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es_distilbert_lora_adapter_1.0
nhankins/es_distilbert_lora_adapter_1.0
es_distilbert_lora_adapter_1.0 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 13, 2024
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Feb 13, 2024
- Updated
- Feb 13, 2024
- SHA
- 0a9928a4
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: 0.5734
- Precision: 0.7362
- Recall: 0.8026
- F1 and accuracy: {'accuracy': 0.6970509383378016, 'f1': 0.7679671457905544}
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 | 372 | 0.6573 | 0.6247 | 1.0 | {'accuracy': 0.6246648793565683, 'f1': 0.768976897689769} |
| 0.6699 | 2.0 | 744 | 0.6452 | 0.6247 | 1.0 | {'accuracy': 0.6246648793565683, 'f1': 0.768976897689769} |
| 0.6412 | 3.0 | 1116 | 0.6130 | 0.6602 | 0.8755 | {'accuracy': 0.6407506702412868, 'f1': 0.7527675276752768} |
| 0.6412 | 4.0 | 1488 | 0.5949 | 0.7413 | 0.8240 | {'accuracy': 0.710455764075067, 'f1': 0.7804878048780487} |
| 0.6158 | 5.0 | 1860 | 0.5860 | 0.7323 | 0.8455 | {'accuracy': 0.710455764075067, 'f1': 0.7848605577689244} |
| 0.5891 | 6.0 | 2232 | 0.5802 | 0.7381 | 0.7983 | {'accuracy': 0.6970509383378016, 'f1': 0.7670103092783506} |
| 0.5855 | 7.0 | 2604 | 0.5770 | 0.7354 | 0.8112 | {'accuracy': 0.6997319034852547, 'f1': 0.7714285714285714} |
| 0.5855 | 8.0 | 2976 | 0.5757 | 0.7328 | 0.8240 | {'accuracy': 0.7024128686327078, 'f1': 0.7757575757575758} |
| 0.5839 | 9.0 | 3348 | 0.5741 | 0.7362 | 0.8026 | {'accuracy': 0.6970509383378016, 'f1': 0.7679671457905544} |
| 0.5759 | 10.0 | 3720 | 0.5734 | 0.7362 | 0.8026 | {'accuracy': 0.6970509383378016, 'f1': 0.7679671457905544} |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
- License
- apache-2.0
- Created
- Feb 13, 2024
- Updated
- Feb 13, 2024