Updating README

README.md

@@ -10,7 +10,7 @@ widget:
 **CAMeLBERT MSA NER Model** is Named Entity Recognition (NER) model that was built by fine-tuning the [CAMeLBERT Modern Standard Arabic (MSA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa/) model. For the fine-tuning, we used the [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/) dataset. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
 
 ## Intended uses
-You can use the CAMeLBERT MSA NER Model directly as part of the transformers pipeline.
+You can use the CAMeLBERT MSA NER Model directly as part of the transformers pipeline or as part of our [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) NER component.
 
 #### How to use
 You can use this model directly with a pipeline to do NER:
@@ -43,8 +43,20 @@ You can use this model directly with a pipeline to do NER:
   'start': 50,
   'end': 57}]
 ```
+
+Here is how to use this model with our CAMeLTools toolkit:
+```python
+>>> from camel_tools.ner import NERecognizer
+>>> ner = NERecognizer('CAMeL-Lab/bert-base-arabic-camelbert-msa-ner')
+>>> sentence = 'إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع'.split()
+>>> ner.predict_sentence(sentence)
+>>> ['O', 'B-LOC', 'O', 'O', 'O', 'O', 'B-LOC', 'I-LOC', 'I-LOC', 'O']
+```
+
 *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models 
 
+
+
 ## Citation
 ```bibtex
 @inproceedings{inoue-etal-2021-interplay,