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---
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
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