Create README.md

README.md

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+---
+language: fa
+license: apache-2.0
+---
+
+# ParsBERT (v2.0)
+A Transformer-based Model for Persian Language Understanding
+
+We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes!
+Please follow the [ParsBERT](https://github.com/hooshvare/parsbert) repo for the latest information about previous and current models.
+
+
+## Persian Sentiment [Digikala, SnappFood, DeepSentiPers]
+
+It aims to classify text, such as comments, based on their emotional bias. We tested three well-known datasets for this task: `Digikala` user comments, `SnappFood` user comments, and `DeepSentiPers` in two binary-form and multi-form types.
+
+
+### DeepSentiPers
+
+which is a balanced and augmented version of SentiPers, contains 12,138 user opinions about digital products labeled with five different classes; two positives (i.e., happy and delighted), two negatives (i.e., furious and angry) and one neutral class. Therefore, this dataset can be utilized for both multi-class and binary classification. In the case of binary classification, the neutral class and its corresponding sentences are removed from the dataset.
+
+**Binary:**
+1. Negative (Furious + Angry)
+2. Positive (Happy + Delighted)
+
+**Multi**
+1. Furious
+2. Angry
+3. Neutral
+4. Happy
+5. Delighted
+
+
+|   Label   |   #  |
+|:---------:|:----:|
+|  Furious  |  236 |
+|   Angry   | 1357 |
+|  Neutral  | 2874 |
+|   Happy   | 2848 |
+| Delighted | 2516 |
+
+
+
+**Download**
+You can download the dataset from:
+
+- [SentiPers](https://github.com/phosseini/sentipers)
+- [DeepSentiPers](https://github.com/JoyeBright/DeepSentiPers)
+
+## Results
+
+The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
+
+|          Dataset         | ParsBERT v2 | ParsBERT v1 | mBERT | DeepSentiPers |
+|:------------------------:|:-----------:|:-----------:|:-----:|:-------------:|
+|  SentiPers (Multi Class) |    71.31*   |    71.11    |   -   |     69.33     |
+| SentiPers (Binary Class) |    92.42*   |    92.13    |   -   |     91.98     |
+
+
+## How to use :hugs:
+
+| Task                | Notebook                                                                                                                                                                                          |
+|---------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Sentiment Analysis | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/hooshvare/parsbert/blob/master/notebooks/Taaghche_Sentiment_Analysis.ipynb) |
+
+
+### BibTeX entry and citation info
+
+Please cite in publications as the following:
+
+```bibtex
+@article{ParsBERT,
+    title={ParsBERT: Transformer-based Model for Persian Language Understanding},
+    author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
+    journal={ArXiv},
+    year={2020},
+    volume={abs/2005.12515}
+}
+```
+
+## Questions?
+Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/issues) repo.