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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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base_model: DeepPavlov/distilrubert-tiny-cased-conversational-v1 |
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model-index: |
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- name: distilrubert-tiny-cased-conversational-v1_empathy_preprocessed_punct_lowercasing |
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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_empathy_preprocessed_punct_lowercasing |
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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: 0.6036 |
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- Accuracy: 0.7458 |
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- F1: 0.7409 |
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- Precision: 0.7420 |
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- Recall: 0.7458 |
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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=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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| 1.0953 | 1.0 | 9 | 1.0692 | 0.4661 | 0.3740 | 0.3740 | 0.4661 | |
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| 1.066 | 2.0 | 18 | 1.0242 | 0.5593 | 0.5491 | 0.5446 | 0.5593 | |
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| 1.0119 | 3.0 | 27 | 0.9259 | 0.6102 | 0.6106 | 0.6147 | 0.6102 | |
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| 0.9118 | 4.0 | 36 | 0.8659 | 0.5847 | 0.5349 | 0.5835 | 0.5847 | |
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| 0.8921 | 5.0 | 45 | 0.7925 | 0.6356 | 0.6133 | 0.6275 | 0.6356 | |
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| 0.83 | 6.0 | 54 | 0.7776 | 0.6271 | 0.6087 | 0.6199 | 0.6271 | |
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| 0.8015 | 7.0 | 63 | 0.7675 | 0.6695 | 0.6601 | 0.6871 | 0.6695 | |
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| 0.7334 | 8.0 | 72 | 0.7133 | 0.6780 | 0.6659 | 0.6748 | 0.6780 | |
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| 0.696 | 9.0 | 81 | 0.6939 | 0.6864 | 0.6758 | 0.6833 | 0.6864 | |
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| 0.6349 | 10.0 | 90 | 0.6555 | 0.7119 | 0.7057 | 0.7085 | 0.7119 | |
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| 0.6482 | 11.0 | 99 | 0.6585 | 0.7288 | 0.7202 | 0.7339 | 0.7288 | |
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| 0.5924 | 12.0 | 108 | 0.6223 | 0.7373 | 0.7332 | 0.7343 | 0.7373 | |
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| 0.5437 | 13.0 | 117 | 0.6364 | 0.7288 | 0.7231 | 0.7296 | 0.7288 | |
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| 0.5653 | 14.0 | 126 | 0.6158 | 0.7373 | 0.7266 | 0.7342 | 0.7373 | |
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| 0.5314 | 15.0 | 135 | 0.6104 | 0.7458 | 0.7439 | 0.7435 | 0.7458 | |
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| 0.4912 | 16.0 | 144 | 0.6119 | 0.7458 | 0.7433 | 0.7442 | 0.7458 | |
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| 0.4819 | 17.0 | 153 | 0.6040 | 0.7458 | 0.7452 | 0.7448 | 0.7458 | |
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| 0.4873 | 18.0 | 162 | 0.6113 | 0.7288 | 0.7248 | 0.7275 | 0.7288 | |
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| 0.4729 | 19.0 | 171 | 0.6035 | 0.7373 | 0.7292 | 0.7341 | 0.7373 | |
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| 0.4654 | 20.0 | 180 | 0.6036 | 0.7458 | 0.7409 | 0.7420 | 0.7458 | |
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### Framework versions |
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- Transformers 4.21.2 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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