ModernBERT

ModernBERT is a next-generation encoder model with outstanding performance.

CommonProductProgrammingBERTTransformer

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.

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.

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

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.

1. Install the ModernBERT model: Use pip to install the `transformers` library and load the ModernBERT model from Hugging Face Hub.
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

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