Instructions to use salmaaidli/Flaubert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use salmaaidli/Flaubert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="salmaaidli/Flaubert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("salmaaidli/Flaubert") model = AutoModelForSequenceClassification.from_pretrained("salmaaidli/Flaubert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from salmaaidli/Flaubert: direct link, hf CLI and curl.
- Browser
- Download file 218 MB
-
https://huggingface.co/salmaaidli/Flaubert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://salmaaidli/Flaubert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/salmaaidli/Flaubert/resolve/main/pytorch_model.bin
218 MB
- Xet hash:
- b488ad1976265ec8a1260b50839e34f00a4ff8b9064f3735382e3ba47489f81e
- Size of remote file:
- 218 MB
- SHA256:
- 1a632419c5caf21b0b7c0d2c7d92cc67414ffb373eafeb73279fccbfab167de8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.