Moka-ai

Models by this creator

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833

m3e-base

moka-ai

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-base model 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.

Updated 5/28/2024

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185

m3e-large

moka-ai

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 5/28/2024

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m3e-small

moka-ai

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 9/6/2024

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