π€ Intent Detection using Fine-Tuned BERT
This project utilizes a fine-tuned BERT model (bert-base-uncased
) for intent classification tasks. It is an encoder-only transformer designed to detect user intents from text inputs (e.g., chatbot queries) and classify them into predefined categories such as banking
, travel
, finance
, and more.
The model is trained on the CLINC150 (clinc_oos) dataset and evaluated using accuracy as the primary metric.
π Dataset --> CLINC150
The project uses the CLINC150 dataset, a benchmark dataset for intent classification in task-oriented dialogue systems.
π§Ύ Dataset Overview
- Total intents: 150 unique user intents
- Domains: 10 real-world domains (e.g., banking, travel, weather, small talk)
- Examples: ~22,500 utterances
- Language: English
- Out-of-scope (OOS): Includes OOS examples to test robustness
π¦ Source
- Official repo: clinc/oos-eval
- Hugging Face:
clinc_oos
π Example
Request: "I want to book a flight"
Response: "book_flight"
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Model tree for SaherMuhamed/bert-intention-classifier
Base model
google-bert/bert-base-uncased