Instructions to use textattack/distilbert-base-uncased-MRPC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/distilbert-base-uncased-MRPC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/distilbert-base-uncased-MRPC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/distilbert-base-uncased-MRPC") model = AutoModelForSequenceClassification.from_pretrained("textattack/distilbert-base-uncased-MRPC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bdd8e0d7b3e919f380a00d002f2132a928ee63b4f266cb0dc1b32ea1f0cbf32c
- Size of remote file:
- 268 MB
- SHA256:
- 32acb69ff467165828cfc6fa23a6f816fab777a5ac3d3c572585c85131d2ad9e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.