Instructions to use ronit33/intent-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ronit33/intent-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ronit33/intent-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ronit33/intent-classifier") model = AutoModelForSequenceClassification.from_pretrained("ronit33/intent-classifier") - Notebooks
- Google Colab
- Kaggle
File size: 346 Bytes
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metrics:
- accuracy
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datasets:
- http://kaggle.com/datasets/bitext/training-dataset-for-chatbotsvirtual-assistants
model-index:
- name: Intent Classifier with Deberta
results:
- task:
- name: Text Classification
- type: text-classification
- metrics:
- name: Accuracy
- type: accuracy
- value: 0.996
--- |