Instructions to use textattack/bert-base-uncased-MRPC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-MRPC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-MRPC")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-MRPC") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-MRPC", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from textattack/bert-base-uncased-MRPC: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/textattack/bert-base-uncased-MRPC/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://textattack/bert-base-uncased-MRPC/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/textattack/bert-base-uncased-MRPC/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 388869254f6f10292b3b59136b298c8d1f75f8016d10565176d6f581aae396c9
- 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.