Instructions to use EIStakovskii/camembert_base_fluency with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EIStakovskii/camembert_base_fluency with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EIStakovskii/camembert_base_fluency")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EIStakovskii/camembert_base_fluency") model = AutoModelForSequenceClassification.from_pretrained("EIStakovskii/camembert_base_fluency", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from EIStakovskii/camembert_base_fluency: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/EIStakovskii/camembert_base_fluency/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://EIStakovskii/camembert_base_fluency/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/EIStakovskii/camembert_base_fluency/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 2d3c84bca5e96b989ddedad0f235d94abb6a84f6b89ba8065f2cdfa69b8c40b0
- Size of remote file:
- 443 MB
- SHA256:
- 04b8e4fece329de1d4a78121ee7a8b17219a41e7c70c65bbbca634df80e9bc3d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.