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raman-ai-369/flan-t5-small-qag

sourceHugging Facemitupdated 2y agoView on Hugging Face
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<!-- Provide a quick summary of what the model is/does. --> The model is finetuned to generate questions for a given context and the answer given the answer is in the context.

Model Details

Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. This model has been finetuned to generate questions from a given context and a relevant answer. It has used LoRA with a rank of 128 and alpha of 256. The dataset used is SquadV2 and it has been modified to best suit the task at hand - Instruction finetuning using Cloze prompts.

  • —Developed by: V L Raman Raj Botta
  • —Model type: QG
  • —Language(s) (NLP): Python
  • —License: MIT
  • —Finetuned from model : google/flan-t5-small

Model Sources [optional]

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Uses

<!-- Generate relevant questions based on context and the answer provided -->

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Downstream Use [optional]

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Bias, Risks, and Limitations

<!-- The model generates irrelavant and sometimes offensive questions give the context. -->

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Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

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Training Details

Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> Squad V2 [More Information Needed]

Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> LoRA , rank r = 128 and alpha = 256

Preprocessing [optional]

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Training Hyperparameters
  • —Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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  • —Hours used: [More Information Needed]
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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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APA:

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Glossary [optional]

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Model Card Authors [optional]

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