Instructions to use bookbot/roberta-base-emphasis-onnx-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bookbot/roberta-base-emphasis-onnx-quantized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="bookbot/roberta-base-emphasis-onnx-quantized")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("bookbot/roberta-base-emphasis-onnx-quantized") model = AutoModelForQuestionAnswering.from_pretrained("bookbot/roberta-base-emphasis-onnx-quantized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ef7332ce8bf4760730c9bdb80b29fc8e385b1fad12d2d56679b40b219a7254ec
- Size of remote file:
- 43.2 MB
- SHA256:
- e32b6ceee734f30b0ecc257c11e3a76b3c1ca32ceadeadad8f9758ed97111bd0
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.