Instructions to use Nubletz/BERT-Simplification-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nubletz/BERT-Simplification-Embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Nubletz/BERT-Simplification-Embedding")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Nubletz/BERT-Simplification-Embedding") model = AutoModel.from_pretrained("Nubletz/BERT-Simplification-Embedding") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e56f4a300bbff4e969817f3b95b79b2a2da124b767dad374d9b43fa47519fb4f
- Size of remote file:
- 438 MB
- SHA256:
- 8e47716a979def3ee4331621abb95a2a07619cf6428ca798c051201cbbc0ff89
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.