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---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-downstream-build_rr
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-downstream-build_rr
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8610
- Precision-macro: 0.6015
- Recall-macro: 0.5642
- Macro-f1: 0.5742
- Precision-micro: 0.7871
- Recall-micro: 0.7871
- Micro-f1: 0.7871
- Accuracy: 0.7871
## 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision-macro | Recall-macro | Macro-f1 | Precision-micro | Recall-micro | Micro-f1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:--------:|
| No log | 1.0 | 124 | 0.9703 | 0.5485 | 0.3447 | 0.3566 | 0.7155 | 0.7155 | 0.7155 | 0.7155 |
| No log | 2.0 | 248 | 0.8005 | 0.5181 | 0.5222 | 0.5080 | 0.7353 | 0.7353 | 0.7353 | 0.7353 |
| No log | 3.0 | 372 | 0.8156 | 0.5626 | 0.5322 | 0.5288 | 0.7454 | 0.7454 | 0.7454 | 0.7454 |
| No log | 4.0 | 496 | 0.7056 | 0.5881 | 0.5197 | 0.5180 | 0.7704 | 0.7704 | 0.7704 | 0.7704 |
| 1.0549 | 5.0 | 620 | 0.7526 | 0.5878 | 0.5906 | 0.5775 | 0.7642 | 0.7642 | 0.7642 | 0.7642 |
| 1.0549 | 6.0 | 744 | 0.7094 | 0.6336 | 0.5395 | 0.5649 | 0.7812 | 0.7812 | 0.7812 | 0.7812 |
| 1.0549 | 7.0 | 868 | 0.7391 | 0.6475 | 0.5339 | 0.5535 | 0.7808 | 0.7808 | 0.7808 | 0.7808 |
| 1.0549 | 8.0 | 992 | 0.7354 | 0.6169 | 0.5756 | 0.5881 | 0.7930 | 0.7930 | 0.7930 | 0.7930 |
| 0.545 | 9.0 | 1116 | 0.8143 | 0.5951 | 0.5963 | 0.5928 | 0.7805 | 0.7805 | 0.7805 | 0.7805 |
| 0.545 | 10.0 | 1240 | 0.8352 | 0.6029 | 0.5915 | 0.5918 | 0.7794 | 0.7794 | 0.7794 | 0.7794 |
| 0.545 | 11.0 | 1364 | 0.8610 | 0.6015 | 0.5642 | 0.5742 | 0.7871 | 0.7871 | 0.7871 | 0.7871 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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