Instructions to use nlpaueb/sec-bert-num with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpaueb/sec-bert-num with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nlpaueb/sec-bert-num")# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("nlpaueb/sec-bert-num") model = AutoModelForPreTraining.from_pretrained("nlpaueb/sec-bert-num", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a14e3db378678f7c9bde723befaa58b69a9b8999bbf1a33df9967d59e177b02c
- Size of remote file:
- 533 MB
- SHA256:
- 6cb261121c99853957b9bb6b415a3bca5a1a9be16e6f2a9fa997464a6b707227
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.