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:
- bdb537750bc67bd6e6d964f4e3c64ea00c2e707a940e123148762a6be136cca7
- Size of remote file:
- 1.06 kB
- SHA256:
- e19f798beaeeadae7ccdf6a4cee90ac5db9e7ad25ca686246dd57d9086d1b5ce
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