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zh_euph_distilbert_lora_final
nhankins/zh_euph_distilbert_lora_final
zh_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 15, 2024
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Feb 15, 2024
- Updated
- Feb 15, 2024
- SHA
- 1fa5459d
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: 0.4881
- Precision: 0.7966
- Recall: 0.9216
- F1 and accuracy: {'accuracy': 0.7605985037406484, 'f1': 0.8545454545454545}
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 | 0.5429 | 0.7631 | 1.0 | {'accuracy': 0.7630922693266833, 'f1': 0.8656294200848657} |
| 0.5808 | 2.0 | 802 | 0.5361 | 0.7631 | 1.0 | {'accuracy': 0.7630922693266833, 'f1': 0.8656294200848657} |
| 0.5805 | 3.0 | 1203 | 0.5235 | 0.7631 | 1.0 | {'accuracy': 0.7630922693266833, 'f1': 0.8656294200848657} |
| 0.5554 | 4.0 | 1604 | 0.5096 | 0.7669 | 1.0 | {'accuracy': 0.7680798004987531, 'f1': 0.8680851063829788} |
| 0.5214 | 5.0 | 2005 | 0.5046 | 0.7734 | 0.9706 | {'accuracy': 0.7605985037406484, 'f1': 0.8608695652173913} |
| 0.5214 | 6.0 | 2406 | 0.4971 | 0.7950 | 0.9379 | {'accuracy': 0.7680798004987531, 'f1': 0.8605697151424289} |
| 0.5152 | 7.0 | 2807 | 0.4919 | 0.7983 | 0.9183 | {'accuracy': 0.7605985037406484, 'f1': 0.8541033434650457} |
| 0.4956 | 8.0 | 3208 | 0.4881 | 0.8017 | 0.9118 | {'accuracy': 0.7605985037406484, 'f1': 0.8532110091743118} |
| 0.4891 | 9.0 | 3609 | 0.4881 | 0.7972 | 0.9248 | {'accuracy': 0.7630922693266833, 'f1': 0.8562783661119516} |
| 0.5038 | 10.0 | 4010 | 0.4881 | 0.7966 | 0.9216 | {'accuracy': 0.7605985037406484, 'f1': 0.8545454545454545} |
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 15, 2024
- Updated
- Feb 15, 2024