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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fd1e23881a33ca0550f42ff85064ea61212f205de9ec4215735868bceca04cbf
- Size of remote file:
- 439 MB
- SHA256:
- 037164e15fe4eee630f50915b5b5a2a13b22b8deca1e37f6487a30fc4e0618c1
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