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Intended uses & limitations

How to use

You can use this model with spacy.

!pip install https://huggingface.co/karthid/ta_Tamil_NER/resolve/main/ta_Tamil_NER-any-py3-none-any.whl

import ta_Tamil_NER

from spacy import displacy

nlp = ta_Tamil_NER.load()

doc = nlp("கூகுள் நிறுவனம் தனது முக்கிய வசதியான ஸ்ட்ரீட் வியூ வசதியை 10 நகரங்களில் இந்தியாவில் அறிமுகப்படுத்தி உள்ளது.")

displacy.render(doc,jupyter=True, style = "ent")

Feature Description
Name ta_Tamil_NER
Version 0.0.0
spaCy >=3.2.4,<3.3.0
Default Pipeline transformer, ner
Components transformer, ner
Vectors 0 keys, 0 unique vectors (0 dimensions)
Sources n/a
License n/a
Author Karthi Dhayalan

Label Scheme

View label scheme
Component Labels
ner B-PER, I-PER, B-ORG, I-ORG, B-LOC, I-LOC

Accuracy

Type Score
ENTS_F 84.92
ENTS_P 84.34
ENTS_R 85.52
TRANSFORMER_LOSS 1842600.06
NER_LOSS 108788.05
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Evaluation results