Instructions to use jbdaniel/bert-large-uncased-finetuned-bert-large-uncase-p2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jbdaniel/bert-large-uncased-finetuned-bert-large-uncase-p2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jbdaniel/bert-large-uncased-finetuned-bert-large-uncase-p2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jbdaniel/bert-large-uncased-finetuned-bert-large-uncase-p2") model = AutoModelForMaskedLM.from_pretrained("jbdaniel/bert-large-uncased-finetuned-bert-large-uncase-p2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e420b0af8fe3c5fb7ee4254207ff778c16eef471ce5411b95cdc64a485880e61
- Size of remote file:
- 3.45 kB
- SHA256:
- f50409bdcb030d2f9ab6eb2e51f4bb38b7736b793922f4aa01f97d197cfd8fb0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.