Instructions to use Sayan01/mrpc-Bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sayan01/mrpc-Bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sayan01/mrpc-Bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sayan01/mrpc-Bert") model = AutoModelForSequenceClassification.from_pretrained("Sayan01/mrpc-Bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bdfdc6f98545d67252da5fe4612959ab567de9eec4df12dd1a93f5c0c01b8a66
- Size of remote file:
- 438 MB
- SHA256:
- 708e9e57d662cac44bc0ce6fc8dea744f16ccb189659a39603deab64c029bde8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.