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yo_distilbert_lora_adapter_1.0
nhankins/yo_distilbert_lora_adapter_1.0
yo_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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Public
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
- 89db85f1
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: 0.6038
- Precision: 0.7039
- Recall: 0.9283
- F1 and accuracy: {'accuracy': 0.7017994858611826, 'f1': 0.8006872852233677}
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 | 0.6457 | 0.6452 | 1.0 | {'accuracy': 0.6452442159383034, 'f1': 0.784375} |
| 0.6427 | 2.0 | 776 | 0.6410 | 0.6452 | 1.0 | {'accuracy': 0.6452442159383034, 'f1': 0.784375} |
| 0.6298 | 3.0 | 1164 | 0.6290 | 0.6486 | 1.0 | {'accuracy': 0.6503856041131105, 'f1': 0.786833855799373} |
| 0.6216 | 4.0 | 1552 | 0.6243 | 0.6552 | 0.9841 | {'accuracy': 0.6555269922879178, 'f1': 0.7866242038216561} |
| 0.6216 | 5.0 | 1940 | 0.6148 | 0.6799 | 0.9562 | {'accuracy': 0.6812339331619537, 'f1': 0.7947019867549668} |
| 0.6091 | 6.0 | 2328 | 0.6131 | 0.6791 | 0.9442 | {'accuracy': 0.6760925449871465, 'f1': 0.79} |
| 0.5892 | 7.0 | 2716 | 0.6102 | 0.6871 | 0.9363 | {'accuracy': 0.6838046272493573, 'f1': 0.7925801011804384} |
| 0.5818 | 8.0 | 3104 | 0.6051 | 0.6997 | 0.9283 | {'accuracy': 0.6966580976863753, 'f1': 0.7979452054794521} |
| 0.5818 | 9.0 | 3492 | 0.6061 | 0.6953 | 0.9363 | {'accuracy': 0.6940874035989717, 'f1': 0.797962648556876} |
| 0.5766 | 10.0 | 3880 | 0.6038 | 0.7039 | 0.9283 | {'accuracy': 0.7017994858611826, 'f1': 0.8006872852233677} |
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