Zero-Shot Classification
Transformers
PyTorch
Portuguese
deberta-v2
text-classification
deberta-v3
zero-shot
Instructions to use Mel-Iza0/zero-shot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mel-Iza0/zero-shot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Mel-Iza0/zero-shot")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mel-Iza0/zero-shot") model = AutoModelForSequenceClassification.from_pretrained("Mel-Iza0/zero-shot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Mel-Iza0/zero-shot: direct link, hf CLI and curl.
- Browser
- Download file 738 MB
-
https://huggingface.co/Mel-Iza0/zero-shot/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Mel-Iza0/zero-shot/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Mel-Iza0/zero-shot/resolve/main/pytorch_model.bin
738 MB
- Xet hash:
- 5f970579a8e6a1186c1db5db07c6790c78203af53a84efb3411c79e107a9bc04
- Size of remote file:
- 738 MB
- SHA256:
- 40d63a2f93bb373e2fdff37b2b35e8f904600e16d84c1d98130491d66d6c8a8e
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