Create README.md

RAEDME.md

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+---
+language: 
+- ar
+license: apache-2.0
+widget:
+ - text: "أنا بخير"
+---
+# CAMeLBERT Mix SA Model
+## Model description
+**CAMeLBERT Mix SA Model** is a Sentiment Analysis (SA) 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 [ASTD](https://aclanthology.org/D15-1299.pdf), [ArSAS](http://lrec-conf.org/workshops/lrec2018/W30/pdf/22_W30.pdf), and [SemEval](https://aclanthology.org/S17-2088.pdf) datasets.
+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 SA model directly as part of our [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) SA 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) SA component:
+```python
+>>> from camel_tools.sentiment import SentimentAnalyzer
+>>> sa = SentimentAnalyzer("CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment")
+>>> sentences = ['أنا بخير', 'أنا لست بخير']
+>>> sa.predict(sentences)
+>>> ['positive', 'negative']
+```
+You can also use the NER model directly with a transformers pipeline:
+```python
+>>> from transformers import pipeline
+>>> sa = pipeline('sentiment-analysis', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment')
+>>> sentences = ['أنا بخير', 'أنا لست بخير']
+>>> sa(sentences)
+[{'label': 'positive', 'score': 0.9616648554801941},
+ {'label': 'negative', 'score': 0.9779177904129028}]
+```
+*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.",
+}
+```