Instructions to use certainstar/Trained-Mul-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use certainstar/Trained-Mul-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="certainstar/Trained-Mul-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("certainstar/Trained-Mul-classification") model = AutoModelForSequenceClassification.from_pretrained("certainstar/Trained-Mul-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from certainstar/Trained-Mul-classification: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/certainstar/Trained-Mul-classification/resolve/main/optimizer.pt
- Command line
-
hf download hf://certainstar/Trained-Mul-classification/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/certainstar/Trained-Mul-classification/resolve/main/optimizer.pt
1.42 GB
- Xet hash:
- 9745390fdeb5d2a203caa531d505d7c53ba981c398d2d07cd00e82df693726e5
- Size of remote file:
- 1.42 GB
- SHA256:
- afd8766b32c28117ab1e9cb56fff58054d24ba23e768a4a1124fe9cf54ee1ed6
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