spaCy/examples/training/ner_example_data/ner-token-per-line-with-pos.iob

When	WRB	O
Sebastian	NNP	B-PERSON
Thrun	NNP	I-PERSON
started	VBD	O
working	VBG	O
on	IN	O
self	NN	O
-	HYPH	O
driving	VBG	O
cars	NNS	O
at	IN	O
Google	NNP	B-ORG
in	IN	O
2007	CD	B-DATE
,	,	O
few	JJ	O
people	NNS	O
outside	RB	O
of	IN	O
the	DT	O
company	NN	O
took	VBD	O
him	PRP	O
seriously	RB	O
.	.	O
“	''	O
I	PRP	O
can	MD	O
tell	VB	O
you	PRP	O
very	RB	O
senior	JJ	O
CEOs	NNS	O
of	IN	O
major	JJ	O
American	JJ	B-NORP
car	NN	O
companies	NNS	O
would	MD	O
shake	VB	O
my	PRP$	O
hand	NN	O
and	CC	O
turn	VB	O
away	RB	O
because	IN	O
I	PRP	O
was	VBD	O
n’t	RB	O
worth	JJ	O
talking	VBG	O
to	IN	O
,	,	O
”	''	O
said	VBD	O
Thrun	NNP	B-PERSON
,	,	O
in	IN	O
an	DT	O
interview	NN	O
with	IN	O
Recode	NNP	B-ORG
earlier	RBR	B-DATE
this	DT	I-DATE
week	NN	I-DATE
.	.	O
-												Updates/bugfixes for NER/IOB converters (#4186)

* 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

											
										
										
											2019-08-29 13:04:01 +03:00
+								When	WRB	O
 								Sebastian	NNP	B-PERSON
 								Thrun	NNP	I-PERSON
 								started	VBD	O
 								working	VBG	O
 								on	IN	O
 								self	NN	O
 								-	HYPH	O
 								driving	VBG	O
 								cars	NNS	O
 								at	IN	O
 								Google	NNP	B-ORG
 								in	IN	O
 	CD	B-DATE
 								,	,	O
 								few	JJ	O
 								people	NNS	O
 								outside	RB	O
 								of	IN	O
 								the	DT	O
 								company	NN	O
 								took	VBD	O
 								him	PRP	O
 								seriously	RB	O
 								.	.	O
 								“	''	O
 								I	PRP	O
 								can	MD	O
 								tell	VB	O
 								you	PRP	O
 								very	RB	O
 								senior	JJ	O
 								CEOs	NNS	O
 								of	IN	O
 								major	JJ	O
 								American	JJ	B-NORP
 								car	NN	O
 								companies	NNS	O
 								would	MD	O
 								shake	VB	O
 								my	PRP$	O
 								hand	NN	O
 								and	CC	O
 								turn	VB	O
 								away	RB	O
 								because	IN	O
 								I	PRP	O
 								was	VBD	O
 								n’t	RB	O
 								worth	JJ	O
 								talking	VBG	O
 								to	IN	O
 								,	,	O
 								”	''	O
 								said	VBD	O
 								Thrun	NNP	B-PERSON
 								,	,	O
 								in	IN	O
 								an	DT	O
 								interview	NN	O
 								with	IN	O
 								Recode	NNP	B-ORG
 								earlier	RBR	B-DATE
 								this	DT	I-DATE
 								week	NN	I-DATE
 								.	.	O