Instructions to use insaf/Flaubert-DA_no_Augmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use insaf/Flaubert-DA_no_Augmentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="insaf/Flaubert-DA_no_Augmentation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("insaf/Flaubert-DA_no_Augmentation") model = AutoModelForSequenceClassification.from_pretrained("insaf/Flaubert-DA_no_Augmentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from insaf/Flaubert-DA_no_Augmentation: direct link, hf CLI and curl.
- Browser
- Download file 549 MB
-
https://huggingface.co/insaf/Flaubert-DA_no_Augmentation/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://insaf/Flaubert-DA_no_Augmentation/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/insaf/Flaubert-DA_no_Augmentation/resolve/main/pytorch_model.bin
549 MB
- Xet hash:
- 94643fe44bbd30e2ae630134740d7567e29d6437b7ce123c6f3776850e5d4073
- Size of remote file:
- 549 MB
- SHA256:
- b1e0c67648a10818680407d742cad0bf87b78dcce080c1d9616249f1f2094174
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