Text Classification
Transformers
TensorBoard
Safetensors
English
deberta-v2
cross-encoder
sequence-classification
text-embeddings-inference
Instructions to use xpmir/cross-encoder-DeBERTav3-Hinge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xpmir/cross-encoder-DeBERTav3-Hinge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xpmir/cross-encoder-DeBERTav3-Hinge")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xpmir/cross-encoder-DeBERTav3-Hinge") model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-DeBERTav3-Hinge") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_prefix_space": true, | |
| "backend": "tokenizers", | |
| "bos_token": "[CLS]", | |
| "cls_token": "[CLS]", | |
| "do_lower_case": false, | |
| "eos_token": "[SEP]", | |
| "extra_special_tokens": [ | |
| "[PAD]", | |
| "[CLS]", | |
| "[SEP]" | |
| ], | |
| "is_local": false, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "split_by_punct": false, | |
| "tokenizer_class": "DebertaV2Tokenizer", | |
| "unk_id": 3, | |
| "unk_token": "[UNK]", | |
| "vocab_type": "spm" | |
| } | |