Instructions to use textattack/albert-base-v2-WNLI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/albert-base-v2-WNLI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/albert-base-v2-WNLI")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/albert-base-v2-WNLI") model = AutoModelForSequenceClassification.from_pretrained("textattack/albert-base-v2-WNLI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a3ce9e1948332cb5bac5e05dd1487a5a405358c5b17dde659181bc938d2e817
- Size of remote file:
- 46.7 MB
- SHA256:
- 74387195d81f316c004d7633d125e383e67d4254c70638ea827ac5b96c466c97
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