Instructions to use BERRAMOU/camembert-math-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BERRAMOU/camembert-math-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BERRAMOU/camembert-math-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BERRAMOU/camembert-math-classification") model = AutoModelForSequenceClassification.from_pretrained("BERRAMOU/camembert-math-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from BERRAMOU/camembert-math-classification: direct link, hf CLI and curl.
- Browser
- Download file 5.33 kB
-
https://huggingface.co/BERRAMOU/camembert-math-classification/resolve/2098d9858e1e0c2c83f2aa561e386ec2f77e5523/training_args.bin
- Command line
-
hf download hf://BERRAMOU/camembert-math-classification@2098d9858e1e0c2c83f2aa561e386ec2f77e5523/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/BERRAMOU/camembert-math-classification/resolve/2098d9858e1e0c2c83f2aa561e386ec2f77e5523/training_args.bin
5.33 kB
- Xet hash:
- 1a234e3b25d70e47eb907ebcc47242ded5642e00bdf548e9310187c71bc230f8
- Size of remote file:
- 5.33 kB
- SHA256:
- c93c94431ed1b105cf39b8089e751149a67138ab2f457268e1b123bce0d578a9
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