Instructions to use castorini/LiT5-Score-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use castorini/LiT5-Score-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("castorini/LiT5-Score-base") model = AutoModelForSeq2SeqLM.from_pretrained("castorini/LiT5-Score-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from castorini/LiT5-Score-base: direct link, hf CLI and curl.
- Browser
- Download file 1.39 MB
-
https://huggingface.co/castorini/LiT5-Score-base/resolve/main/tokenizer.json
- Command line
-
hf download hf://castorini/LiT5-Score-base/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/castorini/LiT5-Score-base/resolve/main/tokenizer.json
1.39 MB
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