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")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e471e4a7ec3610ab9d0563a8f417c2a937f7e2255b15d5e7e06eeb751645f0e7
- Size of remote file:
- 3.13 GB
- SHA256:
- 41291881e13c6235ed47149cec903bee9493e45d9d7325587a9fa2e266c526c0
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