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zh_distilbert_lora_adapter_1.0
nhankins/zh_distilbert_lora_adapter_1.0
zh_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
- d4382724
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: 0.5474
- Precision: 0.7635
- Recall: 0.9338
- F1 and accuracy: {'accuracy': 0.7456359102244389, 'f1': 0.8401253918495297}
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 | 401 | 0.6017 | 0.7157 | 1.0 | {'accuracy': 0.71571072319202, 'f1': 0.8343023255813953} |
| 0.5798 | 2.0 | 802 | 0.5967 | 0.7157 | 1.0 | {'accuracy': 0.71571072319202, 'f1': 0.8343023255813953} |
| 0.5546 | 3.0 | 1203 | 0.5722 | 0.7157 | 1.0 | {'accuracy': 0.71571072319202, 'f1': 0.8343023255813953} |
| 0.5403 | 4.0 | 1604 | 0.5624 | 0.7259 | 0.9965 | {'accuracy': 0.7281795511221946, 'f1': 0.8399412628487517} |
| 0.5206 | 5.0 | 2005 | 0.5597 | 0.7368 | 0.9756 | {'accuracy': 0.7331670822942643, 'f1': 0.8395802098950524} |
| 0.5206 | 6.0 | 2406 | 0.5588 | 0.7520 | 0.9617 | {'accuracy': 0.7456359102244389, 'f1': 0.8440366972477064} |
| 0.5153 | 7.0 | 2807 | 0.5679 | 0.7554 | 0.9686 | {'accuracy': 0.7531172069825436, 'f1': 0.8488549618320611} |
| 0.4959 | 8.0 | 3208 | 0.5693 | 0.7576 | 0.9582 | {'accuracy': 0.7506234413965087, 'f1': 0.8461538461538461} |
| 0.4801 | 9.0 | 3609 | 0.5466 | 0.7635 | 0.9338 | {'accuracy': 0.7456359102244389, 'f1': 0.8401253918495297} |
| 0.4949 | 10.0 | 4010 | 0.5474 | 0.7635 | 0.9338 | {'accuracy': 0.7456359102244389, 'f1': 0.8401253918495297} |
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