Instructions to use Delicia/newModelClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Delicia/newModelClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Delicia/newModelClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Delicia/newModelClassification") model = AutoModelForSequenceClassification.from_pretrained("Delicia/newModelClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from Delicia/newModelClassification: direct link, hf CLI and curl.
- Browser
- Download file 2.24 GB
-
https://huggingface.co/Delicia/newModelClassification/resolve/main/model.safetensors
- Command line
-
hf download hf://Delicia/newModelClassification/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Delicia/newModelClassification/resolve/main/model.safetensors
2.24 GB
- Xet hash:
- 9787d6df2a2a79f5f6c564255e8a796c8f8782cf955fffec1157cfdbdf5d3cb7
- Size of remote file:
- 2.24 GB
- SHA256:
- 18250d1586c312a163194acd0369944baa23c316493eaf33f3be80367b1fb889
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