Instructions to use OpenSemShift/bert-c2-en-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenSemShift/bert-c2-en-de with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="OpenSemShift/bert-c2-en-de")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("OpenSemShift/bert-c2-en-de") model = AutoModelForMaskedLM.from_pretrained("OpenSemShift/bert-c2-en-de", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8fbb9cb85325ccb3eadd5105fabf993d7baf54f27c100b52e2ba3cfa01b7c32
- Size of remote file:
- 1.58 kB
- SHA256:
- 8d15a80b533a22785eb7ca422c939487e3ff8a66b28861c69b6db24a63c9d586
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.