Text Classification
Transformers
PyTorch
Core ML
Safetensors
English
distilbert
text-embeddings-inference
Instructions to use Falconsai/intent_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Falconsai/intent_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Falconsai/intent_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Falconsai/intent_classification") model = AutoModelForSequenceClassification.from_pretrained("Falconsai/intent_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2bc5c81360fd553408572b387e848a0e9ace5d2b2b6d1445545106bcb38f3a23
- Size of remote file:
- 268 MB
- SHA256:
- 292e786305afd79f04f799a7c6f7756b29f261ff6e944b23c3a540baa24741ba
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