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---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base_ary_loss_0.0001
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_ary_loss_0.0001
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0476
- Spearman Corr: -0.0518
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 0.9 | 200 | 0.0463 | nan |
| No log | 1.8 | 400 | 0.0463 | nan |
| 0.0485 | 2.7 | 600 | 0.0481 | nan |
| 0.0485 | 3.6 | 800 | 0.0470 | nan |
| 0.0488 | 4.5 | 1000 | 0.0472 | -0.0282 |
| 0.0488 | 5.41 | 1200 | 0.0465 | nan |
| 0.0485 | 6.31 | 1400 | 0.0465 | nan |
| 0.0485 | 7.21 | 1600 | 0.0462 | nan |
| 0.0489 | 8.11 | 1800 | 0.0470 | nan |
| 0.0489 | 9.01 | 2000 | 0.0463 | -0.0105 |
| 0.0489 | 9.91 | 2200 | 0.0464 | nan |
| 0.0485 | 10.81 | 2400 | 0.0464 | nan |
| 0.0485 | 11.71 | 2600 | 0.0464 | nan |
| 0.0485 | 12.61 | 2800 | 0.0465 | nan |
| 0.0485 | 13.51 | 3000 | 0.0464 | 0.0548 |
| 0.0485 | 14.41 | 3200 | 0.0476 | -0.0518 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
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