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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| metrics: | |
| - accuracy | |
| 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 | |
| --- |