Instructions to use waboucay/camembert-base-finetuned-repnum_wl_3_classes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use waboucay/camembert-base-finetuned-repnum_wl_3_classes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="waboucay/camembert-base-finetuned-repnum_wl_3_classes")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("waboucay/camembert-base-finetuned-repnum_wl_3_classes") model = AutoModelForSequenceClassification.from_pretrained("waboucay/camembert-base-finetuned-repnum_wl_3_classes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from waboucay/camembert-base-finetuned-repnum_wl_3_classes: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/waboucay/camembert-base-finetuned-repnum_wl_3_classes/resolve/main/training_args.bin
- Command line
-
hf download hf://waboucay/camembert-base-finetuned-repnum_wl_3_classes/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/waboucay/camembert-base-finetuned-repnum_wl_3_classes/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- d4a2ba107da54300e9d9bacbce9082c79a1aabc39b295452782f308d257774da
- Size of remote file:
- 3.06 kB
- SHA256:
- 9dca7d8d7af61702d818633b0efd9e21cfd499991c2238112b6444c41b2635d2
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