Instructions to use UBC-NLP/prags2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/prags2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="UBC-NLP/prags2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/prags2") model = AutoModelForMaskedLM.from_pretrained("UBC-NLP/prags2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 63ea4add0f8d8368060bdd2c372d33a7bd17231da14c2c16e76ee1165e163aaa
- Size of remote file:
- 499 MB
- SHA256:
- eb61c033382a0d87a006c6ac3d73928b7ba3235657e056a63856cb578adc224f
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