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:
- 981262a4b3192126ba54b403870d7ca3ee544ac31a0bd538ea833fc66541ae01
- Size of remote file:
- 3.52 kB
- SHA256:
- d7a1fe38c61b96d2ecb3a62f941e39e9e1097786c2c1fbe84bb183d256ee57e5
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