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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_amh-hau-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_amh-hau-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: 0.0289
- Spearman Corr: 0.7801
## 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 | 1.55 | 200 | 0.0251 | 0.7275 |
| 0.0824 | 3.1 | 400 | 0.0250 | 0.7584 |
| 0.0309 | 4.65 | 600 | 0.0251 | 0.7713 |
| 0.0251 | 6.2 | 800 | 0.0254 | 0.7824 |
| 0.0251 | 7.75 | 1000 | 0.0271 | 0.7791 |
| 0.0222 | 9.3 | 1200 | 0.0258 | 0.7837 |
| 0.0199 | 10.85 | 1400 | 0.0302 | 0.7791 |
| 0.0182 | 12.4 | 1600 | 0.0244 | 0.7815 |
| 0.0166 | 13.95 | 1800 | 0.0299 | 0.7793 |
| 0.0166 | 15.5 | 2000 | 0.0256 | 0.7818 |
| 0.0154 | 17.05 | 2200 | 0.0275 | 0.7880 |
| 0.0145 | 18.6 | 2400 | 0.0283 | 0.7780 |
| 0.0138 | 20.16 | 2600 | 0.0299 | 0.7810 |
| 0.0138 | 21.71 | 2800 | 0.0298 | 0.7834 |
| 0.013 | 23.26 | 3000 | 0.0279 | 0.7813 |
| 0.0125 | 24.81 | 3200 | 0.0279 | 0.7802 |
| 0.0119 | 26.36 | 3400 | 0.0281 | 0.7790 |
| 0.0117 | 27.91 | 3600 | 0.0289 | 0.7801 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
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