Text Classification
Transformers
PyTorch
TensorFlow
TensorBoard
French
camembert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use baptiste-pasquier/camembert-allocine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baptiste-pasquier/camembert-allocine with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="baptiste-pasquier/camembert-allocine")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("baptiste-pasquier/camembert-allocine") model = AutoModelForSequenceClassification.from_pretrained("baptiste-pasquier/camembert-allocine", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from baptiste-pasquier/camembert-allocine: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/baptiste-pasquier/camembert-allocine/resolve/main/training_args.bin
- Command line
-
hf download hf://baptiste-pasquier/camembert-allocine/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/baptiste-pasquier/camembert-allocine/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- 9029ae6a1eb5b57cee5f0e2128155cb96d9dab371176dd020efeff051d78e55b
- Size of remote file:
- 3.52 kB
- SHA256:
- 159ca7271c5eb2c3c8bd4de654c07c9b8b179aaf1ef106cd87e9712a62a8e0cc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.