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:
- a524089714da3a21e75be0c59687411c6cc76f8a689de42302791bb2c1bc7c09
- Size of remote file:
- 46.7 MB
- SHA256:
- f4a6d02f4ecf816f9fd0db58a961aeb3dc891fb3720b2ae8c77fc3ae6411b9de
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