Instructions to use driftbench/mrpc_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use driftbench/mrpc_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="driftbench/mrpc_base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("driftbench/mrpc_base") model = AutoModelForSequenceClassification.from_pretrained("driftbench/mrpc_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3b5aab14e08204a98d7e8d3637b6a6fdd1c06b05926daf8a5a66d6681f27b728
- Size of remote file:
- 499 MB
- SHA256:
- 45a67db70350f17fe32c2554c84798dea3d95f2ecb95c139954f8f01e0786d8a
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