ContextSense-DistilBert-SquadV2-QA

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Model Details

ContextSense-DistilBert-SquadV2-QA

Model Description

ContextSense is a fine-tuned version of distilbert-base-uncased specifically optimized for high-speed Question Answering tasks.

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: [Vishal Roy]
• Funded by [optional]: [Personal Project (Self-funded)]
• Shared by [optional]: [Vishal1095]
• Model type: [Transformer-based Encoder (DistilBERT). Specifically, this is a Question Answering model fine-tuned for extractive QA.]
• Language(s) (NLP): [English (en)]
• Finetuned from model [optional]: [distilbert-base-uncased]

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Uses

• Extracting precise answers from large documents.
• Context-aware information retrieval for AI agents.
• Lightweight deployment: Being a DistilBERT model, it offers a great balance between accuracy and inference speed, making it ideal for local deployment on mid-range GPUs or CPU-bound environments.

Direct Use

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Bias, Risks, and Limitations

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Recommendations

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How to Get Started with the Model

You can use this model directly with the Hugging Face pipeline:

from transformers import pipeline

qa_pipeline = pipeline(
    "question-answering", 
    model="Vishal1095/ContextSense-DistilBert-SquadV2-QA"
)

context = "In 2026, AI practitioners are increasingly focusing on local model deployment to ensure data privacy and reduce latency."
question = "What are AI practitioners focusing on in 2026?"

result = qa_pipeline(question=question, context=context)
print(f"Answer: {result['answer']} (Score: {result['score']:.4f})")

Training Details

• Base Model: DistilBert-Base-Uncased
• Dataset: SQuAD v2.0
• Hardware: Trained locally on NVIDIA RTX 4080 (16GB GDDR6X) cluster.
• Checkpoint: checkpoint-19548 (Selected for the best balance between training loss and validation accuracy).
• Precision: Fine-tuned using float32.

Training Data

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Training Procedure

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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 -->

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Evaluation

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Testing Data, Factors & Metrics

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Summary

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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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Technical Specifications [optional]

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