Feature Extraction
Transformers
PyTorch
TensorFlow
TensorBoard
Safetensors
French
deberta-v2
deberta-v3
debertav2
debertav3
camembert
text-embeddings-inference
Instructions to use almanach/camembertav2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use almanach/camembertav2-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="almanach/camembertav2-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("almanach/camembertav2-base") model = AutoModel.from_pretrained("almanach/camembertav2-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from almanach/camembertav2-base: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/almanach/camembertav2-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://almanach/camembertav2-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/almanach/camembertav2-base/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 58f8b93925672e4d024aae6fc2270b0d53abf92d47f9960ebdff7002fd1c646d
- Size of remote file:
- 443 MB
- SHA256:
- 8c1f382d16f79a998e47059a6367c0d5f9e216526d2758638d58e9f50dd60ef4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.