NbAiLab / nb-education-quality-evaluator

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Model's Last Updated: November 30 2024
text-classification

Introduction of nb-education-quality-evaluator

Model Details of nb-education-quality-evaluator

NB Education Quality Evaluator

Introduction

This model is designed to rate the quality of Norwegian training corpora based on educational content . It predicts a continuous score (float from 0 to 5), assessing the educational value of Norwegian texts. The model is inspired by the classifiers used in the FineWeb project and is trained mainly on Norwegian content.

Model Architecture

It is trained on top of the nb-bert-base model and utilizes code from CosmoPedia .

Training Data

The dataset used for training is derived from GlotCC and has been annotated using Gemini 1.5 Flash.

Purpose

The performance of large language models (LLMs) heavily depends on the quality and size of their pretraining datasets. This regressor aims to assess and enhance the educational value of Norwegian textual data, contributing to better-performing Norwegian LLMs.

This model is part of a pair; the other is the NB Linguistic Quality Regressor , which focuses on linguistic quality.

Using the Model

For convenience we also provide the run_regressor_bert.py script. This is also based on run_edu_bert.py from Cosmopedia. You can modify this script to annotate HuggingFace datasets directly. Cosmopedia also provides slurm-scripts here. We have not included these since we have had the opportunity to test them.

Training and Evaluation Procedure

The following command where used for training. Please note that train_regressor_bert.py has a few minor changes to the original train_edu_bert.py :

 python train_regressor_bert.py --base_model_name="NbAiLab/nb-bert-base" --dataset_name="user/educational-annotations" --target_column="score" --checkpoint_dir="/home/user/checkpoints/scandinavian_bert/"

The following script where used for evaluation.

 python eval_regressor_bert.py --checkpoint_dir="/home/user/checkpoints/scandinavian_bert/final/" --dataset_name="user/educational-annotations"
Classification Report
Class Precision Recall F1-Score Support
0 0.78 0.70 0.74 18274
1 0.67 0.75 0.71 23348
2 0.49 0.47 0.48 6621
3 0.47 0.26 0.33 1314
4 0.60 0.07 0.12 433
5 0.00 0.00 0.00 10
Metric Value
Accuracy 0.68
Macro Avg
- Precision 0.50
- Recall 0.38
- F1-Score 0.40
Weighted Avg
- Precision 0.68
- Recall 0.68
- F1-Score 0.67
Total Support 50000
Confusion Matrix
Class 0 Class 1 Class 2 Class 3 Class 4 Class 5
Class 0 12873 5327 74 0 0 0
Class 1 3486 17582 2238 41 1 0
Class 2 75 3244 3105 197 0 0
Class 3 5 206 746 338 19 0
Class 4 0 45 217 140 30 1
Class 5 0 1 8 1 0 0
Evaluation Metrics
Metric Value
Eval Loss 0.2926119863986969
Eval Precision 0.5010686403845288
Eval Recall 0.37549345115259253
Eval F1 Macro 0.39714660593426115
Eval Accuracy 0.67856
Eval Runtime 86.0674
Eval Samples Per Second 580.94
Eval Steps Per Second 4.543
Epoch 19.91
Training Metrics
Metric Value
Loss 0.2803
Grad Norm 0.5055287480354309
Learning Rate 5.119453924914675e-07
Epoch 19.97
Training Runtime
Metric Value
Train Runtime 19555.3448
Train Samples Per Second 460.232
Train Steps Per Second 1.798
Train Loss 0.29856721191276053
Epoch 20.0
Citing & Authors

The model was trained and documentation written by Per Egil Kummervold.

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