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en_euph_distilbert_lora_final
nhankins/en_euph_distilbert_lora_final
en_euph_distilbert_lora_final is a machine learning model from nhankins. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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Updated Feb 16, 2024
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Feb 16, 2024
- Updated
- Feb 16, 2024
- SHA
- 598fbecf
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. -->
distilbert-base-multilingual-cased-lora-text-classification
This model is a fine-tuned version of distilbert/distilbert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5041
- Precision: 0.7846
- Recall: 0.9075
- F1 and accuracy: {'accuracy': 0.7544757033248082, 'f1': 0.8415841584158416}
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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 and accuracy |
|---|---|---|---|---|---|---|
| No log | 1.0 | 391 | 0.5886 | 0.7187 | 1.0 | {'accuracy': 0.7186700767263428, 'f1': 0.8363095238095238} |
| 0.6142 | 2.0 | 782 | 0.5735 | 0.7187 | 1.0 | {'accuracy': 0.7186700767263428, 'f1': 0.8363095238095238} |
| 0.5823 | 3.0 | 1173 | 0.5369 | 0.7321 | 0.9822 | {'accuracy': 0.7289002557544757, 'f1': 0.838905775075988} |
| 0.5451 | 4.0 | 1564 | 0.5190 | 0.7486 | 0.9537 | {'accuracy': 0.7365728900255755, 'f1': 0.8388106416275432} |
| 0.5451 | 5.0 | 1955 | 0.5266 | 0.7542 | 0.9609 | {'accuracy': 0.7468030690537084, 'f1': 0.8450704225352114} |
| 0.5161 | 6.0 | 2346 | 0.5047 | 0.7731 | 0.9217 | {'accuracy': 0.7493606138107417, 'f1': 0.8409090909090909} |
| 0.5093 | 7.0 | 2737 | 0.5046 | 0.7761 | 0.9253 | {'accuracy': 0.7544757033248082, 'f1': 0.8441558441558441} |
| 0.4962 | 8.0 | 3128 | 0.5047 | 0.7774 | 0.9075 | {'accuracy': 0.7468030690537084, 'f1': 0.8374384236453202} |
| 0.4996 | 9.0 | 3519 | 0.5024 | 0.7937 | 0.8897 | {'accuracy': 0.7544757033248082, 'f1': 0.8389261744966443} |
| 0.4996 | 10.0 | 3910 | 0.5041 | 0.7846 | 0.9075 | {'accuracy': 0.7544757033248082, 'f1': 0.8415841584158416} |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
- License
- apache-2.0
- Created
- Feb 16, 2024
- Updated
- Feb 16, 2024