lapa-llm/gec-score-model

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gec-score-model

This model is a fine-tuned version of intfloat/multilingual-e5-base on the peterua/OmniGEC-ModelTraining dataset.

Training script is available here: https://github.com/lapa-llm/lapa-llm/blob/main/pretraining/quality-classifiers/gec_score.py

It achieves the following results on the evaluation set:

• —Loss: 0.1941
• —Precision: 0.7031
• —Recall: 0.7030
• —F1 Macro: 0.7030
• —Accuracy: 0.7030

Model description

This model outputs a score how grammatical correct is the provided text.

Intended uses & limitations

Pretraining data filtering.

Training and evaluation data

Training script is located here: https://github.com/lapa-llm/lapa-llm/blob/main/pretraining/quality-classifiers/gec_score.py

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 3e-05
• —trainbatchsize: 32
• —evalbatchsize: 128
• —seed: 0
• —distributed_type: multi-GPU
• —num_devices: 8
• —totaltrainbatch_size: 256
• —totalevalbatch_size: 1024
• —optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
• —lrschedulertype: cosine
• —lrschedulerwarmup_steps: 500
• —num_epochs: 60

Training results

Framework versions

• —Transformers 4.56.1
• —Pytorch 2.6.0a0+ecf3bae40a.nv25.01
• —Datasets 4.0.0
• —Tokenizers 0.22.0