Instructions to use JuliCSD/BERT-AACText-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JuliCSD/BERT-AACText-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JuliCSD/BERT-AACText-test")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("JuliCSD/BERT-AACText-test") model = AutoModelForMaskedLM.from_pretrained("JuliCSD/BERT-AACText-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 457c9ec99496a13ceb11e4df31ab9ec698c683805e624d98f172fe8e67bcc764
- Size of remote file:
- 438 MB
- SHA256:
- ab8ffe2b8e144df7a7d9c702f236cb18586a53e9b1a52cdb0e85e15a21c1dbb2
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