Instructions to use nlpaueb/sec-bert-shape with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpaueb/sec-bert-shape with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nlpaueb/sec-bert-shape")# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("nlpaueb/sec-bert-shape") model = AutoModelForPreTraining.from_pretrained("nlpaueb/sec-bert-shape", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 11a0813e3fa5a9789d71cdd25ac5917d44acde8d934ace73b919ab0e8b896233
- Size of remote file:
- 439 MB
- SHA256:
- 84e2e91d6e2558588bc35294f92ed817ff6cef092fa5128fc3ae3052f17cfe9b
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