Update README.md

README.md

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 ---
-license: mit
+language: 
+- en
+tags:
+- question-answering
+- t5
+- compact-model
+- sgarbi
+license: apache-2.0
+datasets:
+- squad2
+- quac
+- nq
 ---
+
+# 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}
+}