---
base_model: NbAiLab/nb-bert-base
library_name: transformers
license: cc-by-4.0
metrics:
- accuracy
- precision
- recall
- f1
tags:
- generated_from_trainer
model-index:
- name: nbbert
  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. -->

# nbbert

This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9305
- Precision: 0.9342
- Recall: 0.9305
- F1: 0.9305
- Loss: 0.4443

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Accuracy | Precision | Recall | F1     | Validation Loss |
|:-------------:|:-------:|:----:|:--------:|:---------:|:------:|:------:|:---------------:|
| No log        | 0.9412  | 8    | 0.4361   | 0.6823    | 0.4361 | 0.3171 | 0.8924          |
| No log        | 2.0     | 17   | 0.8851   | 0.8758    | 0.8851 | 0.8748 | 0.4652          |
| No log        | 2.9412  | 25   | 0.8281   | 0.8333    | 0.8281 | 0.8204 | 0.5819          |
| No log        | 4.0     | 34   | 0.8759   | 0.8922    | 0.8759 | 0.8749 | 0.4312          |
| No log        | 4.9412  | 42   | 0.8550   | 0.8762    | 0.8550 | 0.8548 | 0.5312          |
| No log        | 6.0     | 51   | 0.8944   | 0.8940    | 0.8944 | 0.8941 | 0.3318          |
| No log        | 6.9412  | 59   | 0.9209   | 0.9255    | 0.9209 | 0.9210 | 0.3824          |
| No log        | 8.0     | 68   | 0.9213   | 0.9282    | 0.9213 | 0.9219 | 0.4385          |
| No log        | 8.9412  | 76   | 0.9205   | 0.9226    | 0.9205 | 0.9205 | 0.3830          |
| No log        | 10.0    | 85   | 0.9249   | 0.9309    | 0.9249 | 0.9252 | 0.4137          |
| No log        | 10.9412 | 93   | 0.9269   | 0.9310    | 0.9269 | 0.9270 | 0.4014          |
| No log        | 12.0    | 102  | 0.9293   | 0.9321    | 0.9293 | 0.9293 | 0.3923          |
| No log        | 12.9412 | 110  | 0.9277   | 0.9320    | 0.9277 | 0.9278 | 0.4565          |
| No log        | 14.0    | 119  | 0.9305   | 0.9342    | 0.9305 | 0.9305 | 0.4166          |
| No log        | 14.9412 | 127  | 0.9281   | 0.9325    | 0.9281 | 0.9282 | 0.4512          |
| No log        | 16.0    | 136  | 0.9297   | 0.9336    | 0.9297 | 0.9298 | 0.4465          |
| No log        | 16.9412 | 144  | 0.9273   | 0.9318    | 0.9273 | 0.9274 | 0.4624          |
| No log        | 18.0    | 153  | 0.9277   | 0.9321    | 0.9277 | 0.9278 | 0.4593          |
| No log        | 18.8235 | 160  | 0.9305   | 0.9342    | 0.9305 | 0.9305 | 0.4443          |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1