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transformers

huggingface / transformers

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[Good first issue] Documentation links in older docs versions

LysandreJik
LysandreJik commented Oct 22, 2020

🚀 Feature request

This is a documentation request in order to make it easier to find corresponding examples in the documentation.

Good first issue if you want to get acquainted with the docs and how to build docs using Sphinx!

Current issue

Here's the issue: currently, if one goes to an older documentation version to check the "examples" page, for example, [v2.6.0](https://huggin

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Documentation Good First Issue
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yongzhuo / nlp_xiaojiang

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自然语言处理（nlp），小姜机器人（闲聊检索式chatbot），BERT句向量-相似度（Sentence Similarity），XLNET句向量-相似度（text xlnet embedding），文本分类（Text classification）， 实体提取（ner，bert+bilstm+crf），数据增强（text augment, data enhance），同义句同义词生成，句子主干提取（mainpart），中文汉语短文本相似度，文本特征工程，keras-http-service调用

nlp text-classification distance chatbot chinese feature bert data-augmentation enhance text-augment xlnet
  • Updated Oct 26, 2020
  • Python

yongzhuo / Keras-TextClassification

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中文长文本分类、短句子分类、多标签分类、两句子相似度（Chinese Text Classification of Keras NLP, multi-label classify, or sentence classify, long or short），字词句向量嵌入层（embeddings）和网络层（graph）构建基类，FastText，TextCNN，CharCNN，TextRNN, RCNN, DCNN, DPCNN, VDCNN, CRNN, Bert, Xlnet, Albert, Attention, DeepMoji, HAN, 胶囊网络-CapsuleNet, Transformer-encode, Seq2seq, SWEM, LEAM, TextGCN

nlp text-classification keras embeddings fasttext albert bert capsule han rcnn dcnn textcnn crnn dpcnn vdcnn charcnn xlnet transfermer keras-textclassification leam
  • Updated Aug 26, 2020
  • Python

EricFillion / happy-transformer

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Create a logger info message that warns users who do not use HappyROBERTA Large for masked word prediction

EricFillion
EricFillion commented Jan 9, 2020

All other language models do not perform as well as HappyROBERTA large for masked word prediction. We should encourage users to use HappyROBERTA Large by displaying a logger message if they use a suboptimal language model. This message will encourage them to use HappyROBERTA Large.

There are still some situations where a user may want to use another model, so we will keep them available.

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good first issue

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