|
--- |
|
license: mit |
|
base_model: FacebookAI/xlm-roberta-base |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: xlm-roberta-base_lr5e-06_seed42_basic_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_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: 0.0269 |
|
- Spearman Corr: 0.8037 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:-------------:| |
|
| 0.118 | 2.33 | 200 | 0.0131 | 0.7608 | |
|
| 0.0306 | 4.65 | 400 | 0.0163 | 0.7890 | |
|
| 0.0248 | 6.98 | 600 | 0.0188 | 0.7988 | |
|
| 0.0216 | 9.3 | 800 | 0.0272 | 0.7926 | |
|
| 0.0201 | 11.63 | 1000 | 0.0228 | 0.8040 | |
|
| 0.0171 | 13.95 | 1200 | 0.0208 | 0.8059 | |
|
| 0.0158 | 16.28 | 1400 | 0.0271 | 0.8070 | |
|
| 0.0145 | 18.6 | 1600 | 0.0236 | 0.8062 | |
|
| 0.0139 | 20.93 | 1800 | 0.0279 | 0.8020 | |
|
| 0.0132 | 23.26 | 2000 | 0.0243 | 0.8010 | |
|
| 0.013 | 25.58 | 2200 | 0.0269 | 0.8037 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.37.2 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.17.0 |
|
- Tokenizers 0.15.2 |
|
|