Zero-Shot Classification
Transformers
PyTorch
Safetensors
English
deberta-v2
text-classification
deberta-v1
deberta-mnli
Instructions to use NDugar/v2xl-again-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NDugar/v2xl-again-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="NDugar/v2xl-again-mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NDugar/v2xl-again-mnli") model = AutoModelForSequenceClassification.from_pretrained("NDugar/v2xl-again-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download special_tokens_map.json from NDugar/v2xl-again-mnli: direct link, hf CLI and curl.
- Browser
- Download file 156 Bytes
-
https://huggingface.co/NDugar/v2xl-again-mnli/resolve/refs%2Fpr%2F1/special_tokens_map.json
- Command line
-
hf download hf://NDugar/v2xl-again-mnli@refs/pr/1/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/NDugar/v2xl-again-mnli/resolve/refs%2Fpr%2F1/special_tokens_map.json
156 Bytes
| {"bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"} |