1bd604d11c
* Initial commit for MyT5 model * custom implementation of MyT5 tokenizer, unused files deleted * unittest for myt5 tokenizer * upadate of import structure and style * removed remmanents of MyT5Config * fixed docstrings * Updates after review: filled documentaion file, new docstrings and tests added * Fixed code style issues * fixed copied from to refer to function * updated loading myt5 tokenizer in tests, added sample byte map file to fixtures * changes after review * removed redundant copied from * removed redundant copied from * optimalization and loading model from hf * [run_slow] myt5 * [run-slow] myt5 * Updated en documentation for myt5 Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> --------- Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
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myt5
Overview
The myt5 model was proposed in MYTE: Morphology-Driven Byte Encoding for Better and Fairer Multilingual Language Modeling by Tomasz Limisiewicz, Terra Blevins, Hila Gonen, Orevaoghene Ahia, and Luke Zettlemoyer. MyT5 (Myte T5) is a multilingual language model based on T5 architecture. The model uses a morphologically-driven byte (MYTE) representation described in our paper. MYTE uses codepoints corresponding to morphemes in contrast to characters used in UTF-8 encoding. As a pre-requisite, we used unsupervised morphological segmentation (Morfessor) to obtain morpheme inventories for 99 languages. However, the morphological segmentation step is not needed when using the pre-defined morpheme inventory from the hub (see: Tomli/myt5-base).
The abstract from the paper is the following:
A major consideration in multilingual language modeling is how to best represent languages with diverse vocabularies and scripts. Although contemporary text encoding methods cover most of the world’s writing systems, they exhibit bias towards the high-resource languages of the Global West. As a result, texts of underrepresented languages tend to be segmented into long sequences of linguistically meaningless units. To address the disparities, we introduce a new paradigm that encodes the same information with segments of consistent size across diverse languages. Our encoding convention (MYTE) is based on morphemes, as their inventories are more balanced across languages than characters, which are used in previous methods. We show that MYTE produces shorter encodings for all 99 analyzed languages, with the most notable improvements for non-European languages and non-Latin scripts. This, in turn, improves multilingual LM performance and diminishes the perplexity gap throughout diverse languages.
This model was contributed by Tomasz Limisiewicz. The original code can be found here.
MyT5Tokenizer
autodoc MyT5Tokenizer - build_inputs_with_special_tokens - get_special_tokens_mask - create_token_type_ids_from_sequences - save_vocabulary
MyT5Tokenizer
autodoc MyT5Tokenizer