TaPvex: A Tagged and Phrased word2vec Model
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:' TaPvex: A Tagged and Phrased word2vec Model Benjamin J. Radford September 29, 2017 Summary TaPvex is a trained word2vec model of part-of-speech-tagged and named-entity-tagged words and phrases. The model was trained on a large corpus of English language news text from the early 2010s. Words have been tagged using Stanford CoreNLP to include named entities (NER) and Penn Treebank parts-of-speech (POS). Tagged words have been concatenated into n-gram phrases. The model contains 1.17 million unique words and phrases. Word vectors are of size 150. Use All three files (TaPvex, TaPvex.syn0.npy, TaPvex.syn1.npy) must be located in th' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["word vectors, word2vec, nlp, part of speech, pos, named entity recognition, ner"]} <|eot_id|>
 Here is the Description of a dataset. Description:' TaPvex: A Tagged and Phrased word2vec Model Benjamin J. Radford September 29, 2017 Summary TaPvex is a trained word2vec model of part-of-speech-tagged and named-entity-tagged words and phrases. The model was trained on a large corpus of English language news text from the early 2010s. Words have been tagged using Stanford CoreNLP to include named entities (NER) and Penn Treebank parts-of-speech (POS). Tagged words have been concatenated into n-gram phrases. The model contains 1.17 million unique words and phrases. Word vectors are of size 150. Use All three files (TaPvex, TaPvex.syn0.npy, TaPvex.syn1.npy) must be located in th' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
 TaPvex: A Tagged and Phrased word2vec Model Benjamin J. Radford September 29, 2017 Summary TaPvex is a trained word2vec model of part-of-speech-tagged and named-entity-tagged words and phrases. The model was trained on a large corpus of English language news text from the early 2010s. Words have been tagged using Stanford CoreNLP to include named entities (NER) and Penn Treebank parts-of-speech (POS). Tagged words have been concatenated into n-gram phrases. The model contains 1.17 million unique words and phrases. Word vectors are of size 150. Use All three files (TaPvex, TaPvex.syn0.npy, TaPvex.syn1.npy) must be located in th
word vectors, word2vec, nlp, part of speech, pos, named entity recognition, ner