Instructions to use newsha/PQuAD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use newsha/PQuAD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="newsha/PQuAD")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("newsha/PQuAD") model = AutoModelForQuestionAnswering.from_pretrained("newsha/PQuAD", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: fa | |
| tags: | |
| - parsbert | |
| - bert | |
| - question-answering | |
| - nlp | |
| license: mit | |
| base_model: hooshvare/parsbert-base-uncased | |
| # PQuAD: Persian Question Answering Model | |
| This model is a fine-tuned version of **[ParsBERT](https://huggingface.co/hooshvare/parsbert-base-uncased)** (state-of-the-art Persian language model) for the task of **Question Answering**. | |
| It was trained on a proprietary Persian QA dataset as part of a BSc thesis at **Amirkabir University of Technology**. | |
| ## Model Details | |
| - **Base Model:** ParsBERT (Hooshvare Lab) | |
| - **Task:** Extractive Question Answering | |
| - **Language:** Persian (Farsi) | |
| - **Framework:** PyTorch & Transformers | |
| ## How to Use | |
| You can use this model directly with the Hugging Face `pipeline`: | |
| ```python | |
| from transformers import pipeline | |
| # Load the pipeline | |
| qa_pipeline = pipeline("question-answering", model="newsha/PQuAD") | |
| context = "دانشگاه صنعتی امیرکبیر یکی از باسابقهترین دانشگاههای فنی ایران است که در سال ۱۳۳۷ در تهران تأسیس شد." | |
| question = "دانشگاه امیرکبیر در چه سالی تأسیس شد؟" | |
| result = qa_pipeline(question=question, context=context) | |
| print(f"Answer: {result['answer']}") | |
| # Output: ۱۳۳۷ |