Instructions to use textattack/distilbert-base-cased-CoLA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/distilbert-base-cased-CoLA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/distilbert-base-cased-CoLA")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/distilbert-base-cased-CoLA") model = AutoModelForSequenceClassification.from_pretrained("textattack/distilbert-base-cased-CoLA") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 448ab8f8f028d459366eb2ff1e32725b019adcc98d92cc47bca2df685ac82120
- Size of remote file:
- 263 MB
- SHA256:
- 666fa18b8b997407582f37351572d7b0536e32e7d08f0839b541dc6448228468
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