Fill-Mask
Transformers
PyTorch
Safetensors
gpt_bert
feature-extraction
gpt-bert
babylm
remote-code
custom_code
Instructions to use jumelet/gptbert-afr-100steps-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jumelet/gptbert-afr-100steps-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jumelet/gptbert-afr-100steps-small", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jumelet/gptbert-afr-100steps-small", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 319761c87015923b7b50845d60923841003fbc2aba3f6f3931485a2cd36db2e6
- Size of remote file:
- 145 MB
- SHA256:
- 1587e82739e6577aaf91027e9b17b505c7cde4b85cfe24b018bf722a0ec07fe2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.