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:
- 347226b8711006c6488b0f37edb7f496b52463834ac1d7ed55c1502dbc1c67ff
- Size of remote file:
- 4.73 kB
- SHA256:
- ebd4712b6f7be63f1a56373c46f0e10fff7be22c21887abdee4cba83e4376d58
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.