File size: 5,318 Bytes
b8b62e2 98fd064 b8b62e2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
<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(F1) | 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}},
}
```
|