Zero-Shot Classification
Transformers
PyTorch
Safetensors
English
deberta-v2
text-classification
deberta-v3
deberta-v2`
deberta-mnli
Instructions to use NDugar/deberta-v2-xlarge-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NDugar/deberta-v2-xlarge-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="NDugar/deberta-v2-xlarge-mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NDugar/deberta-v2-xlarge-mnli") model = AutoModelForSequenceClassification.from_pretrained("NDugar/deberta-v2-xlarge-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from NDugar/deberta-v2-xlarge-mnli: direct link, hf CLI and curl.
- Browser
- Download file 419 Bytes
-
https://huggingface.co/NDugar/deberta-v2-xlarge-mnli/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://NDugar/deberta-v2-xlarge-mnli/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/NDugar/deberta-v2-xlarge-mnli/resolve/main/tokenizer_config.json
419 Bytes
| {"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, "sp_model_kwargs": {}, "vocab_type": "spm", "model_max_length": 512, "special_tokens_map_file": null, "tokenizer_file": null, "name_or_path": "microsoft/deberta-v2-xlarge", "tokenizer_class": "DebertaV2Tokenizer"} |