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---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta-base-outputs
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-outputs
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: 0.5836
- Accuracy: 0.6636
- F1: 0.6948
- Precision: 0.6409
- Recall: 0.7587
## 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: 1e-06
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6984 | 0.1778 | 1000 | 0.6931 | 0.5072 | 0.4296 | 0.5167 | 0.3677 |
| 0.6952 | 0.3556 | 2000 | 0.6932 | 0.4956 | 0.0032 | 0.6667 | 0.0016 |
| 0.6931 | 0.5333 | 3000 | 0.6922 | 0.5314 | 0.3417 | 0.5874 | 0.2409 |
| 0.6927 | 0.7111 | 4000 | 0.6901 | 0.5272 | 0.6625 | 0.5179 | 0.9192 |
| 0.6883 | 0.8889 | 5000 | 0.6792 | 0.5714 | 0.6346 | 0.5570 | 0.7373 |
| 0.6756 | 1.0667 | 6000 | 0.6521 | 0.6114 | 0.5702 | 0.6455 | 0.5107 |
| 0.6476 | 1.2444 | 7000 | 0.6317 | 0.627 | 0.6909 | 0.5939 | 0.8257 |
| 0.6278 | 1.4222 | 8000 | 0.6058 | 0.6474 | 0.6799 | 0.6276 | 0.7417 |
| 0.6134 | 1.6 | 9000 | 0.5959 | 0.6564 | 0.6909 | 0.6328 | 0.7607 |
| 0.6119 | 1.7778 | 10000 | 0.5870 | 0.6618 | 0.6933 | 0.6393 | 0.7571 |
| 0.6033 | 1.9556 | 11000 | 0.5836 | 0.6636 | 0.6948 | 0.6409 | 0.7587 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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