Instructions to use Intel/xlnet-base-cased-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/xlnet-base-cased-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Intel/xlnet-base-cased-mrpc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Intel/xlnet-base-cased-mrpc") model = AutoModelForSequenceClassification.from_pretrained("Intel/xlnet-base-cased-mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Intel/xlnet-base-cased-mrpc: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/Intel/xlnet-base-cased-mrpc/resolve/main/training_args.bin
- Command line
-
hf download hf://Intel/xlnet-base-cased-mrpc/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Intel/xlnet-base-cased-mrpc/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- e0b1ea5bdae0317da3990ff4ef11af820b62d2506350ddd920ab584bec90d768
- Size of remote file:
- 3.06 kB
- SHA256:
- b62e5a671aa2a65b27140fbb487c094a58ebe69c2d6d73c0f07e2938b7dd7c1e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.