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* push ModernBertForQuestionAnswering * update ModernBertForQuestionAnswering * update __init__ loading * set imports for ModernBertForQuestionAnswering * update ModernBertForQuestionAnswering * remove debugging logs * update init_weights method * remove custom initialization for ModernBertForQuestionAnswering * apply make fix-copies * apply make style * apply make fix-copies * append ModernBertForQuestionAnswering to the pipeline supported models * remove unused file * remove invalid autoload value * update en/model_doc/modernbert.md * apply make fixup command * make fixup * Update dummies * update usage tips for ModernBertForQuestionAnswering * update usage tips for ModernBertForQuestionAnswering * add init * add lint * add consistency * update init test * change text to trigger stuck text * use self.loss_function instead of custom loss By @Cyrilvallez Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com> * Update modeling_modernbert.py make comparable commit to even it out * Match whitespace * whitespace --------- Co-authored-by: Matt <rocketknight1@gmail.com> Co-authored-by: Orion Weller <wellerorion@gmail.com> Co-authored-by: Orion Weller <31665361+orionw@users.noreply.github.com> Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com>
106 lines
6.0 KiB
Markdown
106 lines
6.0 KiB
Markdown
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
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# ModernBERT
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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## Overview
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The ModernBERT model was proposed in [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://arxiv.org/abs/2412.13663) by Benjamin Warner, Antoine Chaffin, Benjamin Clavié, Orion Weller, Oskar Hallström, Said Taghadouini, Alexis Galalgher, Raja Bisas, Faisal Ladhak, Tom Aarsen, Nathan Cooper, Grifin Adams, Jeremy Howard and Iacopo Poli.
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It is a refresh of the traditional encoder architecture, as used in previous models such as [BERT](https://huggingface.co/docs/transformers/en/model_doc/bert) and [RoBERTa](https://huggingface.co/docs/transformers/en/model_doc/roberta).
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It builds on BERT and implements many modern architectural improvements which have been developed since its original release, such as:
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- [Rotary Positional Embeddings](https://huggingface.co/blog/designing-positional-encoding) to support sequences of up to 8192 tokens.
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- [Unpadding](https://arxiv.org/abs/2208.08124) to ensure no compute is wasted on padding tokens, speeding up processing time for batches with mixed-length sequences.
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- [GeGLU](https://arxiv.org/abs/2002.05202) Replacing the original MLP layers with GeGLU layers, shown to improve performance.
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- [Alternating Attention](https://arxiv.org/abs/2004.05150v2) where most attention layers employ a sliding window of 128 tokens, with Global Attention only used every 3 layers.
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- [Flash Attention](https://github.com/Dao-AILab/flash-attention) to speed up processing.
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- A model designed following recent [The Case for Co-Designing Model Architectures with Hardware](https://arxiv.org/abs/2401.14489), ensuring maximum efficiency across inference GPUs.
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- Modern training data scales (2 trillion tokens) and mixtures (including code ande math data)
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The abstract from the paper is the following:
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*Encoder-only transformer models such as BERT offer a great performance-size tradeoff for retrieval and classification tasks with respect to larger decoder-only models. Despite being the workhorse of numerous production pipelines, there have been limited Pareto improvements to BERT since its release. In this paper, we introduce ModernBERT, bringing modern model optimizations to encoder-only models and representing a major Pareto improvement over older encoders. Trained on 2 trillion tokens with a native 8192 sequence length, ModernBERT models exhibit state-of-the-art results on a large pool of evaluations encompassing diverse classification tasks and both single and multi-vector retrieval on different domains (including code). In addition to strong downstream performance, ModernBERT is also the most speed and memory efficient encoder and is designed for inference on common GPUs.*
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The original code can be found [here](https://github.com/answerdotai/modernbert).
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## Resources
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A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ModernBert.
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<PipelineTag pipeline="text-classification"/>
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- A notebook on how to [finetune for General Language Understanding Evaluation (GLUE) with Transformers](https://github.com/AnswerDotAI/ModernBERT/blob/main/examples/finetune_modernbert_on_glue.ipynb), also available as a Google Colab [notebook](https://colab.research.google.com/github/AnswerDotAI/ModernBERT/blob/main/examples/finetune_modernbert_on_glue.ipynb). 🌎
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<PipelineTag pipeline="sentence-similarity"/>
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- A script on how to [finetune for text similarity or information retrieval with Sentence Transformers](https://github.com/AnswerDotAI/ModernBERT/blob/main/examples/train_st.py). 🌎
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- A script on how to [finetune for information retrieval with PyLate](https://github.com/AnswerDotAI/ModernBERT/blob/main/examples/train_pylate.py). 🌎
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<PipelineTag pipeline="fill-mask"/>
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- [Masked language modeling task guide](../tasks/masked_language_modeling)
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<PipelineTag pipeline="question-answering"/>
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- [`ModernBertForQuestionAnswering`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering) and [colab notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering.ipynb).
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## ModernBertConfig
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[[autodoc]] ModernBertConfig
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<frameworkcontent>
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<pt>
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## ModernBertModel
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[[autodoc]] ModernBertModel
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- forward
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## ModernBertForMaskedLM
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[[autodoc]] ModernBertForMaskedLM
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- forward
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## ModernBertForSequenceClassification
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[[autodoc]] ModernBertForSequenceClassification
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- forward
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## ModernBertForTokenClassification
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[[autodoc]] ModernBertForTokenClassification
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- forward
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## ModernBertForQuestionAnswering
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[[autodoc]] ModernBertForQuestionAnswering
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- forward
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### Usage tips
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The ModernBert model can be fine-tuned using the HuggingFace Transformers library with its [official script](https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa.py) for question-answering tasks.
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</pt>
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</frameworkcontent>
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