Instructions to use microsoft/deberta-v2-xlarge-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/deberta-v2-xlarge-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="microsoft/deberta-v2-xlarge-mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v2-xlarge-mnli") model = AutoModelForSequenceClassification.from_pretrained("microsoft/deberta-v2-xlarge-mnli", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from microsoft/deberta-v2-xlarge-mnli: direct link, hf CLI and curl.
- Browser
- Download file 52 Bytes
-
https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/0f6a42e33518de8fe040aa8dff9da023d98ca808/tokenizer_config.json
- Command line
-
hf download hf://microsoft/deberta-v2-xlarge-mnli@0f6a42e33518de8fe040aa8dff9da023d98ca808/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/0f6a42e33518de8fe040aa8dff9da023d98ca808/tokenizer_config.json
52 Bytes
| { | |
| "do_lower_case": false, | |
| "vocab_type": "spm" | |
| } | |