Vishal1095/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]

Model Sources [optional]

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• Demo [optional]: [More Information Needed]

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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Downstream Use [optional]

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Bias, Risks, and Limitations

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Recommendations

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

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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Evaluation

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Summary

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Environmental Impact

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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