Instructions to use DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT") model = AutoModelForSequenceClassification.from_pretrained("DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT: direct link, hf CLI and curl.
- Browser
- Download file 2.27 MB
-
https://huggingface.co/DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT/resolve/refs%2Fpr%2F2/tokenizer.json
- Command line
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hf download hf://DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT@refs/pr/2/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT/resolve/refs%2Fpr%2F2/tokenizer.json
2.27 MB
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