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---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: pretrain_model
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. -->
# pretrain_model
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: 0.6196
- Precision: 0.6607
- Recall: 0.6589
- F1: 0.6598
- Accuracy: 0.6575
## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6965 | 0.1377 | 500 | 0.6910 | 0.526 | 1.0 | 0.6894 | 0.526 |
| 0.6963 | 0.2755 | 1000 | 0.6921 | 0.526 | 1.0 | 0.6894 | 0.526 |
| 0.6957 | 0.4132 | 1500 | 0.6666 | 0.6154 | 0.7300 | 0.6678 | 0.618 |
| 0.6914 | 0.5510 | 2000 | 0.6834 | 0.7069 | 0.4677 | 0.5629 | 0.618 |
| 0.6768 | 0.6887 | 2500 | 0.6838 | 0.6412 | 0.6388 | 0.64 | 0.622 |
| 0.6786 | 0.8264 | 3000 | 0.6539 | 0.7273 | 0.4259 | 0.5372 | 0.614 |
| 0.663 | 0.9642 | 3500 | 0.6743 | 0.6560 | 0.5437 | 0.5946 | 0.61 |
| 0.6564 | 1.1019 | 4000 | 0.6381 | 0.6763 | 0.6198 | 0.6468 | 0.644 |
| 0.6468 | 1.2397 | 4500 | 0.6010 | 0.6613 | 0.7871 | 0.7188 | 0.676 |
| 0.6275 | 1.3774 | 5000 | 0.6103 | 0.7246 | 0.5703 | 0.6383 | 0.66 |
| 0.6275 | 1.5152 | 5500 | 0.6018 | 0.7311 | 0.5894 | 0.6526 | 0.67 |
| 0.6141 | 1.6529 | 6000 | 0.5947 | 0.7269 | 0.6578 | 0.6906 | 0.69 |
| 0.617 | 1.7906 | 6500 | 0.5872 | 0.7165 | 0.6920 | 0.7041 | 0.694 |
| 0.6059 | 1.9284 | 7000 | 0.5816 | 0.7227 | 0.7034 | 0.7129 | 0.702 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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