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
File size: 963 Bytes
1bf77c1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | {
"bos_token": {
"content": "[CLS]",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": true
},
"eos_token": {
"content": "[SEP]",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": true
},
"unk_token": {
"content": "[UNK]",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": true
},
"sep_token": {
"content": "[SEP]",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": true
},
"pad_token": {
"content": "[PAD]",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": true
},
"cls_token": {
"content": "[CLS]",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": true
},
"mask_token": {
"content": "[MASK]",
"single_word": false,
"lstrip": true,
"rstrip": false,
"normalized": true
}
}
|