Fill-Mask
Transformers
Safetensors
English
tolm
babylm
strict-small
masked-language-modeling
factorized
ltg-bert
custom_code
Instructions to use miguelcsx/factorized-natural-dense with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use miguelcsx/factorized-natural-dense with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="miguelcsx/factorized-natural-dense", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("miguelcsx/factorized-natural-dense", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from miguelcsx/factorized-natural-dense: direct link, hf CLI and curl.
- Browser
- Download file 1.13 MB
-
https://huggingface.co/miguelcsx/factorized-natural-dense/resolve/main/tokenizer.json
- Command line
-
hf download hf://miguelcsx/factorized-natural-dense/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/miguelcsx/factorized-natural-dense/resolve/main/tokenizer.json
1.13 MB
File too large to display, you can check the raw version instead.