---
library_name: transformers
license: mit
base_model: intfloat/multilingual-e5-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: gec-score-model
  results: []
datasets:
- peterua/OmniGEC-ModelTraining
language:
- uk
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# gec-score-model

This model is a fine-tuned version of [intfloat/multilingual-e5-base](https://huggingface.co/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
- train_batch_size: 32
- eval_batch_size: 128
- seed: 0
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 256
- total_eval_batch_size: 1024
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 60

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1 Macro | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:--------:|:--------:|
| No log        | 0       | 0    | 0.2963          | 0.5503    | 0.5013 | 0.3409   | 0.5013   |
| 0.2297        | 7.4074  | 400  | 0.2287          | 0.6545    | 0.6336 | 0.6208   | 0.6336   |
| 0.2051        | 14.8148 | 800  | 0.2041          | 0.6722    | 0.6660 | 0.6630   | 0.6660   |
| 0.1957        | 22.2222 | 1200 | 0.1982          | 0.6889    | 0.6885 | 0.6883   | 0.6885   |
| 0.1939        | 29.6296 | 1600 | 0.1963          | 0.6971    | 0.6964 | 0.6962   | 0.6964   |
| 0.1916        | 37.0370 | 2000 | 0.1946          | 0.7005    | 0.7004 | 0.7004   | 0.7004   |
| 0.1907        | 44.4444 | 2400 | 0.1944          | 0.7018    | 0.7017 | 0.7017   | 0.7017   |
| 0.1888        | 51.8519 | 2800 | 0.1944          | 0.6990    | 0.6984 | 0.6982   | 0.6984   |
| 0.1884        | 59.2593 | 3200 | 0.1941          | 0.7031    | 0.7030 | 0.7030   | 0.7030   |


### Framework versions

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