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---
library_name: transformers
license: cc-by-4.0
base_model: NbAiLab/nb-bert-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: nbbert_indirect_speech
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_indirect_speech
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:
- Accuracy: 0.8555
- Precision: 0.8632
- Recall: 0.8555
- F1: 0.8537
- Loss: 0.6652
## 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: 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Accuracy | Precision | Recall | F1 | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------:|:------:|:------:|:---------------:|
| No log | 1.0 | 13 | 0.5937 | 0.6744 | 0.5937 | 0.4918 | 0.7937 |
| No log | 2.0 | 26 | 0.7974 | 0.8004 | 0.7974 | 0.7938 | 0.5315 |
| No log | 3.0 | 39 | 0.7321 | 0.7927 | 0.7321 | 0.7257 | 0.7020 |
| No log | 4.0 | 52 | 0.7905 | 0.7812 | 0.7905 | 0.7856 | 0.5378 |
| No log | 5.0 | 65 | 0.8130 | 0.8173 | 0.8130 | 0.8093 | 0.5648 |
| No log | 6.0 | 78 | 0.7978 | 0.7880 | 0.7978 | 0.7927 | 0.5450 |
| No log | 7.0 | 91 | 0.8313 | 0.8468 | 0.8313 | 0.8274 | 0.5858 |
| No log | 8.0 | 104 | 0.8313 | 0.8373 | 0.8313 | 0.8272 | 0.5247 |
| No log | 9.0 | 117 | 0.8294 | 0.8499 | 0.8294 | 0.8293 | 0.6528 |
| No log | 10.0 | 130 | 0.8414 | 0.8558 | 0.8414 | 0.8385 | 0.5475 |
| No log | 11.0 | 143 | 0.8472 | 0.8611 | 0.8472 | 0.8472 | 0.5893 |
| No log | 12.0 | 156 | 0.8421 | 0.8518 | 0.8421 | 0.8436 | 0.6162 |
| No log | 13.0 | 169 | 0.8382 | 0.8370 | 0.8382 | 0.8370 | 0.5678 |
| No log | 14.0 | 182 | 0.8502 | 0.8531 | 0.8502 | 0.8479 | 0.5941 |
| No log | 15.0 | 195 | 0.8521 | 0.8666 | 0.8521 | 0.8500 | 0.7085 |
| No log | 16.0 | 208 | 0.8511 | 0.8571 | 0.8511 | 0.8502 | 0.6244 |
| No log | 17.0 | 221 | 0.8548 | 0.8641 | 0.8548 | 0.8526 | 0.6625 |
| No log | 18.0 | 234 | 0.8553 | 0.8646 | 0.8553 | 0.8529 | 0.6697 |
| No log | 18.48 | 240 | 0.8555 | 0.8632 | 0.8555 | 0.8537 | 0.6652 |
### Framework versions
- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Tokenizers 0.21.0
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