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 NER [ARMAN, PEYMA, ARMAN+PEYMA]
+
+This task aims to extract named entities in the text, such as names and label with appropriate `NER` classes such as locations, organizations, etc. The datasets used for this task contain sentences that are marked with `IOB` format. In this format, tokens that are not part of an entity are tagged as `”O”` the `”B”`tag corresponds to the first word of an object, and the `”I”` tag corresponds to the rest of the terms of the same entity. Both `”B”` and `”I”` tags are followed by a hyphen (or underscore), followed by the entity category. Therefore, the NER task is a multi-class token classification problem that labels the tokens upon being fed a raw text. There are two primary datasets used in Persian NER, `ARMAN`, and `PEYMA`. In ParsBERT, we prepared ner for both datasets as well as a combination of both datasets.
+
+
+### ARMAN
+
+ARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.
+
+1. Organization
+2. Location
+3. Facility
+4. Event
+5. Product
+6. Person
+
+
+|     Label    |   #   |
+|:------------:|:-----:|
+| Organization | 30108 |
+|   Location   | 12924 |
+|   Facility   |  4458 |
+|     Event    |  7557 |
+|    Product   |  4389 |
+|    Person    | 15645 |
+
+**Download**
+You can download the dataset from [here](https://github.com/HaniehP/PersianNER)
+
+
+## Results
+
+The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
+
+| Dataset | ParsBERT v2 | ParsBERT v1 | mBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF |
+|---------|-------------|-------------|-------|------------|--------------|----------|----------------|------------|
+| ARMAN   | 99.84*      | 98.79       | 95.89 | 89.9       | 84.03        | 86.55    | -              | 77.45      |
+
+
+## How to use :hugs:
+
+| Notebook     |      Description      |   |
+|:----------|:-------------|------:|
+| [How to use Pipelines](https://github.com/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb)  | Simple and efficient way to use State-of-the-Art models on downstream tasks through transformers | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.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.