Instructions to use BEE-spoke-data/neobert-100k-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BEE-spoke-data/neobert-100k-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BEE-spoke-data/neobert-100k-test", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("BEE-spoke-data/neobert-100k-test", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e894afe7143b0f92c62005a947281071cbfba2afc25a8d56b4c6793634803e4
- Size of remote file:
- 537 MB
- SHA256:
- d71fc827b1bce4bf6201186eaea64c84a903f567a27b106f43ae2adc59fc250c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.