IMPARA QE
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# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gotutiyan/IMPARA-QE")
model = AutoModelForSequenceClassification.from_pretrained("gotutiyan/IMPARA-QE", device_map="auto")Quick Links
A trained QE model for IMPARA, a reference-less performance measure for GEC task.
This model achieves 95.93 for Pearson's correlation and 93.01 for Spearman's, for Grundkiewicz +15's Expected Wins score (Note that bert-base-cased is used for SE model).
You can see the detail in this GitHub repository, e.g. How to use this model.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gotutiyan/IMPARA-QE")