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:
- 978800b7539da2b917f3561798661995f838f4281966735156c60e6021a8d89f
- Size of remote file:
- 533 MB
- SHA256:
- 1cc3aad334c260d1b95d0b55e6d8f2394b2bd899574bd1b3f656bfbdef4ff8b1
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