metadata
license: apache-2.0
datasets:
- gexai/inquisitiveqg
language:
- en
metrics:
- accuracy
base_model:
- distilbert/distilbert-base-uncased
pipeline_tag: text-classification
Model Details
Text classification model for ambiguity in questions. Classifies questions as ambiguous or clear. Based on distilbert/distilbert-base-uncased.
Example:
"Did he do it?" {'label': 'AMBIG', 'score': 0.9029870629310608}
"Did Peter win the game?" {'label': 'CLEAR', 'score': 0.8900136351585388}
Out-of-Scope Use
The model was only trained to classify single questions. Other kinds of data are not tested.
Training Data
I manually labeled a small part of the inquisitiveqg dataset mixed with a private dataset to train the model to recognize ambiguity in questions. A satisfactory model with 85.5% accuracy was created.
Metrics
"eval_accuracy": 0.8551401869158879,
"eval_loss": 0.3658725619316101,