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---
library_name: transformers
license: cc-by-4.0
base_model: NbAiLab/nb-bert-base
tags:
- generated_from_trainer
model-index:
- name: nbbert_ED
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_ED
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: 0.9955
- F1-score: 0.8361
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1-score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 69 | 0.6947 | 0.4209 |
| No log | 2.0 | 138 | 0.8251 | 0.6436 |
| No log | 3.0 | 207 | 0.6215 | 0.7587 |
| No log | 4.0 | 276 | 0.5942 | 0.7622 |
| No log | 5.0 | 345 | 0.6512 | 0.7622 |
| No log | 6.0 | 414 | 0.5853 | 0.7855 |
| No log | 7.0 | 483 | 1.1781 | 0.6619 |
| 0.4341 | 8.0 | 552 | 0.9684 | 0.7596 |
| 0.4341 | 9.0 | 621 | 0.8108 | 0.7951 |
| 0.4341 | 10.0 | 690 | 0.9732 | 0.7849 |
| 0.4341 | 11.0 | 759 | 0.8429 | 0.8276 |
| 0.4341 | 12.0 | 828 | 1.1912 | 0.7576 |
| 0.4341 | 13.0 | 897 | 1.0208 | 0.8115 |
| 0.4341 | 14.0 | 966 | 0.9234 | 0.8197 |
| 0.1528 | 15.0 | 1035 | 0.8931 | 0.8357 |
| 0.1528 | 16.0 | 1104 | 1.1005 | 0.8025 |
| 0.1528 | 17.0 | 1173 | 0.9808 | 0.8279 |
| 0.1528 | 18.0 | 1242 | 1.0438 | 0.8195 |
| 0.1528 | 19.0 | 1311 | 1.0193 | 0.8197 |
| 0.1528 | 20.0 | 1380 | 0.9955 | 0.8361 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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