Instructions to use gszabo/base_subtoken20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gszabo/base_subtoken20 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gszabo/base_subtoken20")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gszabo/base_subtoken20") model = AutoModelForMaskedLM.from_pretrained("gszabo/base_subtoken20", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from gszabo/base_subtoken20: direct link, hf CLI and curl.
- Browser
- Download file 78 Bytes
-
https://huggingface.co/gszabo/base_subtoken20/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://gszabo/base_subtoken20/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/gszabo/base_subtoken20/resolve/main/tokenizer_config.json
78 Bytes
| { | |
| "model_max_length": 512, | |
| "tokenizer_class": "PreTrainedTokenizerFast" | |
| } | |