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yo_xlmroberta_lora_adapter_1.0
nhankins/yo_xlmroberta_lora_adapter_1.0
yo_xlmroberta_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.
should probably proofread and complete it, then remove this comment. --
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Public
Updated Feb 10, 2024
Repo size
24.8 MB
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At a glance
- Access
- Public
- Created
- Feb 10, 2024
- Updated
- Feb 10, 2024
- SHA
- 3720ff0c
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. -->
xlm-roberta-base-finetuned-yoruba-lora-text-classification
This model is a fine-tuned version of Davlan/xlm-roberta-base-finetuned-yoruba on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5843
- Precision: 0.6850
- Recall: 1.0
- F1 and accuracy: {'accuracy': 0.6915167095115681, 'f1': 0.8130841121495327}
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 | 0.6314 | 0.6710 | 1.0 | {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} |
| 0.6474 | 2.0 | 776 | 0.6289 | 0.6710 | 1.0 | {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} |
| 0.652 | 3.0 | 1164 | 0.6270 | 0.6710 | 1.0 | {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} |
| 0.646 | 4.0 | 1552 | 0.6235 | 0.6710 | 1.0 | {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} |
| 0.646 | 5.0 | 1940 | 0.6188 | 0.6710 | 1.0 | {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} |
| 0.6459 | 6.0 | 2328 | 0.6092 | 0.6710 | 1.0 | {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} |
| 0.6281 | 7.0 | 2716 | 0.5958 | 0.6710 | 1.0 | {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} |
| 0.6268 | 8.0 | 3104 | 0.5912 | 0.6727 | 1.0 | {'accuracy': 0.6735218508997429, 'f1': 0.8043143297380586} |
| 0.6268 | 9.0 | 3492 | 0.5861 | 0.6850 | 1.0 | {'accuracy': 0.6915167095115681, 'f1': 0.8130841121495327} |
| 0.6105 | 10.0 | 3880 | 0.5843 | 0.6850 | 1.0 | {'accuracy': 0.6915167095115681, 'f1': 0.8130841121495327} |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
- Created
- Feb 10, 2024
- Updated
- Feb 10, 2024