mirror of
https://github.com/explosion/spaCy.git
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82159b5c19
* Updates/bugfixes for NER/IOB converters * Converter formats `ner` and `iob` use autodetect to choose a converter if possible * `iob2json` is reverted to handle sentence-per-line data like `word1|pos1|ent1 word2|pos2|ent2` * Fix bug in `merge_sentences()` so the second sentence in each batch isn't skipped * `conll_ner2json` is made more general so it can handle more formats with whitespace-separated columns * Supports all formats where the first column is the token and the final column is the IOB tag; if present, the second column is the POS tag * As in CoNLL 2003 NER, blank lines separate sentences, `-DOCSTART- -X- O O` separates documents * Add option for segmenting sentences (new flag `-s`) * Parser-based sentence segmentation with a provided model, otherwise with sentencizer (new option `-b` to specify model) * Can group sentences into documents with `n_sents` as long as sentence segmentation is available * Only applies automatic segmentation when there are no existing delimiters in the data * Provide info about settings applied during conversion with warnings and suggestions if settings conflict or might not be not optimal. * Add tests for common formats * Add '(default)' back to docs for -c auto * Add document count back to output * Revert changes to converter output message * Use explicit tabs in convert CLI test data * Adjust/add messages for n_sents=1 default * Add sample NER data to training examples * Update README * Add links in docs to example NER data * Define msg within converters
349 lines
7.9 KiB
JSON
349 lines
7.9 KiB
JSON
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"ner":"O"
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"orth":"him",
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"ner":"O"
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{
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{
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{
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{
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"ner":"O"
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{
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"orth":"I",
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"tag":"PRP",
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"ner":"O"
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{
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"orth":"can",
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"ner":"O"
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{
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"orth":"tell",
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{
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"orth":"you",
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"orth":"very",
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"ner":"O"
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{
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"orth":"senior",
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"ner":"O"
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{
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"orth":"CEOs",
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"tag":"NNS",
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"ner":"O"
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{
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"orth":"of",
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"tag":"IN",
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"ner":"O"
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{
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"orth":"major",
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"ner":"O"
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{
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"orth":"American",
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"tag":"JJ",
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"ner":"U-NORP"
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{
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"orth":"car",
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"tag":"NN",
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"ner":"O"
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{
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"orth":"companies",
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"ner":"O"
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"ner":"O"
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"ner":"O"
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{
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"ner":"O"
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{
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"orth":"because",
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"ner":"O"
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"ner":"O"
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{
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"ner":"O"
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{
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"orth":"n\u2019t",
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{
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"orth":"worth",
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"ner":"O"
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{
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"ner":"O"
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{
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"orth":"to",
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"tag":"IN",
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"ner":"O"
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},
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{
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"orth":",",
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"tag":",",
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"ner":"O"
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},
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{
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"orth":"\u201d",
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"tag":"''",
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"ner":"O"
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},
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{
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"orth":"said",
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"tag":"VBD",
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"ner":"O"
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},
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{
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"orth":"Thrun",
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"tag":"NNP",
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"ner":"U-PERSON"
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{
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"orth":",",
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"ner":"O"
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},
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{
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"orth":"in",
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"ner":"O"
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{
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"orth":"an",
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"tag":"DT",
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"ner":"O"
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{
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"orth":"interview",
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{
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"orth":"with",
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"tag":"IN",
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"ner":"O"
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},
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{
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"orth":"Recode",
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"tag":"NNP",
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"ner":"U-ORG"
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},
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{
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"orth":"earlier",
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"tag":"RBR",
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"ner":"B-DATE"
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},
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{
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"orth":"this",
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"tag":"DT",
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"ner":"I-DATE"
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},
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{
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"orth":"week",
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"tag":"NN",
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"ner":"L-DATE"
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},
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{
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"orth":".",
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"tag":".",
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"ner":"O"
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}
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]
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}
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]
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}
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]
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}
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] |