Feature Extraction
Transformers
PyTorch
TensorBoard
Safetensors
French
deberta-v2
deberta
deberta-v3
text-embeddings-inference
Instructions to use almanach/camemberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use almanach/camemberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="almanach/camemberta-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("almanach/camemberta-base") model = AutoModel.from_pretrained("almanach/camemberta-base") - Notebooks
- Google Colab
- Kaggle
File size: 453 Bytes
f460d39 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"do_lower_case": false,
"bos_token": "[CLS]",
"eos_token": "[SEP]",
"unk_token": "[UNK]",
"sep_token": "[SEP]",
"pad_token": "[PAD]",
"cls_token": "[CLS]",
"mask_token": "[MASK]",
"split_by_punct": false,
"special_tokens_map_file": null,
"name_or_path": "vocab/camembert-deberta/",
"sp_model_kwargs": {},
"tokenizer_file": null,
"tokenizer_class": "DebertaV2Tokenizer",
"vocab_type": "spm"
} |