With 13 billion parameters, it achieves state-of-the-art performance on standard Chinese and English benchmarks among models of its size. This release includes both a pre-training model (Baichuan-13B-Base) and an aligned model with dialogue capabilities (Baichuan-13B-Chat). Key features of Baichuan-13B-Base include: Larger model size and more training data: It expands the parameter count to 13 billion based on Baichuan-7B, and has trained on 1.4 trillion tokens, exceeding LLaMA-13B by 40%. Open-source pre-training and alignment models: The pre-training model is suitable for developers, while the aligned model (Baichuan-13B-Chat) has strong dialogue capabilities. Efficient inference: Quantized INT8 and INT4 versions are available for deployment on consumer GPUs with minimal performance loss. Open-source and commercially usable: The model is free for academic research and can also be used commercially after obtaining permission. Model inputs and outputs Inputs Text prompts Outputs Continuation of the input text, generating coherent and relevant responses. Capabilities Baichuan-13B-Base demonstrates impressive performance on a wide range of tasks, including open-ended text generation, question answering, and multi-task benchmarks. It particularly excels at Chinese and English language understanding and generation, making it a powerful tool for developers and researchers working on natural language processing applications. What can I use it for? The Baichuan-13B-Base model can be finetuned for a variety of downstream tasks, such as: Content generation (e.g., articles, stories, product descriptions) Question answering and knowledge retrieval Dialogue systems and chatbots Summarization and text simplification Translation between Chinese and English Developers can also use the model's pre-training as a strong starting point for building custom language models tailored to their specific needs. Things to try With its large scale and strong performance, Baichuan-13B-Base offers many exciting possibilities for experimentation and exploration. Some ideas to try include: Prompt engineering to elicit different types of responses, such as creative writing, task-oriented dialogue, or analytical reasoning. Finetuning the model on domain-specific datasets to create specialized language models for fields like law, medicine, or finance. Exploring the model's capabilities in multilingual tasks, such as cross-lingual question answering or generation. Investigating the model's reasoning abilities by designing prompts that require complex understanding or logical inference. The open-source nature of Baichuan-13B-Base and the accompanying code library make it an accessible and flexible platform for researchers and developers to push the boundaries of large language model capabilities.
Updated Invalid Date