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distilrubert-tiny-cased-conversational-v1_finetuned_empathy_classifier

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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_empathy_classifier
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+ results: []
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+ ---
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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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+
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+ # distilrubert-tiny-cased-conversational-v1_finetuned_empathy_classifier
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+
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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: 0.6624
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+ - Accuracy: 0.6780
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+ - F1: 0.6878
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+ - Precision: 0.7175
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+ - Recall: 0.6780
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 1.09 | 1.0 | 9 | 1.0661 | 0.4407 | 0.4464 | 0.6498 | 0.4407 |
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+ | 1.0292 | 2.0 | 18 | 0.9658 | 0.5678 | 0.5223 | 0.5179 | 0.5678 |
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+ | 0.942 | 3.0 | 27 | 0.8659 | 0.5932 | 0.5807 | 0.5723 | 0.5932 |
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+ | 0.8614 | 4.0 | 36 | 0.7864 | 0.6186 | 0.5924 | 0.5879 | 0.6186 |
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+ | 0.8002 | 5.0 | 45 | 0.7766 | 0.6017 | 0.5946 | 0.6086 | 0.6017 |
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+ | 0.7633 | 6.0 | 54 | 0.7545 | 0.6186 | 0.6022 | 0.6151 | 0.6186 |
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+ | 0.7249 | 7.0 | 63 | 0.7649 | 0.6356 | 0.6381 | 0.6921 | 0.6356 |
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+ | 0.6687 | 8.0 | 72 | 0.7115 | 0.6695 | 0.6741 | 0.7154 | 0.6695 |
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+ | 0.6426 | 9.0 | 81 | 0.6554 | 0.6864 | 0.6761 | 0.6807 | 0.6864 |
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+ | 0.6144 | 10.0 | 90 | 0.6649 | 0.6864 | 0.6909 | 0.7172 | 0.6864 |
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+ | 0.6252 | 11.0 | 99 | 0.8685 | 0.6186 | 0.6118 | 0.6880 | 0.6186 |
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+ | 0.5988 | 12.0 | 108 | 0.6306 | 0.6949 | 0.7015 | 0.7107 | 0.6949 |
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+ | 0.56 | 13.0 | 117 | 0.6919 | 0.6610 | 0.6662 | 0.7061 | 0.6610 |
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+ | 0.5468 | 14.0 | 126 | 0.6563 | 0.6949 | 0.6980 | 0.7188 | 0.6949 |
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+ | 0.5658 | 15.0 | 135 | 0.6351 | 0.6949 | 0.7048 | 0.7280 | 0.6949 |
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+ | 0.5262 | 16.0 | 144 | 0.6902 | 0.6780 | 0.6821 | 0.7173 | 0.6780 |
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+ | 0.4777 | 17.0 | 153 | 0.6237 | 0.6949 | 0.6981 | 0.7056 | 0.6949 |
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+ | 0.4771 | 18.0 | 162 | 0.6688 | 0.6780 | 0.6799 | 0.7035 | 0.6780 |
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+ | 0.4737 | 19.0 | 171 | 0.6482 | 0.6864 | 0.6957 | 0.7219 | 0.6864 |
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+ | 0.5033 | 20.0 | 180 | 0.6624 | 0.6780 | 0.6878 | 0.7175 | 0.6780 |
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+
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+
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+ ### Framework versions
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+
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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