Instructions to use ffgcc/condenser-bert-large-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ffgcc/condenser-bert-large-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ffgcc/condenser-bert-large-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ffgcc/condenser-bert-large-uncased") model = AutoModelForMaskedLM.from_pretrained("ffgcc/condenser-bert-large-uncased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f0c4faafea1ef4373d1eab4bdd0454aa9399fc686a155d9aa274f23428ee011
- Size of remote file:
- 1.34 GB
- SHA256:
- 19fcc4fc1da7989898ba37b16ac46a465bc6822e61626f3dc712d8053167c41e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.