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* toctree * not-doctested.txt * collapse sections * feedback * update * rewrite get started sections * fixes * fix * loading models * fix * customize models * share * fix link * contribute part 1 * contribute pt 2 * fix toctree * tokenization pt 1 * Add new model (#32615) * v1 - working version * fix * fix * fix * fix * rename to correct name * fix title * fixup * rename files * fix * add copied from on tests * rename to `FalconMamba` everywhere and fix bugs * fix quantization + accelerate * fix copies * add `torch.compile` support * fix tests * fix tests and add slow tests * copies on config * merge the latest changes * fix tests * add few lines about instruct * Apply suggestions from code review Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * fix * fix tests --------- Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * "to be not" -> "not to be" (#32636) * "to be not" -> "not to be" * Update sam.md * Update trainer.py * Update modeling_utils.py * Update test_modeling_utils.py * Update test_modeling_utils.py * fix hfoption tag * tokenization pt. 2 * image processor * fix toctree * backbones * feature extractor * fix file name * processor * update not-doctested * update * make style * fix toctree * revision * make fixup * fix toctree * fix * make style * fix hfoption tag * pipeline * pipeline gradio * pipeline web server * add pipeline * fix toctree * not-doctested * prompting * llm optims * fix toctree * fixes * cache * text generation * fix * chat pipeline * chat stuff * xla * torch.compile * cpu inference * toctree * gpu inference * agents and tools * gguf/tiktoken * finetune * toctree * trainer * trainer pt 2 * optims * optimizers * accelerate * parallelism * fsdp * update * distributed cpu * hardware training * gpu training * gpu training 2 * peft * distrib debug * deepspeed 1 * deepspeed 2 * chat toctree * quant pt 1 * quant pt 2 * fix toctree * fix * fix * quant pt 3 * quant pt 4 * serialization * torchscript * scripts * tpu * review * model addition timeline * modular * more reviews * reviews * fix toctree * reviews reviews * continue reviews * more reviews * modular transformers * more review * zamba2 * fix * all frameworks * pytorch * supported model frameworks * flashattention * rm check_table * not-doctested.txt * rm check_support_list.py * feedback * updates/feedback * review * feedback * fix * update * feedback * updates * update --------- Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>
76 lines
3.3 KiB
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76 lines
3.3 KiB
Markdown
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
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# mLUKE
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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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</div>
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## Overview
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The mLUKE model was proposed in [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://arxiv.org/abs/2110.08151) by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka. It's a multilingual extension
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of the [LUKE model](https://arxiv.org/abs/2010.01057) trained on the basis of XLM-RoBERTa.
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It is based on XLM-RoBERTa and adds entity embeddings, which helps improve performance on various downstream tasks
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involving reasoning about entities such as named entity recognition, extractive question answering, relation
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classification, cloze-style knowledge completion.
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The abstract from the paper is the following:
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*Recent studies have shown that multilingual pretrained language models can be effectively improved with cross-lingual
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alignment information from Wikipedia entities. However, existing methods only exploit entity information in pretraining
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and do not explicitly use entities in downstream tasks. In this study, we explore the effectiveness of leveraging
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entity representations for downstream cross-lingual tasks. We train a multilingual language model with 24 languages
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with entity representations and show the model consistently outperforms word-based pretrained models in various
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cross-lingual transfer tasks. We also analyze the model and the key insight is that incorporating entity
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representations into the input allows us to extract more language-agnostic features. We also evaluate the model with a
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multilingual cloze prompt task with the mLAMA dataset. We show that entity-based prompt elicits correct factual
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knowledge more likely than using only word representations.*
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This model was contributed by [ryo0634](https://huggingface.co/ryo0634). The original code can be found [here](https://github.com/studio-ousia/luke).
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## Usage tips
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One can directly plug in the weights of mLUKE into a LUKE model, like so:
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```python
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from transformers import LukeModel
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model = LukeModel.from_pretrained("studio-ousia/mluke-base")
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```
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Note that mLUKE has its own tokenizer, [`MLukeTokenizer`]. You can initialize it as follows:
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```python
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from transformers import MLukeTokenizer
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tokenizer = MLukeTokenizer.from_pretrained("studio-ousia/mluke-base")
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```
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<Tip>
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As mLUKE's architecture is equivalent to that of LUKE, one can refer to [LUKE's documentation page](luke) for all
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tips, code examples and notebooks.
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</Tip>
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## MLukeTokenizer
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[[autodoc]] MLukeTokenizer
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- __call__
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- save_vocabulary
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