Create README.md

README.md

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+### How to use
+#### Requirements
+
+Transformers require `transformers` and `sentencepiece`, both of which can be
+installed using `pip`.
+
+```sh
+pip install transformers sentencepiece
+```
+
+#### Pipelines 🚀
+
+In case you are not familiar with Transformers, you can use pipelines instead.
+
+Note that, pipelines can't have _no answer_ for the questions.
+
+```python
+from transformers import pipeline
+
+model_name = "SajjadAyoubi/lm-roberta-large-fa-qa"
+qa_pipeline = pipeline("question-answering", model=model_name, tokenizer=model_name)
+
+text = "سلام من سجاد ایوبی هستم ۲۰ سالمه و به پردازش زبان طبیعی علاقه دارم"
+questions = ["اسمم چیه؟", "چند سالمه؟", "به چی علاقه دارم؟"]
+
+for question in questions:
+    print(qa_pipeline({"context": text, "question": question}))
+
+>>> {'score': 0.4839823544025421, 'start': 8, 'end': 18, 'answer': 'سجاد ایوبی'}
+>>> {'score': 0.3747948706150055, 'start': 24, 'end': 32, 'answer': '۲۰ سالمه'}
+>>> {'score': 0.5945395827293396, 'start': 38, 'end': 55, 'answer': 'پردازش زبان طبیعی'}
+```
+
+#### Manual approach 🔥
+
+Using the Manual approach, it is possible to have _no answer_ with even better
+performance.
+
+- PyTorch
+
+```python
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+from src.utils import AnswerPredictor
+
+model_name = "SajjadAyoubi/lm-roberta-large-fa-qa"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+
+text = "سلام من سجاد ایوبی هستم ۲۰ سالمه و به پردازش زبان طبیعی علاقه دارم"
+questions = ["اسمم چیه؟", "چند سالمه؟", "به چی علاقه دارم؟"]
+
+# this class is from src/utils.py and you can read more about it
+predictor = AnswerPredictor(model, tokenizer, device="cpu", n_best=10)
+preds = predictor(questions, [text] * 3, batch_size=3)
+
+for k, v in preds.items():
+    print(v)
+```
+
+Produces an output such below:
+```
+100%|██████████| 1/1 [00:00<00:00,  3.56it/s]
+{'score': 8.040637016296387, 'text': 'سجاد ایوبی'}
+{'score': 9.901972770690918, 'text': '۲۰'}
+{'score': 12.117212295532227, 'text': 'پردازش زبان طبیعی'}
+```
+
+- TensorFlow 2.X
+
+```python
+from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering
+from src.utils import TFAnswerPredictor
+
+model_name = "SajjadAyoubi/lm-roberta-large-fa-qa"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = TFAutoModelForQuestionAnswering.from_pretrained(model_name)
+
+text = "سلام من سجاد ایوبی هستم ۲۰ سالمه و به پردازش زبان طبیعی علاقه دارم"
+questions = ["اسمم چیه؟", "چند سالمه؟", "به چی علاقه دارم؟"]
+
+# this class is from src/utils.py, you can read more about it
+predictor = TFAnswerPredictor(model, tokenizer, n_best=10)
+preds = predictor(questions, [text] * 3, batch_size=3)
+
+for k, v in preds.items():
+    print(v)
+```
+
+Produces an output such below:
+
+```text
+100%|██████████| 1/1 [00:00<00:00,  3.56it/s]
+{'score': 8.040637016296387, 'text': 'سجاد ایوبی'}
+{'score': 9.901972770690918, 'text': '۲۰'}
+{'score': 12.117212295532227, 'text': 'پردازش زبان طبیعی'}
+```
+
+Or you can access the whole demonstration using [HowToUse iPython Notebook on Google Colab](https://colab.research.google.com/github/sajjjadayobi/PersianQA/blob/main/notebooks/HowToUse.ipynb)