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furina_arq_corr_2e-05
Shijia/furina_arq_corr_2e-05
furina_arq_corr_2e-05 is a text classification model from Shijia. 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
- a796731e
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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_arq_corr_2e-05
This model is a fine-tuned version of yihongLiu/furina on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0214
- Spearman Corr: 0.7725
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 | 0.92 | 200 | 0.0215 | 0.7689 |
| No log | 1.84 | 400 | 0.0221 | 0.7652 |
| 0.0024 | 2.76 | 600 | 0.0215 | 0.7726 |
| 0.0024 | 3.69 | 800 | 0.0223 | 0.7706 |
| 0.0022 | 4.61 | 1000 | 0.0218 | 0.7726 |
| 0.0022 | 5.53 | 1200 | 0.0219 | 0.7717 |
| 0.002 | 6.45 | 1400 | 0.0211 | 0.7684 |
| 0.002 | 7.37 | 1600 | 0.0216 | 0.7698 |
| 0.0017 | 8.29 | 1800 | 0.0222 | 0.7721 |
| 0.0017 | 9.22 | 2000 | 0.0228 | 0.7731 |
| 0.0017 | 10.14 | 2200 | 0.0217 | 0.7710 |
| 0.0017 | 11.06 | 2400 | 0.0221 | 0.7739 |
| 0.0016 | 11.98 | 2600 | 0.0220 | 0.7708 |
| 0.0016 | 12.9 | 2800 | 0.0224 | 0.7732 |
| 0.0016 | 13.82 | 3000 | 0.0216 | 0.7750 |
| 0.0015 | 14.75 | 3200 | 0.0215 | 0.7774 |
| 0.0015 | 15.67 | 3400 | 0.0219 | 0.7754 |
| 0.0014 | 16.59 | 3600 | 0.0222 | 0.7751 |
| 0.0014 | 17.51 | 3800 | 0.0216 | 0.7751 |
| 0.0013 | 18.43 | 4000 | 0.0209 | 0.7755 |
| 0.0013 | 19.35 | 4200 | 0.0217 | 0.7728 |
| 0.0013 | 20.28 | 4400 | 0.0210 | 0.7731 |
| 0.0013 | 21.2 | 4600 | 0.0212 | 0.7737 |
| 0.0012 | 22.12 | 4800 | 0.0214 | 0.7725 |
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