Instructions to use Splend1dchan/XDBERT-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Splend1dchan/XDBERT-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Splend1dchan/XDBERT-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Splend1dchan/XDBERT-base") model = AutoModel.from_pretrained("Splend1dchan/XDBERT-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d75cbb486475b4f9364454fe2776b8be033e16b6c8ef3a2496c700fa7fe0775
- Size of remote file:
- 438 MB
- SHA256:
- 4aece42c44f62a98becdaf84244b588b28026ac701591dd791baeba069c5b991
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