camel_tools.ner¶

This module contains the CAMeL Tools Named Entity Recognition component.

Classes¶

class camel_tools.ner.NERecognizer(model_path, use_gpu=True)¶

CAMeL Tools NER component.

Parameters:	model_path (`str`) – The path to the fine-tuned model. use_gpu (`bool`, optional) – The flag to use a GPU or not. Defaults to True.

static labels()¶

Get the list of NER labels returned by predictions.

Returns:	List of NER labels.
Return type:	`list` of `str`

predict(sentences, batch_size=32)¶

Predict the named entity labels of a list of sentences.

Parameters:	sentences (`list` of `list` of `str`) – The input sentences. batch_size (`int`) – The batch size.
Returns:	The predicted named entity labels for the given sentences.
Return type:	`list` of `list` of `str`

predict_sentence(sentence)¶

Predict the named entity labels of a single sentence.

Parameters:	sentence (`list` of `str`) – The input sentence.
Returns:	The predicted named entity labels for the given sentence.
Return type:	`list` of `str`

static pretrained(model_name=None, use_gpu=True)¶

Load a pre-trained model provided with camel_tools.

Parameters:	model_name (`str`, optional) – Name of pre-trained model to load. One model is available: ‘arabert’. If None, the default model (‘arabert’) will be loaded. Defaults to None. use_gpu (`bool`, optional) – The flag to use a GPU or not. Defaults to True.
Returns:	Instance with loaded pre-trained model.
Return type:	`NERecognizer`

Examples¶

Below is an example of how to load and use the default pre-trained model.

from camel_tools.ner import NERecognizer

ner = NERecognizer.pretrained()

# Predict the labels of a single sentence.
# The sentence must be pretokenized by whitespace and punctuation.
sentence = 'إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع .'.split()
labels = ner.predict_sentence(sentence)

# Print the list of token-label pairs
print(list(zip(sentence, labels)))