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--- |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: distilrubert-tiny-cased-conversational-v1_finetuned_emotion_experiment_augmented_anger_fear |
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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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# distilrubert-tiny-cased-conversational-v1_finetuned_emotion_experiment_augmented_anger_fear |
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This model is a fine-tuned version of [DeepPavlov/distilrubert-tiny-cased-conversational-v1](https://huggingface.co/DeepPavlov/distilrubert-tiny-cased-conversational-v1) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3760 |
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- Accuracy: 0.8758 |
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- F1: 0.8750 |
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- Precision: 0.8753 |
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- Recall: 0.8758 |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=0.0001 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 1.2636 | 1.0 | 69 | 1.0914 | 0.6013 | 0.5599 | 0.5780 | 0.6013 | |
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| 1.029 | 2.0 | 138 | 0.9180 | 0.6514 | 0.6344 | 0.6356 | 0.6514 | |
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| 0.904 | 3.0 | 207 | 0.8235 | 0.6827 | 0.6588 | 0.6904 | 0.6827 | |
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| 0.8084 | 4.0 | 276 | 0.7272 | 0.7537 | 0.7477 | 0.7564 | 0.7537 | |
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| 0.7242 | 5.0 | 345 | 0.6435 | 0.7860 | 0.7841 | 0.7861 | 0.7860 | |
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| 0.6305 | 6.0 | 414 | 0.5543 | 0.8173 | 0.8156 | 0.8200 | 0.8173 | |
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| 0.562 | 7.0 | 483 | 0.4860 | 0.8392 | 0.8383 | 0.8411 | 0.8392 | |
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| 0.5042 | 8.0 | 552 | 0.4474 | 0.8528 | 0.8514 | 0.8546 | 0.8528 | |
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| 0.4535 | 9.0 | 621 | 0.4213 | 0.8580 | 0.8579 | 0.8590 | 0.8580 | |
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| 0.4338 | 10.0 | 690 | 0.4106 | 0.8591 | 0.8578 | 0.8605 | 0.8591 | |
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| 0.4026 | 11.0 | 759 | 0.4064 | 0.8622 | 0.8615 | 0.8632 | 0.8622 | |
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| 0.3861 | 12.0 | 828 | 0.3874 | 0.8737 | 0.8728 | 0.8733 | 0.8737 | |
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| 0.3709 | 13.0 | 897 | 0.3841 | 0.8706 | 0.8696 | 0.8701 | 0.8706 | |
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| 0.3592 | 14.0 | 966 | 0.3841 | 0.8716 | 0.8709 | 0.8714 | 0.8716 | |
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| 0.3475 | 15.0 | 1035 | 0.3834 | 0.8737 | 0.8728 | 0.8732 | 0.8737 | |
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| 0.3537 | 16.0 | 1104 | 0.3805 | 0.8727 | 0.8717 | 0.8722 | 0.8727 | |
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| 0.3317 | 17.0 | 1173 | 0.3775 | 0.8747 | 0.8739 | 0.8741 | 0.8747 | |
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| 0.323 | 18.0 | 1242 | 0.3759 | 0.8727 | 0.8718 | 0.8721 | 0.8727 | |
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| 0.3327 | 19.0 | 1311 | 0.3776 | 0.8758 | 0.8750 | 0.8756 | 0.8758 | |
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| 0.3339 | 20.0 | 1380 | 0.3760 | 0.8758 | 0.8750 | 0.8753 | 0.8758 | |
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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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