Create README.md

README.md

@@ -0,0 +1,78 @@
+<span align="center">
+    <a href="https://huggingface.co/SajjadAyoubi/"><img src="https://img.shields.io/static/v1?label=%F0%9F%A4%97%20Hugging%20Face&message=SajjadAyoubi&color=yellow"></a>
+    <a href="https://colab.research.google.com/github/sajjjadayobi/PersianQA/blob/main/notebooks/Demo.ipynb"><img src="https://img.shields.io/static/v1?label=Colab&message=Fine-tuning Example&logo=Google%20Colab&color=f9ab00"></a>
+</span>
+
+# ParsBigBird: Persian Bert For **Long-Range** Sequences
+The [Bert](https://arxiv.org/abs/1810.04805) and [ParsBert](https://arxiv.org/abs/2005.12515) algorithms can handle texts with token lengths of up to 512, however, many tasks such as summarizing and answering questions require longer texts. In our work, we have trained the [BigBird](https://arxiv.org/abs/2007.14062) model for the Persian language to process texts up to 4096 in the Farsi (Persian) language using sparse attention.
+
+## Evaluation: 🌡️
+We have evaluated the model on three tasks with different sequence lengths
+
+|                             Name                                 | Params |    SnappFood (F1)   | Digikala Magazine |  PersianQA (F1)   | 
+| :--------------------------------------------------------------: | :----: | :-----------------: | :---------------: |  :--------------: |
+| [distil-bigbird-fa-zwnj](https://github.com/sajjjadayobi/ParsBigBird) |  78M   |        85.43%       |     **94.05%**    |     **73.34%**    |
+|       [bert-base-fa](https://github.com/hooshvare/parsbert)      |  118M  |      **87.98%**     |       93.65%      |       70.06%      |
+
+- Despite being as big as distill-bert, the model performs equally well as ParsBert and is much better on PersianQA which requires much more context
+- This evaluation was based on `max_lentgh=2048` (It can be changed up to 4096)
+
+
+## How to use❓
+
+### As Contextualized Word Embedding 
+```python
+from transformers import BigBirdModel, AutoTokenizer
+
+MODEL_NAME = "SajjadAyoubi/distil-bigbird-fa-zwnj"
+# by default its in `block_sparse` block_size=32
+model = BigBirdModel.from_pretrained(MODEL_NAME, block_size=32)
+# you can use full attention like the following: use this when input isn't longer than 512
+model = BigBirdModel.from_pretrained(MODEL_NAME, attention_type="original_full")
+
+text = "😃 امیدوارم مدل بدردبخوری باشه چون خیلی طول کشید تا ترین بشه"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+tokens = tokenizer(text, return_tensors='pt')
+output = model(**tokens) # contextualized embedding
+```
+
+### As Fill Blank
+```python
+from transformers import pipeline
+
+MODEL_NAME = 'SajjadAyoubi/distil-bigbird-fa-zwnj'
+fill = pipeline('fill-mask', model=MODEL_NAME, tokenizer=MODEL_NAME)
+results = fill('تهران پایتخت [MASK] است.')
+print(results[0]['token_str'])
+>>> 'ایران'
+```
+
+## Pretraining details: 🔭
+This model was pretrained using a masked language model (MLM) objective on the Persian section of the Oscar dataset. Following the original BERT training, 15% of tokens were masked. This was first described in this [paper](https://arxiv.org/abs/2007.14062) and released in this [repository](https://github.com/google-research/bigbird). Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token. Model is warm started from `distilbert-fa`’s [checkpoint](https://huggingface.co/HooshvareLab/distilbert-fa-zwnj-base). 
+- For more details, you can take a look at config.json at the model card in 🤗 Model Hub
+
+## Fine Tuning Recommendations: 🐤
+Due to the model's memory requirements, `gradient_checkpointing` and `gradient_accumulation` should be used to maintain a reasonable batch size. Considering this model isn't really big, it's a good idea to first fine-tune it on your dataset using Masked LM objective (also called intermediate fine-tuning) before implementing the main task. In block_sparse mode, it doesn't matter how many tokens are input. It just attends to 256 tokens. Furthermore, original_full should be used up to 512 sequence lengths (instead of block sparse).
+
+### Fine Tuning Examples 👷‍♂️👷‍♀️
+
+| Dataset                               | Fine Tuning Example                                          |
+| ------------------------------------- | ------------------------------------------------------------ |
+| Digikala Magazine Text Classification | <a href="https://colab.research.google.com/github/sajjjadayobi/PersianQA/blob/main/notebooks/Demo.ipynb"><img src="https://img.shields.io/static/v1?label=Colab&message=Fine-tuning Example&logo=Google%20Colab&color=f9ab00"></a> |
+
+
+## Contact us: 🤝
+If you have a technical question regarding the model, pretraining, code or publication, please create an issue in the repository. This is the fastest way to reach us.
+
+## Citation: ↩️
+we didn't publish any papers on the work. However, if you did, please cite us properly with an entry like one below.
+```bibtex
+@misc{ParsBigBird,
+  author          = {Ayoubi, Sajjad},
+  title           = {ParsBigBird: Persian Bert For Long-Range Sequences},
+  year            = 2021,
+  publisher       = {GitHub},
+  journal         = {GitHub repository},
+  howpublished    = {\url{https://github.com/SajjjadAyobi/ParsBigBird}},
+}
+```