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--- |
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language: |
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- en |
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tags: |
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- question-answering |
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- t5 |
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- compact-model |
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- sgarbi |
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license: apache-2.0 |
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datasets: |
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- squad2 |
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- quac |
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- nq |
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- stanfordnlp/coqa |
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- ibm/duorc |
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- squad_v2 |
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--- |
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# Model Card for sgarbi/t5-compact-qa-gen |
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## Model Description |
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`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. |
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## Intended Use |
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This model is intended for a wide range of question-answering tasks, including but not limited to: |
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- Generating study materials from educational texts. |
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- Enhancing search engines with precise Q&A capabilities. |
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- Supporting content creators in generating FAQs. |
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- Deploying on edge devices for real-time question answering in various applications. |
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## How to Use |
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Here is a simple way to use this model with the Transformers library: |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("sgarbi/t5-compact-qa-gen") |
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model = AutoModelForSeq2SeqLM.from_pretrained("sgarbi/t5-compact-qa-gen") |
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text = "INPUT: <qa_builder_context>Your context here." |
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inputs = tokenizer(text, return_tensors="pt") |
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output = model.generate(inputs["input_ids"]) |
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print(tokenizer.decode(output[0], skip_special_tokens=True)) |
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``` |
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## Training Data |
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The model was trained on the following datasets: |
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SQuAD 2.0: A large collection of question and answer pairs based on Wikipedia articles. |
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QuAC: Question Answering in Context, a dataset for modeling, understanding, and participating in information-seeking dialogues. |
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Natural Questions (NQ): A dataset containing real user questions sourced from Google search. |
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Training Procedure |
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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. |
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## Evaluation Results |
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(Include any evaluation metrics and results here to showcase the model's performance on various benchmarks or tasks.) |
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## Limitations and Bias |
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(Describe any limitations of the model, including potential biases in the training data and areas where the model's performance may be suboptimal.) |
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## Ethical Considerations |
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(Provide guidance on ethical considerations for users of the model, including appropriate and inappropriate uses.) |
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## Citation |
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@misc{sgarbi_t5_compact_qa_gen, |
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author = {Erick Sgarbi}, |
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title = {T5 Compact QA Generator}, |
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year = {2024}, |
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publisher = {Hugging Face}, |
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journal = {Hugging Face Model Hub} |
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} |
