Fill-Mask
Transformers
PyTorch
Safetensors
gpt_bert
feature-extraction
gpt-bert
babylm
remote-code
custom_code
Instructions to use jumelet/gptbert-eus-250steps-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jumelet/gptbert-eus-250steps-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jumelet/gptbert-eus-250steps-base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jumelet/gptbert-eus-250steps-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from jumelet/gptbert-eus-250steps-base: direct link, hf CLI and curl.
- Browser
- Download file 1.15 MB
-
https://huggingface.co/jumelet/gptbert-eus-250steps-base/resolve/main/tokenizer.json
- Command line
-
hf download hf://jumelet/gptbert-eus-250steps-base/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/jumelet/gptbert-eus-250steps-base/resolve/main/tokenizer.json
1.15 MB
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