Instructions to use textattack/albert-base-v2-MRPC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/albert-base-v2-MRPC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/albert-base-v2-MRPC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/albert-base-v2-MRPC") model = AutoModelForSequenceClassification.from_pretrained("textattack/albert-base-v2-MRPC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b5c290217c147ab8a258be0a25f9b68e1b9af5481db02662c9a0deaf63fd16b
- Size of remote file:
- 46.7 MB
- SHA256:
- 3db841e641355d4fab5c4fc35586e96a82793059b379154451ccd1d748fbcc6b
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