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---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-multi-head
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. -->
# roberta-base-multi-head
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4882
- Accuracy: 0.5566
- F1 Macro: 0.5333
- F1 Micro: 0.5566
- Precision Macro: 0.5431
- Recall Macro: 0.5389
- Roc Auc: 0.7826
## 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-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- 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
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Micro | Precision Macro | Recall Macro | Roc Auc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:--------:|:---------------:|:------------:|:-------:|
| No log | 0.1304 | 200 | 0.6818 | 0.2672 | 0.1648 | 0.2672 | 0.1218 | 0.2567 | 0.4838 |
| No log | 0.2609 | 400 | 0.6261 | 0.3230 | 0.1221 | 0.3230 | 0.0808 | 0.25 | 0.5071 |
| 0.6589 | 0.3913 | 600 | 0.5625 | 0.3902 | 0.2186 | 0.3902 | 0.2036 | 0.2855 | 0.5948 |
| 0.6589 | 0.5217 | 800 | 0.5461 | 0.4307 | 0.2771 | 0.4307 | 0.3373 | 0.3294 | 0.6677 |
| 0.5528 | 0.6522 | 1000 | 0.5142 | 0.4806 | 0.3522 | 0.4806 | 0.4562 | 0.3832 | 0.7032 |
| 0.5528 | 0.7826 | 1200 | 0.5025 | 0.4966 | 0.3990 | 0.4966 | 0.4866 | 0.4231 | 0.7188 |
| 0.5528 | 0.9130 | 1400 | 0.5006 | 0.4939 | 0.4140 | 0.4939 | 0.4853 | 0.4429 | 0.7312 |
| 0.5111 | 1.0430 | 1600 | 0.4903 | 0.5165 | 0.4163 | 0.5165 | 0.5065 | 0.4369 | 0.7386 |
| 0.5111 | 1.1735 | 1800 | 0.4821 | 0.5267 | 0.4650 | 0.5267 | 0.5003 | 0.4699 | 0.7494 |
| 0.4847 | 1.3039 | 2000 | 0.4803 | 0.5273 | 0.4900 | 0.5273 | 0.5013 | 0.4970 | 0.7582 |
| 0.4847 | 1.4343 | 2200 | 0.4742 | 0.5438 | 0.5020 | 0.5438 | 0.5153 | 0.5015 | 0.7637 |
| 0.4847 | 1.5648 | 2400 | 0.4672 | 0.5476 | 0.4998 | 0.5476 | 0.5270 | 0.4976 | 0.7692 |
| 0.47 | 1.6952 | 2600 | 0.4743 | 0.5396 | 0.4820 | 0.5396 | 0.5346 | 0.4885 | 0.7650 |
| 0.47 | 1.8256 | 2800 | 0.4675 | 0.5512 | 0.5104 | 0.5512 | 0.5282 | 0.5029 | 0.7734 |
| 0.4651 | 1.9561 | 3000 | 0.4671 | 0.5436 | 0.5151 | 0.5436 | 0.5211 | 0.5190 | 0.7747 |
| 0.4651 | 2.0861 | 3200 | 0.4631 | 0.5643 | 0.5269 | 0.5643 | 0.5431 | 0.5209 | 0.7804 |
| 0.4651 | 2.2165 | 3400 | 0.4681 | 0.5445 | 0.5109 | 0.5445 | 0.5359 | 0.5207 | 0.7798 |
| 0.4415 | 2.3469 | 3600 | 0.4695 | 0.5459 | 0.5114 | 0.5459 | 0.5400 | 0.5218 | 0.7801 |
| 0.4415 | 2.4774 | 3800 | 0.4607 | 0.5639 | 0.5358 | 0.5639 | 0.5457 | 0.5335 | 0.7843 |
| 0.4335 | 2.6078 | 4000 | 0.4649 | 0.5525 | 0.5283 | 0.5525 | 0.5354 | 0.5349 | 0.7830 |
| 0.4335 | 2.7382 | 4200 | 0.4676 | 0.5457 | 0.5225 | 0.5457 | 0.5370 | 0.5348 | 0.7854 |
| 0.4335 | 2.8687 | 4400 | 0.4581 | 0.5606 | 0.5272 | 0.5606 | 0.5482 | 0.5250 | 0.7854 |
| 0.4347 | 2.9991 | 4600 | 0.4612 | 0.5650 | 0.5336 | 0.5650 | 0.5425 | 0.5341 | 0.7853 |
| 0.4347 | 3.1291 | 4800 | 0.4654 | 0.5580 | 0.5302 | 0.5580 | 0.5410 | 0.5358 | 0.7856 |
| 0.4048 | 3.2596 | 5000 | 0.4659 | 0.5706 | 0.5452 | 0.5706 | 0.5478 | 0.5463 | 0.7873 |
| 0.4048 | 3.3900 | 5200 | 0.4627 | 0.5692 | 0.5346 | 0.5692 | 0.5538 | 0.5311 | 0.7859 |
| 0.4048 | 3.5204 | 5400 | 0.4733 | 0.5557 | 0.5371 | 0.5557 | 0.5354 | 0.5451 | 0.7858 |
| 0.3995 | 3.6509 | 5600 | 0.4755 | 0.5538 | 0.5267 | 0.5538 | 0.5426 | 0.5308 | 0.7857 |
| 0.3995 | 3.7813 | 5800 | 0.4759 | 0.5467 | 0.5238 | 0.5467 | 0.5383 | 0.5342 | 0.7860 |
| 0.4016 | 3.9117 | 6000 | 0.4698 | 0.5566 | 0.5302 | 0.5566 | 0.5392 | 0.5368 | 0.7859 |
| 0.4016 | 4.0417 | 6200 | 0.4786 | 0.5646 | 0.5389 | 0.5646 | 0.5463 | 0.5369 | 0.7830 |
| 0.4016 | 4.1722 | 6400 | 0.4840 | 0.5636 | 0.5342 | 0.5636 | 0.5409 | 0.5319 | 0.7814 |
| 0.3723 | 4.3026 | 6600 | 0.4760 | 0.5653 | 0.5431 | 0.5653 | 0.5435 | 0.5457 | 0.7855 |
| 0.3723 | 4.4330 | 6800 | 0.4821 | 0.5632 | 0.5340 | 0.5632 | 0.5460 | 0.5348 | 0.7829 |
| 0.3682 | 4.5635 | 7000 | 0.4882 | 0.5566 | 0.5333 | 0.5566 | 0.5431 | 0.5389 | 0.7826 |
### Framework versions
- Transformers 4.53.1
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.2
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