Instructions to use textattack/albert-base-v2-snli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/albert-base-v2-snli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/albert-base-v2-snli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/albert-base-v2-snli") model = AutoModelForSequenceClassification.from_pretrained("textattack/albert-base-v2-snli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ebec50e3d6fee8567b13507aab1b92df5025a77f6bbe7f50431b12c386abb18
- Size of remote file:
- 46.8 MB
- SHA256:
- f48b6b92ad1980963e066bec03043addd021fbaf79c8ff883c9189427b258310
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