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:
- 0bd4efa0f9d355968e229bc969ec1bbf9877e33d8fe7ddc29094897e378245ad
- Size of remote file:
- 499 MB
- SHA256:
- f3245e2223a3183fd79a0a541cd0b010e267927521254b64f0d8b94f1677d247
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