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+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
+Copy
+pip install transformers
+Example Code
+You can use the model with the following Python code:
+
+python
+Copy
+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.