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* Fixed typo: insted to instead * Fixed typo: relase to release * Fixed typo: nighlty to nightly * Fixed typos: versatible, benchamarks, becnhmark to versatile, benchmark, benchmarks * Fixed typo in comment: quantizd to quantized * Fixed typo: architecutre to architecture * Fixed typo: contibution to contribution * Fixed typo: Presequities to Prerequisites * Fixed typo: faste to faster * Fixed typo: extendeding to extending * Fixed typo: segmetantion_maps to segmentation_maps * Fixed typo: Alternativelly to Alternatively * Fixed incorrectly defined variable: output to output_disabled * Fixed typo in library name: tranformers.onnx to transformers.onnx * Fixed missing import: import tensorflow as tf * Fixed incorrectly defined variable: token_tensor to tokens_tensor * Fixed missing import: import torch * Fixed incorrectly defined variable and typo: uromaize to uromanize * Fixed incorrectly defined variable and typo: uromaize to uromanize * Fixed typo in function args: numpy.ndarry to numpy.ndarray * Fixed Inconsistent Library Name: Torchscript to TorchScript * Fixed Inconsistent Class Name: OneformerProcessor to OneFormerProcessor * Fixed Inconsistent Class Named Typo: TFLNetForMultipleChoice to TFXLNetForMultipleChoice * Fixed Inconsistent Library Name Typo: Pytorch to PyTorch * Fixed Inconsistent Function Name Typo: captureWarning to captureWarnings * Fixed Inconsistent Library Name Typo: Pytorch to PyTorch * Fixed Inconsistent Class Name Typo: TrainingArgument to TrainingArguments * Fixed Inconsistent Model Name Typo: Swin2R to Swin2SR * Fixed Inconsistent Model Name Typo: EART to BERT * Fixed Inconsistent Library Name Typo: TensorFLow to TensorFlow * Fixed Broken Link for Speech Emotion Classification with Wav2Vec2 * Fixed minor missing word Typo * Fixed minor missing word Typo * Fixed minor missing word Typo * Fixed minor missing word Typo * Fixed minor missing word Typo * Fixed minor missing word Typo * Fixed minor missing word Typo * Fixed minor missing word Typo * Fixed Punctuation: Two commas * Fixed Punctuation: No Space between XLM-R and is * Fixed Punctuation: No Space between [~accelerate.Accelerator.backward] and method * Added backticks to display model.fit() in codeblock * Added backticks to display openai-community/gpt2 in codeblock * Fixed Minor Typo: will to with * Fixed Minor Typo: is to are * Fixed Minor Typo: in to on * Fixed Minor Typo: inhibits to exhibits * Fixed Minor Typo: they need to it needs * Fixed Minor Typo: cast the load the checkpoints To load the checkpoints * Fixed Inconsistent Class Name Typo: TFCamembertForCasualLM to TFCamembertForCausalLM * Fixed typo in attribute name: outputs.last_hidden_states to outputs.last_hidden_state * Added missing verbosity level: fatal * Fixed Minor Typo: take To takes * Fixed Minor Typo: heuristic To heuristics * Fixed Minor Typo: setting To settings * Fixed Minor Typo: Content To Contents * Fixed Minor Typo: millions To million * Fixed Minor Typo: difference To differences * Fixed Minor Typo: while extract To which extracts * Fixed Minor Typo: Hereby To Here * Fixed Minor Typo: addition To additional * Fixed Minor Typo: supports To supported * Fixed Minor Typo: so that benchmark results TO as a consequence, benchmark * Fixed Minor Typo: a To an * Fixed Minor Typo: a To an * Fixed Minor Typo: Chain-of-though To Chain-of-thought
42 lines
2.3 KiB
Markdown
42 lines
2.3 KiB
Markdown
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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rendered properly in your Markdown viewer.
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# BERTology
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There is a growing field of study concerned with investigating the inner working of large-scale transformers like BERT
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(that some call "BERTology"). Some good examples of this field are:
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- BERT Rediscovers the Classical NLP Pipeline by Ian Tenney, Dipanjan Das, Ellie Pavlick:
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https://arxiv.org/abs/1905.05950
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- Are Sixteen Heads Really Better than One? by Paul Michel, Omer Levy, Graham Neubig: https://arxiv.org/abs/1905.10650
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- What Does BERT Look At? An Analysis of BERT's Attention by Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D.
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Manning: https://arxiv.org/abs/1906.04341
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- CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure: https://arxiv.org/abs/2210.04633
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In order to help this new field develop, we have included a few additional features in the BERT/GPT/GPT-2 models to
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help people access the inner representations, mainly adapted from the great work of Paul Michel
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(https://arxiv.org/abs/1905.10650):
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- accessing all the hidden-states of BERT/GPT/GPT-2,
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- accessing all the attention weights for each head of BERT/GPT/GPT-2,
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- retrieving heads output values and gradients to be able to compute head importance score and prune head as explained
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in https://arxiv.org/abs/1905.10650.
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To help you understand and use these features, we have added a specific example script: [bertology.py](https://github.com/huggingface/transformers/tree/main/examples/research_projects/bertology/run_bertology.py) which extracts information and prune a model pre-trained on
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GLUE.
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