Instructions to use insaf/Flaubert-Vanilla_Augmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use insaf/Flaubert-Vanilla_Augmentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="insaf/Flaubert-Vanilla_Augmentation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("insaf/Flaubert-Vanilla_Augmentation") model = AutoModelForSequenceClassification.from_pretrained("insaf/Flaubert-Vanilla_Augmentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from insaf/Flaubert-Vanilla_Augmentation: direct link, hf CLI and curl.
- Browser
- Download file 549 MB
-
https://huggingface.co/insaf/Flaubert-Vanilla_Augmentation/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://insaf/Flaubert-Vanilla_Augmentation/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/insaf/Flaubert-Vanilla_Augmentation/resolve/main/pytorch_model.bin
549 MB
- Xet hash:
- 2145e2484633d0aa4714db1da393b929d3328cbc111cdf47ce953d7b01489e73
- Size of remote file:
- 549 MB
- SHA256:
- d5a315a1a7f87801ba2670ccf140fdbdf182085c300e642b62c1109535ef04fe
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