ASR Question Detection Model
| Property | Value |
|---|---|
| Author | mrsinghania |
| Model Type | Sequence Classification |
| Training Data | 7000+ Interview Samples |
| Hugging Face URL | View Model |
What is asr-question-detection?
The asr-question-detection model is a specialized natural language processing model designed to distinguish between questions and statements in spoken text. It has been specifically trained on more than 7,000 samples from interview settings, making it particularly effective for analyzing transcribed speech and ASR (Automatic Speech Recognition) output.
Implementation Details
The model is built using the Transformers architecture and can be easily implemented using the Hugging Face library. It utilizes AutoTokenizer and AutoModelForSequenceClassification for processing input text and making predictions.
- Built on the Transformers architecture
- Compatible with Hugging Face's ecosystem
- Optimized for spoken language analysis
- Pre-trained on interview-style conversations
Core Capabilities
- Binary classification of text into questions or statements
- Specialized in handling ASR output
- Robust performance on interview-style conversations
- Easy integration with existing NLP pipelines
Frequently Asked Questions
Q: What makes this model unique?
This model specifically focuses on question detection in spoken language contexts, trained on real interview data, making it particularly effective for processing transcribed speech and ASR output.
Q: What are the recommended use cases?
The model is ideal for applications involving interview analysis, conversation processing, automated meeting transcription analysis, and any scenario where distinguishing questions from statements in spoken text is crucial.
