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multilingual_euph_lora
nhankins/multilingual_euph_lora
multilingual_euph_lora 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
- 45050d2a
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: 0.5321
- Precision: 0.7883
- Recall: 0.8589
- F1 and accuracy: {'accuracy': 0.7487113402061856, 'f1': 0.8220802919708029}
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 |
|---|---|---|---|---|---|---|
| 0.6034 | 1.0 | 1552 | 0.5999 | 0.6781 | 0.9981 | {'accuracy': 0.678479381443299, 'f1': 0.8075588121866564} |
| 0.5756 | 2.0 | 3104 | 0.5892 | 0.7067 | 0.9418 | {'accuracy': 0.696520618556701, 'f1': 0.8075194115243155} |
| 0.5607 | 3.0 | 4656 | 0.5630 | 0.7449 | 0.8770 | {'accuracy': 0.7139175257731959, 'f1': 0.8056042031523644} |
| 0.5458 | 4.0 | 6208 | 0.5549 | 0.7544 | 0.8990 | {'accuracy': 0.7338917525773195, 'f1': 0.8203566768160069} |
| 0.5342 | 5.0 | 7760 | 0.5816 | 0.7381 | 0.9457 | {'accuracy': 0.7364690721649485, 'f1': 0.8290848307563727} |
| 0.5266 | 6.0 | 9312 | 0.5399 | 0.7705 | 0.8799 | {'accuracy': 0.7416237113402062, 'f1': 0.8215398308856252} |
| 0.519 | 7.0 | 10864 | 0.5315 | 0.7932 | 0.8408 | {'accuracy': 0.7442010309278351, 'f1': 0.8162887552059231} |
| 0.4878 | 8.0 | 12416 | 0.5318 | 0.7880 | 0.8541 | {'accuracy': 0.7461340206185567, 'f1': 0.8197621225983532} |
| 0.485 | 9.0 | 13968 | 0.5332 | 0.7851 | 0.8637 | {'accuracy': 0.7480670103092784, 'f1': 0.8225147526100772} |
| 0.5044 | 10.0 | 15520 | 0.5321 | 0.7883 | 0.8589 | {'accuracy': 0.7487113402061856, 'f1': 0.8220802919708029} |
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