Instructions to use alibidaran/intent_recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alibidaran/intent_recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="alibidaran/intent_recognition")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("alibidaran/intent_recognition") model = AutoModelForSequenceClassification.from_pretrained("alibidaran/intent_recognition", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from alibidaran/intent_recognition: direct link, hf CLI and curl.
- Browser
- Download file 1.01 kB
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https://huggingface.co/alibidaran/intent_recognition/resolve/main/README.md
- Command line
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hf download hf://alibidaran/intent_recognition/README.md
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curl -L -o README.md https://huggingface.co/alibidaran/intent_recognition/resolve/main/README.md
1.01 kB
metadata
license: apache-2.0
language:
- en
This model is designed for intent detection, being suitable for designing virtual assistants and chatbots.
labels_name={'greeting': 0, 'goodbye': 1, 'creator': 2, 'name': 3, 'hours': 4, 'number': 5, 'course': 6, 'fees': 7, 'location': 8, 'hostel': 9, 'event': 10, 'document': 11, 'floors': 12, 'syllabus': 13, 'library': 14, 'infrastructure': 15, 'canteen': 16, 'menu': 17, 'placement': 18, 'ithod': 19, 'computerhod': 20, 'extchod': 21, 'principal': 22, 'sem': 23, 'admission': 24, 'scholarship': 25, 'facilities': 26, 'college intake': 27, 'uniform': 28, 'committee': 29, 'random': 30, 'swear': 31, 'vacation': 32, 'sports': 33, 'salutaion': 34, 'task': 35, 'ragging': 36, 'hod': 37}