Instructions to use driftbench/cola_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use driftbench/cola_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="driftbench/cola_base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("driftbench/cola_base") model = AutoModelForSequenceClassification.from_pretrained("driftbench/cola_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b306c5c63ca49bff5458576158e8abd4cf9e5ce3f54166dbade32c5f8816321e
- Size of remote file:
- 3.52 kB
- SHA256:
- d0549122c4931672bdf5ab2ca006ad9cd1a76822bc72df6cc23596597e4591cf
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