---
library_name: transformers
license: cc-by-4.0
base_model: NbAiLab/nb-bert-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: nb-bert-edu-scorer
  results: []
---

<!-- 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. -->

# nb-bert-edu-scorer

This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7249
- Precision: 0.3908
- Recall: 0.3347
- F1 Macro: 0.3334
- Accuracy: 0.48

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 256
- eval_batch_size: 128
- seed: 0
- 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: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1 Macro | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:--------:|:--------:|
| No log        | 0       | 0    | 2.4712          | 0.0986    | 0.1654 | 0.0877   | 0.3496   |
| 0.7734        | 2.6882  | 1000 | 0.7629          | 0.3988    | 0.3258 | 0.3215   | 0.4652   |
| 0.7602        | 5.3763  | 2000 | 0.7505          | 0.3938    | 0.3319 | 0.3284   | 0.4584   |
| 0.7548        | 8.0645  | 3000 | 0.7345          | 0.3924    | 0.3345 | 0.3319   | 0.4758   |
| 0.731         | 10.7527 | 4000 | 0.7300          | 0.3951    | 0.3359 | 0.3335   | 0.4756   |
| 0.7481        | 13.4409 | 5000 | 0.7274          | 0.3957    | 0.3356 | 0.3337   | 0.4818   |
| 0.7255        | 16.1290 | 6000 | 0.7263          | 0.3910    | 0.3361 | 0.3339   | 0.4754   |
| 0.7371        | 18.8172 | 7000 | 0.7249          | 0.3908    | 0.3347 | 0.3334   | 0.48     |


### Framework versions

- Transformers 4.53.2
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.21.2