Zero-Shot Classification
Transformers
PyTorch
English
deberta
text-classification
deberta-v1
deberta-mnli
Instructions to use Narsil/deberta-large-mnli-zero-cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Narsil/deberta-large-mnli-zero-cls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Narsil/deberta-large-mnli-zero-cls")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Narsil/deberta-large-mnli-zero-cls") model = AutoModelForSequenceClassification.from_pretrained("Narsil/deberta-large-mnli-zero-cls", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download bpe_encoder.bin from Narsil/deberta-large-mnli-zero-cls: direct link, hf CLI and curl.
- Browser
- Download file 3.92 MB
-
https://huggingface.co/Narsil/deberta-large-mnli-zero-cls/resolve/main/bpe_encoder.bin
- Command line
-
hf download hf://Narsil/deberta-large-mnli-zero-cls/bpe_encoder.bin
-
curl -L -o bpe_encoder.bin https://huggingface.co/Narsil/deberta-large-mnli-zero-cls/resolve/main/bpe_encoder.bin
3.92 MB
- Xet hash:
- 6393ca7c4f74fc91cefb3897de66371c05d277d9f046d5e0468d8fb63a58a691
- Size of remote file:
- 3.92 MB
- SHA256:
- e7c6f9eecb461c01e09c00656ccf3e27944b9e74bfe29e51632b13d3cd9d6c8e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.