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transformers

huggingface / transformers

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Raise exceptions instead of using assertions for control flow

10
willfrey
willfrey commented Jul 19, 2021

https://github.com/huggingface/transformers/blob/546dc24e0883e5e9f5eb06ec8060e3e6ccc5f6d7/src/transformers/models/gpt2/modeling_gpt2.py#L698

Assertions can't be relied upon for control flow because they can be disabled, as per the following:

$ python --help
usage: python [option] ... [-c cmd | -m mod | file | -] [arg] ...
...
-O     : remove assert and __debug__-dependent statem
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gluon-nlp

dmlc / gluon-nlp

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[Error Message] Improve error message in SentencepieceTokenizer when arguments are not expected.

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preeyank5
preeyank5 commented Dec 3, 2020

Description

While using tokenizers.create with the model and vocab file for a custom corpus, the code throws an error and is not able to generate the BERT vocab file

Error Message

ValueError: Mismatch vocabulary! All special tokens specified must be control tokens in the sentencepiece vocabulary.

To Reproduce

from gluonnlp.data import tokenizers
tokenizers.create('spm', model_p

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JohnSnowLabs / nlu

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1 line for thousands of State of The Art NLP models in hundreds of languages The fastest and most accurate way to solve text problems.

sentiment-analysis text-classification entity-resolution nlu transformers language-detection pandas named-entity-recognition text-summarization seq2seq lemmatizer spell-checker text-translation dependency-parsing sentiment-classifier natural-language-understanding sentence-embeddings t5 streamlit bert-embedding
  • Updated Sep 29, 2021
  • Python
ArticutAPI

Droidtown / ArticutAPI

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API of Articut 中文斷詞 (兼具語意詞性標記)：「斷詞」又稱「分詞」，是中文資訊處理的基礎。Articut 不用機器學習，不需資料模型，只用現代白話中文語法規則，即能達到 SIGHAN 2005 F1-measure 94% 以上，Recall 96% 以上的成績。

nlp natural-language-processing nlu artificial-intelligence cws pos-tagging part-of-speech-tagger pos-tagger natural-language-understanding part-of-speech-embdding
  • Updated Sep 27, 2021
  • Python

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