Instructions to use ModelTC/bert-base-uncased-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/bert-base-uncased-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ModelTC/bert-base-uncased-mrpc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ModelTC/bert-base-uncased-mrpc") model = AutoModelForSequenceClassification.from_pretrained("ModelTC/bert-base-uncased-mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fa43c2168775670a45acb5c96cd71ac0c02e18a31f3ed9fbbcaabc02f81bfe5f
- Size of remote file:
- 438 MB
- SHA256:
- 21a7986a9000bb97d11204644f03ffe41975bc2686fd487052159709fa21c2ad
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.