README.md

metadata

license: mit
language:
  - 'no'
  - nn
  - da
  - sv
  - en

Scandinavian Education Classifier Snowflake

!!! We recomment using our bert-based model instead for production

Trained using code from: [CosmoPedia)[]https://github.com/huggingface/cosmopedia/tree/main/classification], and the nb-bert-base as starting point. The data used in classification is from GlotCC and have been annotated using Gemini 1.5 Flash.

The following command where used for training:

 python train_edu_bert.py --base_model_name="NbAiLab/nb-bert-base" --dataset_name="north/scandinavian-educational-annotations" --target_column="score" --checkpoint_dir="/home/pere/checkpoints/scandinavian_bert/"

Classification Report

Class	Precision	Recall	F1-Score	Support
0	0.76	0.64	0.70	18274
1	0.63	0.76	0.69	23348
2	0.48	0.40	0.43	6621
3	0.57	0.28	0.38	1314
4	0.56	0.06	0.12	433
5	0.00	0.00	0.00	10

Metric	Value
Accuracy	0.65
Macro Avg
- Precision	0.50
- Recall	0.36
- F1-Score	0.38
Weighted Avg
- Precision	0.65
- Recall	0.65
- F1-Score	0.64
Total Support	50000

Confusion Matrix

	Class 0	Class 1	Class 2	Class 3	Class 4	Class 5
Class 0	11725	6460	88	1	0	0
Class 1	3598	17758	1978	14	0	0
Class 2	128	3733	2618	142	0	0
Class 3	6	272	645	369	22	0
Class 4	2	121	161	121	28	0
Class 5	0	2	8	0	0	0

Evaluation Metrics

Metric	Value
Eval Loss	0.3311704695224762
Eval Precision	0.49857140934204414
Eval Recall	0.35718277242555724
Eval F1 Macro	0.38442290605864393
Eval Accuracy	0.64996
Eval Runtime	86.1773
Eval Samples Per Second	580.199
Eval Steps Per Second	4.537
Epoch	19.91

Training Metrics

Metric	Value
Loss	0.318
Grad Norm	0.6617229580879211
Learning Rate	5.119453924914675e-07
Epoch	19.97

Training Runtime

Metric	Value
Train Runtime	19583.1034
Train Samples Per Second	459.58
Train Steps Per Second	1.795
Train Loss	0.341879387194793
Epoch	20.0