tootooba
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flan-t5-qa-study-assistant

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 ---
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 ---
 library_name: transformers
+license: apache-2.0
+language:
+- en
+base_model:
+- google/flan-t5-base
 ---
+# Model Card for FLAN-T5 QA Study Assistant
+This model is fine-tuned from the FLAN-T5 model to perform **extractive question-answering** tasks using the SQuAD dataset. It can generate answers based on the given context and question, which makes it useful for educational purposes, personal assistants, or any task requiring context-based question-answering.
 ## Model Details
 ### Model Description
+This is a **question-answering model** fine-tuned on a subset of the SQuAD dataset using the FLAN-T5 model. It is designed to extract answers from a given context based on a corresponding question.
+- **Developed by:** Tooba Javed
+- **Funded by [optional]:** Not funded
+- **Shared by [optional]:** Tooba Javed
+- **Model type:** Extractive Question-Answering Model (based on FLAN-T5)
+- **Language(s) (NLP):** English
+- **License:** Apache-2.0 License
+- **Finetuned from model [optional]:** google/flan-t5-base
 ### Model Sources [optional]
+- **Repository:** [GitHub link to your notebook if applicable]
+- **Paper [optional]:** N/A
+- **Demo [optional]:** [Hugging Face link: https://huggingface.co/tootooba/flan-t5-qa-study-assistant]
 ## Uses
 ### Direct Use
+The model is intended to be used for answering questions based on provided context. It can be used in:
+- **Education**: To help students generate answers from textbooks or lecture notes.
+- **Customer Support**: Answer common questions from provided documentation or user manuals.
+- **Personal Assistants**: Assist users by answering general knowledge questions based on given text.
 ### Downstream Use [optional]
+The model can be further fine-tuned for domain-specific question-answering tasks, such as:
+- Legal documents
+- Medical information
+- Company-specific internal knowledge bases
 ### Out-of-Scope Use
+This model may not work well for tasks that require deep reasoning or understanding beyond surface-level extraction from text. Additionally, it is not designed for tasks like creative writing or opinion generation.
 ## Bias, Risks, and Limitations
+- **Bias**: The model is trained on general knowledge from the SQuAD dataset, which may include biases present in Wikipedia-style articles.
+- **Risks**: Since the model relies on extracting information from provided context, it may return incomplete or misleading answers if the context is ambiguous or lacks the necessary information.
+- **Limitations**: The model cannot handle complex reasoning or multi-hop question-answering effectively.
 ### Recommendations
+Users should ensure that:
+- They provide relevant and accurate context for better answers.
+- They are aware of potential biases from the dataset.
+- The model's limitations are accounted for in critical applications (e.g., legal or medical advice).
 ## How to Get Started with the Model
+Use the code below to load the model and tokenizer for question-answering tasks.
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+model = AutoModelForSeq2SeqLM.from_pretrained("tootooba/flan-t5-qa-study-assistant").to("cuda")
+tokenizer = AutoTokenizer.from_pretrained("tootooba/flan-t5-qa-study-assistant")
+context = "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It was constructed between 1887 and 1889 as the entrance arch for the 1889 World's Fair."
+question = "When was the Eiffel Tower constructed?"
+inputs = tokenizer(question, context, return_tensors="pt", truncation=True, padding=True).to("cuda")
+outputs = model.generate(inputs.input_ids)
+answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print("Answer:", answer)