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en_distilbert_lora_adapter_2.0
nhankins/en_distilbert_lora_adapter_2.0
en_distilbert_lora_adapter_2.0 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
- 932b8ac7
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: 0.5714
- Precision: 0.7417
- Recall: 1.0
- F1 and accuracy: {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847}
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: 0.001
- 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 | 0.5780 | 0.7417 | 1.0 | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.647 | 2.0 | 782 | 0.5748 | 0.7417 | 1.0 | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6216 | 3.0 | 1173 | 0.5713 | 0.7417 | 1.0 | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6201 | 4.0 | 1564 | 0.5726 | 0.7417 | 1.0 | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6201 | 5.0 | 1955 | 0.5765 | 0.7417 | 1.0 | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6199 | 6.0 | 2346 | 0.5756 | 0.7417 | 1.0 | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6365 | 7.0 | 2737 | 0.5827 | 0.7417 | 1.0 | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6165 | 8.0 | 3128 | 0.5715 | 0.7417 | 1.0 | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6185 | 9.0 | 3519 | 0.5715 | 0.7417 | 1.0 | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6185 | 10.0 | 3910 | 0.5714 | 0.7417 | 1.0 | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
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