Updating README

README.md

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 **CAMeLBERT MSA NER Model** is a 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 or as part of our [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) NER component.
+You can use the CAMeLBERT MSA NER model directly as part of our [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) NER component (*recommended*) or as part of the transformers pipeline.
 
 #### How to use
-You can use this model directly with a pipeline to do NER:
+To use the model with the [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) NER component:
+```python
+>>> from camel_tools.ner import NERecognizer
+>>> from camel_tools.tokenizers.word import simple_word_tokenize
+>>> ner = NERecognizer('CAMeL-Lab/bert-base-arabic-camelbert-msa-ner')
+>>> sentence = simple_word_tokenize('إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع')
+>>> ner.predict_sentence(sentence)
+>>> ['O', 'B-LOC', 'O', 'O', 'O', 'O', 'B-LOC', 'I-LOC', 'I-LOC', 'O']
+```
+
+
+You can also use the NER model directly with a transformers pipeline:
 ```python
 >>> from transformers import pipeline
 >>> ner = pipeline('ner', model='CAMeL-Lab/bert-base-arabic-camelbert-msa-ner')
@@ -43,16 +54,6 @@ 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