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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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- precision |
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- recall |
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- f1 |
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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: 0.6962 |
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- Accuracy: 0.5 |
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- Precision: 0.5 |
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- Recall: 1.0 |
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- F1: 0.6667 |
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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: 5e-05 |
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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 | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| No log | 0.21 | 100 | 0.7035 | 0.4945 | 0.4972 | 0.9750 | 0.6586 | |
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| No log | 0.42 | 200 | 0.6967 | 0.4852 | 0.4912 | 0.8253 | 0.6158 | |
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| No log | 0.62 | 300 | 0.6965 | 0.4899 | 0.4880 | 0.4119 | 0.4467 | |
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| No log | 0.83 | 400 | 0.6931 | 0.5 | 0.5 | 1.0 | 0.6667 | |
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| 0.6974 | 1.04 | 500 | 0.6959 | 0.5 | 0.5 | 1.0 | 0.6667 | |
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| 0.6974 | 1.25 | 600 | 0.7093 | 0.5 | 0.5 | 1.0 | 0.6667 | |
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| 0.6974 | 1.46 | 700 | 0.6949 | 0.5 | 0.5 | 1.0 | 0.6667 | |
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| 0.6974 | 1.66 | 800 | 0.6943 | 0.5 | 0.5 | 1.0 | 0.6667 | |
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| 0.6974 | 1.87 | 900 | 0.6947 | 0.5 | 0.5 | 1.0 | 0.6667 | |
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| 0.6965 | 2.08 | 1000 | 0.6932 | 0.5 | 0.5 | 1.0 | 0.6667 | |
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| 0.6965 | 2.29 | 1100 | 0.6948 | 0.5 | 0.5 | 1.0 | 0.6667 | |
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| 0.6965 | 2.49 | 1200 | 0.6934 | 0.5 | 0.0 | 0.0 | 0.0 | |
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| 0.6965 | 2.7 | 1300 | 0.6934 | 0.5 | 0.5 | 1.0 | 0.6667 | |
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| 0.6965 | 2.91 | 1400 | 0.6962 | 0.5 | 0.5 | 1.0 | 0.6667 | |
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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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