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---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-finetuned-partypredictor
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. -->
# xlm-roberta-base-finetuned-partypredictor
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6783
- Accuracy: 0.2495
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 1.7766 | 0.76 | 5000 | 0.1331 | 1.8909 |
| 1.7572 | 1.52 | 10000 | 0.1331 | 1.7809 |
| 1.7543 | 2.28 | 15000 | 0.1031 | 1.8126 |
| 1.7273 | 3.05 | 20000 | 0.1331 | 1.8048 |
| 1.7435 | 3.81 | 25000 | 0.2675 | 1.7892 |
| 1.7606 | 4.99 | 30000 | 0.3121 | 1.7848 |
| 1.7546 | 5.82 | 35000 | 0.3121 | 1.7737 |
| 1.7417 | 6.65 | 40000 | 0.3121 | 1.7699 |
| 1.7007 | 7.48 | 45000 | 0.1529 | 1.7088 |
| 1.7542 | 7.87 | 50000 | 0.1331 | 1.8058 |
| 1.75 | 8.66 | 55000 | 0.1331 | 1.8347 |
| 1.7505 | 10.05 | 60000 | 1.8079 | 0.1231 |
| 1.7545 | 10.88 | 65000 | 1.7756 | 0.3121 |
| 1.7322 | 11.72 | 70000 | 1.7371 | 0.2707 |
| 1.7082 | 12.56 | 75000 | 1.6886 | 0.2419 |
| 1.7035 | 13.4 | 80000 | 1.6844 | 0.2638 |
| 1.6889 | 14.23 | 85000 | 1.6728 | 0.2525 |
| 1.6779 | 15.07 | 90000 | 1.6737 | 0.2490 |
| 1.6821 | 15.91 | 95000 | 1.6783 | 0.2495 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2
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