Instructions to use AlexMaclean/sentence-compression-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlexMaclean/sentence-compression-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AlexMaclean/sentence-compression-roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AlexMaclean/sentence-compression-roberta") model = AutoModelForTokenClassification.from_pretrained("AlexMaclean/sentence-compression-roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 713e7bdbe796ca9f44f53fc72ba5dfe75b42f252ae7b4b11934197d77748c3e1
- Size of remote file:
- 2.8 kB
- SHA256:
- 398072071cd09c0b589df91c4315cf13acc9537e5768ed0638285141ab588006
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.