Model Card for harpertoken/harpertokenConvAI-finetuned
This model is a fine-tuned version of harpertoken/harpertokenConvAI, a DistilBERT-based question answering model, trained on a subset of the SQuAD dataset.
Model Details
Model Description
This is a fine-tuned question answering model based on DistilBERT, optimized for extractive QA tasks. It has been trained on a small subset of the SQuAD dataset to demonstrate fine-tuning capabilities in a CI environment.
- Developed by: bniladridas
- Model type: DistilBERT for Question Answering
- Language(s) (NLP): English
- License: MIT
- Finetuned from model: harpertoken/harpertokenConvAI
Model Sources
Uses
Direct Use
This model can be used directly for question answering on passages similar to SQuAD. Provide a question and context, and it will predict the answer span.
Downstream Use
Can be further fine-tuned on domain-specific data for improved performance.
Out-of-Scope Use
Not suitable for non-English text, generative tasks, or domains outside of factual QA.
Bias, Risks, and Limitations
Trained on a limited SQuAD subset, may exhibit biases from the dataset. Performance may degrade on out-of-domain questions.
Recommendations
Evaluate on your specific data and consider additional fine-tuning for production use.
How to Get Started with the Model
1from transformers import pipeline
2
3qa = pipeline("question-answering", model="harpertoken/harpertokenConvAI-finetuned")
4result = qa(question="What is the capital of France?", context="France is a country in Europe. Paris is the capital.")
5print(result)
Training Details
Training Data
Subset of SQuAD 1.1 dataset (approximately 1000 examples).
Training Procedure
Training Hyperparameters
- Training regime: fp32
- Epochs: 1
- Batch size: 1
- Learning rate: 2e-5
Speeds, Sizes, Times
Trained in CI environment, minimal time due to small dataset.
Evaluation
Testing Data, Factors & Metrics
Testing Data
SQuAD validation set subset.
Metrics
F1 score, Exact Match.
Results
Basic evaluation on sample questions.
Environmental Impact
Minimal impact due to small-scale training in CI.
- Hardware Type: GitHub Actions runners
- Carbon Emitted: Negligible
Technical Specifications
Model Architecture and Objective
DistilBERT encoder with QA head for span prediction.
Compute Infrastructure
GitHub Actions Ubuntu runners.
Citation
If you use this model, please cite the original DistilBERT and SQuAD papers.
Model Card Contact
bniladridas
