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Aug 17, 2017 · UNK means unknown word, a word that doesn't exist the the vocabulary set. It seems that count is supposed to be a list of pairs of form ['word', number_of_occurences]. -1 is apparently a placeholder value which later is filled with count[0][1] = unk_count. It's a bad, slow, non-"pythonic way" code.
Mar 12, 2018 · The unk token in the pretrained GloVe files is not an unknown token! See this google groups thread where Jeffrey Pennington (GloVe author) writes: The pre-trained vectors do not have an unknown token, and currently the code just ignores out-of-vocabulary words when producing the co-occurrence counts.
Apr 19, 2021 · You might use a capture group to keep <unk>, match non word characters excluding whitespace chars, and replace the double whitespace chars (which can occur after the first substitution) with a single space. The pattern matches: (?<!\S)(<unk>)(?!\S) Capture <unk> between whitespace boundaries in group 1 to keep in the replacement.
Jan 7, 2011 · from tbl. where 1 = case ISNUMERIC(result + 'e0') when 1 then case when CAST(result as float) < 4.0. then 1. else 0. end. when 0 then 0. end. @ajo: Modified my answer with a version that will work for MSSQL.
Feb 12, 2020 · I have designed a model based on BERT to solve NER task. I am using transformers library with the "dccuchile/bert-base-spanish-wwm-cased" pre-trained model. The problem comes when my model detect an entity but the token is '[UNK]'. How could I know which is the string behind that token?
May 7, 2024 · For SentencePieceBPETokenizer, Exception: Unk token `<unk>` not found in the vocabulary
Jul 19, 2021 · If ' [UNK]' is the first (or second when using padding) in the vocabulary, which means that it has token 0 (or 1 when using padding), then the ' [UNK]' will be heavily sampled as negative sample.
Aug 19, 2018 · A RNN will give you a sampling of tokens that are most likely to appear next in your text. In your code you choose the token with the highest probability, in this case «unk». In this case you can omit the «ukn» token and simply take the next most likely token that the RNN suggests based on the probability values that it renders. edited Aug ...
Oct 26, 2019 · Adding a UNK token to the vocabulary is a conventional way to handle oov words in tasks of NLP. It is totally understandable to have it for encoding, but what's the point to have it for decoding? I mean you would never expect your decoder to generate a UNK token during prediction, right?
13. Instead of assigning all the Out of Vocabulary tokens to a common UNK vector (zeros), it is better to assign them a unique random vector. At-least this way when you find the similarity between them with any other word, each of them will be unique and the model can learn something out of it.
