Token-level GED
Collection
6 items • Updated
Token Classification
Transformers
Safetensors
English
bert
grammatical error correction
grammatical error detection
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("gotutiyan/token-ged-bert-large-cased-bin")
model = AutoModelForTokenClassification.from_pretrained("gotutiyan/token-ged-bert-large-cased-bin", device_map="auto")Quick Links
Binary and multi-class grammatical error detection models.
The experiment was performed according to Yuan+ 21.
The code and the performance on GEC benchmarks are avaliable from https://github.com/gotutiyan/ged_baselines.
Trained models are distributed for research and educational purposes only.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="gotutiyan/token-ged-bert-large-cased-bin")