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
-
customize chunk_size in score function
Liyan0609efa05
-
add span highlight (rogue) for neg chunk
Liyan063fe4664 -
update retrieval and doc display ranking
Liyan063fbb656 -
add TFIDF ranking
Liyan06113a57e -
web retrieval update
Liyan06c191acc -
update web retrieval quality
Liyan064ec6f2d -
add entity highlight
Liyan062d158d3 -
span highlight for web search
Liyan0693e9112 -
add span highlight
Liyan060677600
-
generator is not json seralizable; go back to previous version
Liyan0698d958b -
add server-sent events (SSE) function
Liyan06e9622b9 -
add server-sent events (SSE) function
Liyan069695d05 -
address version incompatible issue
Liyan061de34ec -
add progress streaming function
Liyan0666fde05 -
add web search
Liyan061104bf8 -
return ranked docs and scores
Liyan06a496016
-
input format debug
Liyan06ac1a8a5 -
inout format debug
Liyan0619d69d5 -
add customized handler
Liyan063201a95