mansoorhamidzadeh
/

parsbert-persian-QA

@@ -13,196 +13,49 @@ datasets:
 - SajjadAyoubi/persian_qa
 ---
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 - SajjadAyoubi/persian_qa
 ---
 # Model Card for Model ID
+# ParsBERT for Persian Question Answering
+## Model Description
+`mansoorhamidzadeh/parsbert-persian-QA` is a fine-tuned version of the ParsBERT model, specifically adapted for the task of question answering in Persian. ParsBERT is a BERT-based model pre-trained on a large Persian text corpus. This model has been fine-tuned on a Persian QA dataset to provide accurate and contextually relevant answers to questions posed in Persian.
+## Model Architecture
+- **Base Model**: ParsBERT
+- **Task**: Question Answering
+- **Language**: Persian
+- **Number of Parameters**: 110M
+## Intended Use
+This model is intended for use in applications requiring natural language understanding and question answering in Persian, such as:
+- Persian language chatbots
+- Persian information retrieval systems
+- Educational tools for Persian language learners
+## Dataset
+The model was fine-tuned on a Persian QA dataset. The dataset consists of question-answer pairs extracted from various Persian text sources, ensuring a diverse range of topics and contexts.
+## Usage
+To use this model for question answering in Persian, you can load it using the Hugging Face Transformers library. Here’s a quick example:
+```python
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("mansoorhamidzadeh/parsbert-persian-QA")
+model = AutoModelForQuestionAnswering.from_pretrained("mansoorhamidzadeh/parsbert-persian-QA")
+# Create a QA pipeline
+qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)
+# Example usage
+context = "متن زمینه که شامل اطلاعات مرتبط با سوال شما است."
+question = "سوال شما چیست؟"
+result = qa_pipeline(question=question, context=context)
+print(f"Answer: {result['answer']}")