metadata
language: en
license: apache-2.0
library_name: transformers.js
pipeline_tag: token-classification
tags:
- grammatical-error-correction
- gector
- onnx
- transformers.js
GECToR Base 2020 (ONNX)
ONNX quantized version of the original GECToR model from Grammarly for browser-based grammatical error correction with Transformers.js.
Original Model
- Source: Grammarly GECToR
- Paper: GECToR – Grammatical Error Correction: Tag, Not Rewrite (BEA Workshop 2020)
- Architecture: RoBERTa-Base + token classification head
- Parameters: ~125M
Conversion Details
- Format: ONNX
- Quantization: INT8 (dynamic quantization)
- Size: ~125MB
- Converted by: Manual export from PyTorch (AllenNLP format)
How It Works
GECToR uses a token classification approach - instead of generating corrected text, it predicts edit operations for each token:
$KEEP- Keep token unchanged$DELETE- Remove token$REPLACE_word- Replace with specific word$APPEND_word- Append word after token$TRANSFORM_*- Apply transformation (case, verb form, etc.)
The model runs iteratively (typically 2-3 passes) until no more edits are predicted.
Usage with Transformers.js
import { pipeline } from '@huggingface/transformers';
const classifier = await pipeline(
'token-classification',
'YOUR_USERNAME/gector-base-2020',
{ dtype: 'q8' }
);
const result = await classifier('He go to school yesterday.');
// Returns token predictions with edit tags
Performance
Faster than the 2024 version with slightly lower accuracy. Good balance of speed and quality.
License
Apache 2.0 (following original model license)