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--- |
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license: mit |
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base_model: camembert-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: VogagenRelation |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# VogagenRelation |
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This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6923 |
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- Accuracy: 0.5413 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-08 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 0.21 | 100 | 0.6924 | 0.5398 | |
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| No log | 0.42 | 200 | 0.6924 | 0.5406 | |
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| No log | 0.62 | 300 | 0.6924 | 0.5421 | |
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| No log | 0.83 | 400 | 0.6924 | 0.5406 | |
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| 0.6921 | 1.04 | 500 | 0.6924 | 0.5421 | |
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| 0.6921 | 1.25 | 600 | 0.6923 | 0.5406 | |
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| 0.6921 | 1.46 | 700 | 0.6923 | 0.5406 | |
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| 0.6921 | 1.66 | 800 | 0.6923 | 0.5406 | |
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| 0.6921 | 1.87 | 900 | 0.6923 | 0.5398 | |
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| 0.6923 | 2.08 | 1000 | 0.6923 | 0.5406 | |
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| 0.6923 | 2.29 | 1100 | 0.6923 | 0.5406 | |
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| 0.6923 | 2.49 | 1200 | 0.6923 | 0.5390 | |
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| 0.6923 | 2.7 | 1300 | 0.6923 | 0.5390 | |
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| 0.6923 | 2.91 | 1400 | 0.6923 | 0.5398 | |
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| 0.6927 | 3.12 | 1500 | 0.6923 | 0.5406 | |
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| 0.6927 | 3.33 | 1600 | 0.6923 | 0.5398 | |
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| 0.6927 | 3.53 | 1700 | 0.6923 | 0.5398 | |
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| 0.6927 | 3.74 | 1800 | 0.6923 | 0.5398 | |
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| 0.6927 | 3.95 | 1900 | 0.6923 | 0.5413 | |
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| 0.6933 | 4.16 | 2000 | 0.6923 | 0.5413 | |
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| 0.6933 | 4.37 | 2100 | 0.6923 | 0.5413 | |
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| 0.6933 | 4.57 | 2200 | 0.6923 | 0.5413 | |
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| 0.6933 | 4.78 | 2300 | 0.6923 | 0.5406 | |
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| 0.6933 | 4.99 | 2400 | 0.6923 | 0.5406 | |
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| 0.6932 | 5.2 | 2500 | 0.6923 | 0.5406 | |
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| 0.6932 | 5.41 | 2600 | 0.6923 | 0.5413 | |
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| 0.6932 | 5.61 | 2700 | 0.6923 | 0.5429 | |
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| 0.6932 | 5.82 | 2800 | 0.6923 | 0.5421 | |
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| 0.6932 | 6.03 | 2900 | 0.6923 | 0.5406 | |
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| 0.6917 | 6.24 | 3000 | 0.6923 | 0.5421 | |
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| 0.6917 | 6.44 | 3100 | 0.6923 | 0.5421 | |
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| 0.6917 | 6.65 | 3200 | 0.6923 | 0.5421 | |
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| 0.6917 | 6.86 | 3300 | 0.6923 | 0.5413 | |
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| 0.6917 | 7.07 | 3400 | 0.6923 | 0.5413 | |
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| 0.6929 | 7.28 | 3500 | 0.6923 | 0.5413 | |
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| 0.6929 | 7.48 | 3600 | 0.6923 | 0.5413 | |
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| 0.6929 | 7.69 | 3700 | 0.6923 | 0.5413 | |
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| 0.6929 | 7.9 | 3800 | 0.6923 | 0.5413 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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