FLAN-T5 for StrategyQA
This repository contains a fine-tuned version of the FLAN-T5 model for the StrategyQA dataset. The model is trained to perform multi-step reasoning and answer complex multi-choice questions, leveraging the knowledge stored in external resources.

Model Overview
FLAN-T5 (Fine-tuned Language Agnostic T5) is a variant of T5 (Text-to-Text Transfer Transformer) that has been fine-tuned on a wide variety of tasks to improve its ability to generalize across diverse NLP tasks.

StrategyQA Dataset
StrategyQA is a dataset designed for multi-step reasoning tasks, where each question requires a sequence of logical steps to arrive at the correct answer. It focuses on commonsense reasoning and question answering.

This model has been fine-tuned specifically to answer questions from the StrategyQA dataset by retrieving relevant knowledge and reasoning through it.

Model Description
This model was fine-tuned using the FLAN-T5 architecture on the StrategyQA dataset. The model is designed to answer multi-step reasoning questions by retrieving relevant documents and reasoning over them.

Base Model: FLAN-T5
Fine-tuned Dataset: StrategyQA
Task: Multi-step reasoning for question answering
Retriever Type: Dense retriever (using models like ColBERT or DPR for document retrieval)
Intended Use
This model is designed to be used for multi-step reasoning tasks and can be leveraged for a variety of question-answering tasks where the answer requires more than one step of reasoning. It's particularly useful for domains like commonsense reasoning, knowledge-intensive tasks, and complex decision-making questions.

How to Use
To use the model for inference, follow these steps:

Installation
To install the Hugging Face transformers library and use the model, run the following:

bash
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pip install transformers
Example Code
You can use the model with the following Python code:

python
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from transformers import T5ForConditionalGeneration, T5Tokenizer

# Load the model and tokenizer
model_name = "Azaz666/flan-t5-strategyqa"  # Replace with your model name if necessary
model = T5ForConditionalGeneration.from_pretrained(model_name)
tokenizer = T5Tokenizer.from_pretrained(model_name)

# Example question
question = "What is the capital of France?"

# Tokenize the input question
input_ids = tokenizer.encode("question: " + question, return_tensors="pt")

# Generate the answer
outputs = model.generate(input_ids)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(f"Answer: {answer}")
Model Input/Output
Input: The model expects a question in the format question: {your_question_here}.
Output: The output is a generated answer based on the reasoning over the retrieved knowledge.
Example
Input: "What is the capital of France?"

Output: "Paris"

Model Training Details
The model was fine-tuned using the StrategyQA dataset. Here's a brief overview of the training setup:

Pre-trained Model: flan-t5-large
Training Dataset: StrategyQA
Training Steps: The model was fine-tuned on the StrategyQA dataset, which contains questions requiring multiple reasoning steps.
Evaluation Metrics: The model performance was evaluated based on accuracy (whether the predicted answer matched the ground truth).
Limitations
Context Length: The model is limited by the input size, and longer questions or longer passages might be truncated.
Generalization: While fine-tuned for multi-step reasoning, performance may vary depending on the complexity of the question.
Citation
If you use this model or dataset, please cite the following paper:

StrategyQA: https://arxiv.org/abs/2004.06364
License
This model is licensed under the MIT License.