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yo_euph_distilbert_lora_final
nhankins/yo_euph_distilbert_lora_final
yo_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
- 473aad9f
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: 0.6074
- Precision: 0.7192
- Recall: 0.912
- F1 and accuracy: {'accuracy': 0.7146529562982005, 'f1': 0.8042328042328042}
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 | 388 | 0.6278 | 0.6723 | 0.96 | {'accuracy': 0.6735218508997429, 'f1': 0.7907742998352554} |
| 0.5998 | 2.0 | 776 | 0.6380 | 0.6713 | 0.956 | {'accuracy': 0.6709511568123393, 'f1': 0.7887788778877888} |
| 0.5865 | 3.0 | 1164 | 0.6196 | 0.6988 | 0.9 | {'accuracy': 0.6863753213367609, 'f1': 0.7867132867132868} |
| 0.5681 | 4.0 | 1552 | 0.6284 | 0.7018 | 0.932 | {'accuracy': 0.7017994858611826, 'f1': 0.8006872852233677} |
| 0.5681 | 5.0 | 1940 | 0.6072 | 0.7143 | 0.88 | {'accuracy': 0.6966580976863753, 'f1': 0.7885304659498208} |
| 0.5641 | 6.0 | 2328 | 0.6122 | 0.7031 | 0.9 | {'accuracy': 0.6915167095115681, 'f1': 0.7894736842105263} |
| 0.5356 | 7.0 | 2716 | 0.6074 | 0.7125 | 0.912 | {'accuracy': 0.7069408740359897, 'f1': 0.8} |
| 0.5407 | 8.0 | 3104 | 0.6016 | 0.7320 | 0.896 | {'accuracy': 0.7223650385604113, 'f1': 0.8057553956834531} |
| 0.5407 | 9.0 | 3492 | 0.6079 | 0.7192 | 0.912 | {'accuracy': 0.7146529562982005, 'f1': 0.8042328042328042} |
| 0.535 | 10.0 | 3880 | 0.6074 | 0.7192 | 0.912 | {'accuracy': 0.7146529562982005, 'f1': 0.8042328042328042} |
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