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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- accuracy |
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- f1 |
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model-index: |
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- name: camembert-keyword-discriminator |
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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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# camembert-keyword-discriminator |
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This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2180 |
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- Precision: 0.6646 |
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- Recall: 0.7047 |
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- Accuracy: 0.9344 |
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- F1: 0.6841 |
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- Ent/precision: 0.7185 |
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- Ent/accuracy: 0.8157 |
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- Ent/f1: 0.7640 |
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- Con/precision: 0.5324 |
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- Con/accuracy: 0.4860 |
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- Con/f1: 0.5082 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | Ent/precision | Ent/accuracy | Ent/f1 | Con/precision | Con/accuracy | Con/f1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------------:|:------------:|:------:|:-------------:|:------------:|:------:| |
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| 0.2016 | 1.0 | 1875 | 0.1910 | 0.5947 | 0.7125 | 0.9243 | 0.6483 | 0.6372 | 0.8809 | 0.7395 | 0.4560 | 0.3806 | 0.4149 | |
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| 0.1454 | 2.0 | 3750 | 0.1632 | 0.6381 | 0.7056 | 0.9324 | 0.6701 | 0.6887 | 0.8291 | 0.7524 | 0.5064 | 0.4621 | 0.4833 | |
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| 0.1211 | 3.0 | 5625 | 0.1702 | 0.6703 | 0.6678 | 0.9343 | 0.6690 | 0.7120 | 0.7988 | 0.7529 | 0.5471 | 0.4094 | 0.4684 | |
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| 0.1021 | 4.0 | 7500 | 0.1745 | 0.6777 | 0.6708 | 0.9351 | 0.6742 | 0.7206 | 0.7956 | 0.7562 | 0.5557 | 0.4248 | 0.4815 | |
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| 0.0886 | 5.0 | 9375 | 0.1913 | 0.6540 | 0.7184 | 0.9340 | 0.6847 | 0.7022 | 0.8396 | 0.7648 | 0.5288 | 0.4795 | 0.5030 | |
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| 0.0781 | 6.0 | 11250 | 0.2021 | 0.6605 | 0.7132 | 0.9344 | 0.6858 | 0.7139 | 0.8258 | 0.7658 | 0.5293 | 0.4913 | 0.5096 | |
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| 0.0686 | 7.0 | 13125 | 0.2127 | 0.6539 | 0.7132 | 0.9337 | 0.6822 | 0.7170 | 0.8172 | 0.7638 | 0.5112 | 0.5083 | 0.5098 | |
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| 0.0667 | 8.0 | 15000 | 0.2180 | 0.6646 | 0.7047 | 0.9344 | 0.6841 | 0.7185 | 0.8157 | 0.7640 | 0.5324 | 0.4860 | 0.5082 | |
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### Framework versions |
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- Transformers 4.19.2 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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