Instructions to use GebeyaTalent/Intent_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GebeyaTalent/Intent_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GebeyaTalent/Intent_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GebeyaTalent/Intent_classification") model = AutoModelForSequenceClassification.from_pretrained("GebeyaTalent/Intent_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from GebeyaTalent/Intent_classification: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/GebeyaTalent/Intent_classification/resolve/main/model.safetensors
- Command line
-
hf download hf://GebeyaTalent/Intent_classification/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/GebeyaTalent/Intent_classification/resolve/main/model.safetensors
438 MB
- Xet hash:
- f0f76c0ca3ec43ea07a570a7bf64d6a3f88bad010ee6693c5d01060f89874843
- Size of remote file:
- 438 MB
- SHA256:
- 87bf8be611e1c88923ef7ced51d3e629890336c90e0559eeab73b6e74c45599d
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