library_name: transformers
tags: []
bert_squad
Pretrained model on context-based Question Answering using the SQuAD dataset. This model is fine-tuned from the BERT architecture for extracting answers from passages.
Model Description
bert_squad is a transformer-based model trained for context-based question answering tasks. It leverages the pretrained BERT architecture and adapts it for extracting precise answers given a question and a related context. This model uses the Stanford Question Answering Dataset (SQuAD), available via Hugging Face datasets, for training and fine-tuning.
The model was trained using free computational resources, demonstrating its accessibility for educational and small-scale research purposes.
- **Developed by: SADAT PARVEJ, RAFIFA BINTE JAHIR
- **Shared by [optional]: SADAT PARVEJ
- **Language(s) (NLP): ENGLISH
- **Finetuned from model [optional]:https://huggingface.co/google-bert/bert-base-uncased
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
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
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]