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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Falconsai/intent_classification") model = AutoModelForSequenceClassification.from_pretrained("Falconsai/intent_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Falconsai/intent_classification: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Falconsai/intent_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Falconsai/intent_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Falconsai/intent_classification/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 049eb1a9b9e853d5d8d4316cb59db3d6e61fc21f5905cf52d2267565bfa91c9b
- Size of remote file:
- 268 MB
- SHA256:
- 9827b446f7bbcaca6b56c2a8accecc97264953e9d5d9adaf7c29d6a6dad61f3e
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