Instructions to use orisuchy/Descriptive_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use orisuchy/Descriptive_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="orisuchy/Descriptive_Classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("orisuchy/Descriptive_Classifier") model = AutoModelForSequenceClassification.from_pretrained("orisuchy/Descriptive_Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from orisuchy/Descriptive_Classifier: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/orisuchy/Descriptive_Classifier/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://orisuchy/Descriptive_Classifier/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/orisuchy/Descriptive_Classifier/resolve/main/pytorch_model.bin
504 MB
- Xet hash:
- 68b5bdb75485d1b44dc4e31e0821908da2f609e16944272d0176cf35a9e219a1
- Size of remote file:
- 504 MB
- SHA256:
- fef45ce263e0bcd87ab8b5e322a4cf482c5a581978ba17280fa08bb6588e9af9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.