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furina_esp-hau-eng_train_spearman_corr
shanhy/furina_esp-hau-eng_train_spearman_corr
furina_esp-hau-eng_train_spearman_corr is a text classification model from shanhy. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- xlm-roberta
- Access
- Public
- Created
- Feb 17, 2024
- Updated
- Feb 17, 2024
- SHA
- 4e856925
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Base models
Model card
From the Hugging Face model README
<!-- 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. -->
furina_esp-hau-eng_train_spearman_corr
This model is a fine-tuned version of yihongLiu/furina on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0304
- Spearman Corr: 0.7239
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: 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 | 0.48 | 200 | 0.0285 | 0.6376 |
| No log | 0.97 | 400 | 0.0271 | 0.6797 |
| No log | 1.45 | 600 | 0.0309 | 0.6979 |
| No log | 1.94 | 800 | 0.0323 | 0.7248 |
| 0.0447 | 2.42 | 1000 | 0.0257 | 0.7313 |
| 0.0447 | 2.91 | 1200 | 0.0286 | 0.7216 |
| 0.0447 | 3.39 | 1400 | 0.0297 | 0.7338 |
| 0.0447 | 3.88 | 1600 | 0.0320 | 0.7319 |
| 0.0214 | 4.36 | 1800 | 0.0239 | 0.7410 |
| 0.0214 | 4.85 | 2000 | 0.0282 | 0.7449 |
| 0.0214 | 5.33 | 2200 | 0.0267 | 0.7409 |
| 0.0214 | 5.82 | 2400 | 0.0343 | 0.7415 |
| 0.0148 | 6.3 | 2600 | 0.0252 | 0.7393 |
| 0.0148 | 6.79 | 2800 | 0.0293 | 0.7380 |
| 0.0148 | 7.27 | 3000 | 0.0291 | 0.7302 |
| 0.0148 | 7.76 | 3200 | 0.0271 | 0.7465 |
| 0.0103 | 8.24 | 3400 | 0.0287 | 0.7250 |
| 0.0103 | 8.73 | 3600 | 0.0296 | 0.7340 |
| 0.0103 | 9.21 | 3800 | 0.0281 | 0.7388 |
| 0.0103 | 9.7 | 4000 | 0.0278 | 0.7365 |
| 0.0076 | 10.18 | 4200 | 0.0347 | 0.7320 |
| 0.0076 | 10.67 | 4400 | 0.0280 | 0.7360 |
| 0.0076 | 11.15 | 4600 | 0.0266 | 0.7312 |
| 0.0076 | 11.64 | 4800 | 0.0304 | 0.7239 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- Created
- Feb 17, 2024
- Updated
- Feb 17, 2024