

## Overview
This model is used to identify statistical named entities in large text. Statistical Named Entities are entities that indicate the presence of a statistical claim (such as a hypothesis of an experiment) along with the type of test and the confidence value.

Use this model in your repo to categorize a text document to find claims, test statistics and probability scores. The model uses Flair NLP from ground-up to develop a Stats NER for researchers.

## Usage

from flair.models import SequenceTagger

tagger = SequenceTagger.load("VinayNR/stats-ner")

sentence = Sentence(<your_string>, use_tokenizer=True)

tagger.predict(sentence)
{"language": ["en"], "library_name": "flair", "tags": ["statistics"], "datasets": ["conll2003"], "pipeline_tag": "token-classification"}
flair statistics token-classification en dataset:conll2003 region:us
TAGS
#flair #statistics #token-classification #en #dataset-conll2003 #region-us 



## Overview
This model is used to identify statistical named entities in large text. Statistical Named Entities are entities that indicate the presence of a statistical claim (such as a hypothesis of an experiment) along with the type of test and the confidence value.

Use this model in your repo to categorize a text document to find claims, test statistics and probability scores. The model uses Flair NLP from ground-up to develop a Stats NER for researchers.

## Usage

from URL import SequenceTagger

tagger = URL("VinayNR/stats-ner")

sentence = Sentence(<your_string>, use_tokenizer=True)

tagger.predict(sentence)
## Overview
This model is used to identify statistical named entities in large text. Statistical Named Entities are entities that indicate the presence of a statistical claim (such as a hypothesis of an experiment) along with the type of test and the confidence value.

Use this model in your repo to categorize a text document to find claims, test statistics and probability scores. The model uses Flair NLP from ground-up to develop a Stats NER for researchers. ## Usage

from URL import SequenceTagger

tagger = URL("VinayNR/stats-ner")

sentence = Sentence(<your_string>, use_tokenizer=True)

tagger.predict(sentence)
TAGS
#flair #statistics #token-classification #en #dataset-conll2003 #region-us 
 ## Overview
This model is used to identify statistical named entities in large text. Statistical Named Entities are entities that indicate the presence of a statistical claim (such as a hypothesis of an experiment) along with the type of test and the confidence value.

Use this model in your repo to categorize a text document to find claims, test statistics and probability scores. The model uses Flair NLP from ground-up to develop a Stats NER for researchers. ## Usage

from URL import SequenceTagger

tagger = URL("VinayNR/stats-ner")

sentence = Sentence(<your_string>, use_tokenizer=True)

tagger.predict(sentence)
TAGS
#flair #statistics #token-classification #en #dataset-conll2003 #region-us 
## Overview
This model is used to identify statistical named entities in large text. Statistical Named Entities are entities that indicate the presence of a statistical claim (such as a hypothesis of an experiment) along with the type of test and the confidence value.

Use this model in your repo to categorize a text document to find claims, test statistics and probability scores. The model uses Flair NLP from ground-up to develop a Stats NER for researchers.## Usage

from URL import SequenceTagger

tagger = URL("VinayNR/stats-ner")

sentence = Sentence(<your_string>, use_tokenizer=True)

tagger.predict(sentence)