Create README.md

README.md

@@ -0,0 +1,72 @@
+---
+language: 
+- ar
+license: apache-2.0
+widget:
+ - text: "إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع"
+---
+# CAMeLBERT-Mix NER Model
+## Model description
+**CAMeLBERT-Mix NER Model** is a Named Entity Recognition (NER) model that was built by fine-tuning the [CAMeLBERT Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) 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-Mix 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
+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-mix-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-mix-ner')
+>>> ner("إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع")
+[{'word': 'أبوظبي',
+  'score': 0.9895730018615723,
+  'entity': 'B-LOC',
+  'index': 2,
+  'start': 6,
+  'end': 12},
+ {'word': 'الإمارات',
+  'score': 0.8156259655952454,
+  'entity': 'B-LOC',
+  'index': 8,
+  'start': 33,
+  'end': 41},
+ {'word': 'العربية',
+  'score': 0.890906810760498,
+  'entity': 'I-LOC',
+  'index': 9,
+  'start': 42,
+  'end': 49},
+ {'word': 'المتحدة',
+  'score': 0.8169114589691162,
+  'entity': 'I-LOC',
+  'index': 10,
+  'start': 50,
+  'end': 57}]
+```
+*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,
+    title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
+    author = "Inoue, Go  and
+      Alhafni, Bashar  and
+      Baimukan, Nurpeiis  and
+      Bouamor, Houda  and
+      Habash, Nizar",
+    booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
+    month = apr,
+    year = "2021",
+    address = "Kyiv, Ukraine (Online)",
+    publisher = "Association for Computational Linguistics",
+    abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
+}
+```