---
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)