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", device_map="auto")# 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:
- 45f13007d7c477844177f4f60ca53126a3272ed1bf532943f8aa61d65bf1d829
- Size of remote file:
- 439 MB
- SHA256:
- 0a5a97420624ac7b2ea0e8186da6b85de8c34b5a50f1d04628c4e0194d763c99
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