YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

CoEdIT Base — ONNX

ONNX export of jbochi/coedit-base (250M parameters) with encoder-decoder architecture and KV cache support.

CoEdIT is a T5-based model fine-tuned on the grammarly/coedit dataset for text editing tasks including grammar correction, simplification, coherence, and paraphrasing. This base variant is fine-tuned from google/flan-t5-base.

Converted for use with inference4j, an inference-only AI library for Java.

Original Source

Repository: jbochi/coedit-base
License: Apache 2.0

Usage with inference4j

try (var corrector = CoeditGrammarCorrector.coeditBase().build()) {
    System.out.println(corrector.correct("She don't likes swimming."));
    // She doesn't like swimming.
}

Model Details

Property	Value
Architecture	T5 encoder-decoder (250M parameters)
Base model	google/flan-t5-base
Training data	grammarly/coedit
Task	Grammar correction, text editing
Tokenizer	SentencePiece (32,128 tokens)
Original framework	PyTorch (transformers)
Export method	Hugging Face Optimum (encoder-decoder with KV cache)

License

This model is licensed under the Apache License 2.0. Original model by jbochi, trained on the Grammarly CoEdIT dataset.

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