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es_euph_distilbert_lora_final
nhankins/es_euph_distilbert_lora_final
es_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
- 0fda3ca2
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: 0.5930
- Precision: 0.7325
- Recall: 0.7542
- F1 and accuracy: {'accuracy': 0.6702412868632708, 'f1': 0.74321503131524}
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 | 0.6533 | 0.6327 | 1.0 | {'accuracy': 0.6327077747989276, 'f1': 0.7750410509031198} |
| 0.67 | 2.0 | 744 | 0.6432 | 0.6327 | 1.0 | {'accuracy': 0.6327077747989276, 'f1': 0.7750410509031198} |
| 0.6548 | 3.0 | 1116 | 0.6197 | 0.6341 | 0.9915 | {'accuracy': 0.6327077747989276, 'f1': 0.7735537190082644} |
| 0.6548 | 4.0 | 1488 | 0.6020 | 0.6678 | 0.8178 | {'accuracy': 0.6273458445040214, 'f1': 0.7352380952380952} |
| 0.6211 | 5.0 | 1860 | 0.5969 | 0.696 | 0.7373 | {'accuracy': 0.6300268096514745, 'f1': 0.7160493827160493} |
| 0.5929 | 6.0 | 2232 | 0.5954 | 0.6980 | 0.7542 | {'accuracy': 0.6380697050938338, 'f1': 0.7250509164969451} |
| 0.5887 | 7.0 | 2604 | 0.5940 | 0.7412 | 0.7161 | {'accuracy': 0.6621983914209115, 'f1': 0.728448275862069} |
| 0.5887 | 8.0 | 2976 | 0.5937 | 0.7426 | 0.7458 | {'accuracy': 0.675603217158177, 'f1': 0.7441860465116279} |
| 0.5809 | 9.0 | 3348 | 0.5933 | 0.7247 | 0.7585 | {'accuracy': 0.6648793565683646, 'f1': 0.7412008281573499} |
| 0.5726 | 10.0 | 3720 | 0.5930 | 0.7325 | 0.7542 | {'accuracy': 0.6702412868632708, 'f1': 0.74321503131524} |
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