README.md

---
language:
- en
license: apache-2.0
tags:
- question-answering
- t5
- compact-model
- sgarbi
datasets:
- squad2
- quac
- nq
- stanfordnlp/coqa
- ibm/duorc
- squad_v2
---

# Model Card for sgarbi/t5-compact-qa-gen

## Model Description
`sgarbi/t5-compact-qa-gen` is a compact T5-based model designed to generate question and answer pairs from a given text. This model has been trained with a focus on efficiency and speed, making it suitable for deployment on devices with limited computational resources, including CPUs. It utilizes a novel data formatting approach for training, which simplifies the parsing process and enhances the model's performance.

## Intended Use
This model is intended for a wide range of question-answering tasks, including but not limited to:
- Generating study materials from educational texts.
- Enhancing search engines with precise Q&A capabilities.
- Supporting content creators in generating FAQs.
- Deploying on edge devices for real-time question answering in various applications.

## How to Use
Here is a simple way to use this model with the Transformers library:

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("sgarbi/t5-compact-qa-gen")
model = AutoModelForSeq2SeqLM.from_pretrained("sgarbi/t5-compact-qa-gen")

text = "INPUT: <qa_builder_context>Your context here."
inputs = tokenizer(text, return_tensors="pt")
output = model.generate(inputs["input_ids"])
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

## Training Data
The model was trained on the following datasets:

SQuAD 2.0: A large collection of question and answer pairs based on Wikipedia articles.
QuAC: Question Answering in Context, a dataset for modeling, understanding, and participating in information-seeking dialogues.
Natural Questions (NQ): A dataset containing real user questions sourced from Google search.
Training Procedure
The model was trained using a novel input and output formatting technique, focusing on generating "shallow" training data for efficient model training. The model's architecture, flan-T5-small, was selected for its balance between performance and computational efficiency. Training involved fine-tuning the model on the specified datasets, utilizing a custom XML-like format for simplifying the data structure.

## Evaluation Results
(Include any evaluation metrics and results here to showcase the model's performance on various benchmarks or tasks.)

## Limitations and Bias
(Describe any limitations of the model, including potential biases in the training data and areas where the model's performance may be suboptimal.)

## Ethical Considerations
(Provide guidance on ethical considerations for users of the model, including appropriate and inappropriate uses.)

## Citation

@misc{sgarbi_t5_compact_qa_gen,
  author = {Erick Sgarbi},
  title = {T5 Compact QA Generator},
  year = {2024},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub}
}