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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_best_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_best_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: 0.5751
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- Accuracy: 0.8716
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- F1: 0.8713
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- Precision: 0.8721
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- Recall: 0.8716
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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: 0.0001
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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=1e-06
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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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| 0.8851 | 1.0 | 69 | 0.4740 | 0.8361 | 0.8346 | 0.8364 | 0.8361 |
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| 0.4404 | 2.0 | 138 | 0.4018 | 0.8643 | 0.8625 | 0.8672 | 0.8643 |
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| 0.305 | 3.0 | 207 | 0.3754 | 0.8800 | 0.8795 | 0.8794 | 0.8800 |
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| 0.2441 | 4.0 | 276 | 0.3942 | 0.8758 | 0.8748 | 0.8752 | 0.8758 |
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| 0.1837 | 5.0 | 345 | 0.4005 | 0.8873 | 0.8870 | 0.8877 | 0.8873 |
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| 0.1573 | 6.0 | 414 | 0.4468 | 0.8716 | 0.8718 | 0.8730 | 0.8716 |
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| 0.1292 | 7.0 | 483 | 0.4582 | 0.8747 | 0.8750 | 0.8758 | 0.8747 |
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| 0.0949 | 8.0 | 552 | 0.5110 | 0.8601 | 0.8601 | 0.8628 | 0.8601 |
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| 0.0729 | 9.0 | 621 | 0.5415 | 0.8674 | 0.8674 | 0.8681 | 0.8674 |
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| 0.058 | 10.0 | 690 | 0.5751 | 0.8716 | 0.8713 | 0.8721 | 0.8716 |
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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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