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---
library_name: transformers
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: roberta-base-pr
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-pr
This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1841
- F1 Macro: 0.6097
- Precision: 0.6135
- Recall: 0.6205
- Accuracy: 0.7627
## 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: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | Precision | Recall | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log | 1.0 | 240 | 2.3617 | 0.0348 | 0.1449 | 0.1032 | 0.0749 |
| No log | 2.0 | 480 | 0.8375 | 0.5802 | 0.5865 | 0.6081 | 0.7399 |
| 1.9571 | 3.0 | 720 | 0.8221 | 0.5996 | 0.6040 | 0.6244 | 0.7471 |
| 1.9571 | 4.0 | 960 | 0.8073 | 0.6168 | 0.6096 | 0.6356 | 0.7617 |
| 0.9292 | 5.0 | 1200 | 0.7768 | 0.6273 | 0.6273 | 0.6369 | 0.7742 |
| 0.9292 | 6.0 | 1440 | 0.9650 | 0.6009 | 0.6025 | 0.6211 | 0.7445 |
| 0.5053 | 7.0 | 1680 | 1.0663 | 0.6072 | 0.6218 | 0.6186 | 0.7622 |
| 0.5053 | 8.0 | 1920 | 1.1841 | 0.6097 | 0.6135 | 0.6205 | 0.7627 |
### Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1
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