Instructions to use textattack/albert-base-v2-RTE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/albert-base-v2-RTE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/albert-base-v2-RTE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/albert-base-v2-RTE") model = AutoModelForSequenceClassification.from_pretrained("textattack/albert-base-v2-RTE") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7752f42956284ef742d4037773374c7f497858d7042c5cf2a18c081bc23d5c1f
- Size of remote file:
- 46.7 MB
- SHA256:
- 3a9cc3c4ae1360650668c793735d1fbd8e17d6cc75f85baf19d9bcbb51e77db6
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