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* Rename index.mdx to index.md * With saved modifs * Address review comment * Treat all files * .mdx -> .md * Remove special char * Update utils/tests_fetcher.py Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr> --------- Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
42 lines
2.4 KiB
Markdown
42 lines
2.4 KiB
Markdown
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# BERTología
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Hay un creciente campo de estudio empeñado en la investigación del funcionamiento interno de los transformers de gran escala como BERT
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(que algunos llaman "BERTología"). Algunos buenos ejemplos de este campo son:
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- BERT Rediscovers the Classical NLP Pipeline por 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? por 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 por 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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Para asistir al desarrollo de este nuevo campo, hemos incluido algunas features adicionales en los modelos BERT/GPT/GPT-2 para
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ayudar a acceder a las representaciones internas, principalmente adaptado de la gran obra de Paul Michel
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(https://arxiv.org/abs/1905.10650):
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- accediendo a todos los hidden-states de BERT/GPT/GPT-2,
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- accediendo a todos los pesos de atención para cada head de BERT/GPT/GPT-2,
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- adquiriendo los valores de salida y gradientes de las heads para poder computar la métrica de importancia de las heads y realizar la poda de heads como se explica
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en https://arxiv.org/abs/1905.10650.
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Para ayudarte a entender y usar estas features, hemos añadido un script específico de ejemplo: [bertology.py](https://github.com/huggingface/transformers/tree/main/examples/research_projects/bertology/run_bertology.py) mientras extraes información y cortas un modelo pre-entrenado en
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GLUE.
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