Instructions to use lytang/MiniCheck-Flan-T5-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lytang/MiniCheck-Flan-T5-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lytang/MiniCheck-Flan-T5-Large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("lytang/MiniCheck-Flan-T5-Large") model = AutoModelForSeq2SeqLM.from_pretrained("lytang/MiniCheck-Flan-T5-Large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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handle small chunk size
Liyan0667a6912 -
customize chunk_size in score function
Liyan0609efa05
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inpit format debug
Liyan06591028c -
input format debug
Liyan06ac1a8a5 -
input format update
Liyan06f2ce59c -
inout format debug
Liyan0619d69d5