Instructions to use nlpaueb/sec-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpaueb/sec-bert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nlpaueb/sec-bert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("nlpaueb/sec-bert-base") model = AutoModelForPreTraining.from_pretrained("nlpaueb/sec-bert-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nlpaueb/sec-bert-base: direct link, hf CLI and curl.
- Browser
- Download file 439 MB
-
https://huggingface.co/nlpaueb/sec-bert-base/resolve/refs%2Fpr%2F3/pytorch_model.bin
- Command line
-
hf download hf://nlpaueb/sec-bert-base@refs/pr/3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nlpaueb/sec-bert-base/resolve/refs%2Fpr%2F3/pytorch_model.bin
439 MB
- Xet hash:
- b7faaabf40be0d9fc3ec2314cc3c68bfff3faac8b29cb678ef2ce7d57f007c78
- Size of remote file:
- 439 MB
- SHA256:
- cbc4a6a0a79d20a52e2b5599455fb65e10c9b1a115d895bd38c181e05948051b
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