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Modernbert
Overview :
ModernBERT is a next-generation encoder model co-released by Answer.AI and LightOn. It is a comprehensive upgrade of the BERT model, offering longer sequence lengths, better downstream performance, and faster processing speeds. ModernBERT leverages the latest Transformer architecture improvements, with a strong focus on efficiency, trained on modern data scales and sources. As an encoder model, it excels in a variety of natural language processing tasks, particularly in code search and understanding. It is available in two model sizes: a base version (139M parameters) and a large version (395M parameters), suitable for diverse application needs.
Target Users :
The target audience includes researchers, developers, and enterprise users in the natural language processing field. ModernBERT, known for its exceptional performance and efficiency, is particularly suitable for applications that need to handle large volumes of data and require real-time responsiveness, such as search engines, recommendation systems, and chatbots. Moreover, due to its advantages in code comprehension and retrieval, it is also highly beneficial for developers and coding assistance tools.
Use Cases
Used as an encoder in the RAG (Retrieval-Augmented Generation) pipeline to enhance semantic understanding.
Part of an AI-powered integrated development environment (IDE), providing rapid long-context code retrieval.
Processes tasks combining code and natural language on the StackOverflow-QA dataset, achieving exceptional performance with scores exceeding 80.
Features
Supports input sequences of up to 8192 tokens, which is 16 times longer than most encoders.
Excels in various natural language processing tasks including classification, retrieval, and question answering.
Can be loaded and used as a masked language model (MLM) through the `fill-mask` pipeline or `AutoModelForMaskedLM`.
Does not use token type IDs, simplifying downstream use compared to standard BERT models.
Includes a substantial amount of code in the training data, providing unique advantages for programming-related tasks.
Supports Flash Attention 2 for enhanced efficiency.
Serves as a plug-and-play substitute for any BERT-like models.
How to Use
1. Install the ModernBERT model: Use pip to install the `transformers` library and load the ModernBERT model from Hugging Face Hub.
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2. Load the model and tokenizer: Use `AutoTokenizer` and `AutoModelForMaskedLM` to load the tokenizer and model from the pre-trained model.
3. Prepare input text: Encode the text to be processed using the tokenizer to obtain a format that the model can understand.
4. Model inference: Pass the encoded input to the model to obtain the output.
5. Decode the predicted results: Based on the model output logits, identify the predicted token ID and decode it into readable text.
6. Fine-tune the model: Fine-tune the ModernBERT model according to specific downstream tasks to adapt it for particular application scenarios.
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