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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: 0.6409
- Precision: 0.6385
- Recall: 0.6046
- F1: 0.6211
- Accuracy: 0.6354
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6994 | 0.0061 | 250 | 0.6909 | 0.5946 | 0.1740 | 0.2692 | 0.5296 |
| 0.6935 | 0.0122 | 500 | 0.6461 | 0.6368 | 0.5923 | 0.6138 | 0.6288 |
| 0.6862 | 0.0184 | 750 | 0.6710 | 0.6268 | 0.6416 | 0.6341 | 0.6313 |
| 0.6629 | 0.0245 | 1000 | 0.8414 | 0.5772 | 0.7777 | 0.6626 | 0.6056 |
| 0.6729 | 0.0306 | 1250 | 0.6509 | 0.6373 | 0.5992 | 0.6177 | 0.6306 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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