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---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base_lr5e-06_seed42_basic_original_esp-kin-eng_train
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_lr5e-06_seed42_basic_original_esp-kin-eng_train
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: 0.0293
- Spearman Corr: 0.7251
## 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: 5e-06
- 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 | 1.63 | 200 | 0.0280 | 0.6016 |
| 0.062 | 3.27 | 400 | 0.0275 | 0.6391 |
| 0.0304 | 4.9 | 600 | 0.0293 | 0.6641 |
| 0.0245 | 6.53 | 800 | 0.0266 | 0.6888 |
| 0.0217 | 8.16 | 1000 | 0.0304 | 0.6900 |
| 0.0217 | 9.8 | 1200 | 0.0286 | 0.7016 |
| 0.0198 | 11.43 | 1400 | 0.0304 | 0.7080 |
| 0.0181 | 13.06 | 1600 | 0.0277 | 0.7103 |
| 0.0164 | 14.69 | 1800 | 0.0285 | 0.7086 |
| 0.0154 | 16.33 | 2000 | 0.0286 | 0.7233 |
| 0.0154 | 17.96 | 2200 | 0.0259 | 0.7209 |
| 0.0144 | 19.59 | 2400 | 0.0282 | 0.7160 |
| 0.0137 | 21.22 | 2600 | 0.0300 | 0.7168 |
| 0.0129 | 22.86 | 2800 | 0.0300 | 0.7215 |
| 0.0123 | 24.49 | 3000 | 0.0288 | 0.7262 |
| 0.0124 | 26.12 | 3200 | 0.0285 | 0.7256 |
| 0.0124 | 27.76 | 3400 | 0.0291 | 0.7220 |
| 0.0119 | 29.39 | 3600 | 0.0293 | 0.7251 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
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