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--- |
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license: mit |
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base_model: prajjwal1/bert-mini |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: bert-mini-emotion_classifier |
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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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# bert-mini-emotion_classifier |
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This model is a fine-tuned version of [prajjwal1/bert-mini](https://huggingface.co/prajjwal1/bert-mini) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0648 |
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- F1: 0.9315 |
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- Roc Auc: 0.9589 |
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- Accuracy: 0.9224 |
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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=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 5 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
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| 0.4176 | 0.1 | 500 | 0.2929 | 0.6755 | 0.7687 | 0.5550 | |
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| 0.2278 | 0.19 | 1000 | 0.1623 | 0.8931 | 0.9246 | 0.8630 | |
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| 0.1513 | 0.29 | 1500 | 0.1184 | 0.9185 | 0.9450 | 0.9022 | |
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| 0.1198 | 0.38 | 2000 | 0.0957 | 0.9274 | 0.9536 | 0.9197 | |
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| 0.1011 | 0.48 | 2500 | 0.0815 | 0.9306 | 0.9568 | 0.9230 | |
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| 0.0881 | 0.58 | 3000 | 0.0729 | 0.9320 | 0.9575 | 0.9237 | |
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| 0.0815 | 0.67 | 3500 | 0.0669 | 0.9337 | 0.9596 | 0.9256 | |
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| 0.0767 | 0.77 | 4000 | 0.0633 | 0.9346 | 0.9609 | 0.9260 | |
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| 0.0721 | 0.86 | 4500 | 0.0612 | 0.9333 | 0.9602 | 0.9233 | |
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| 0.071 | 0.96 | 5000 | 0.0601 | 0.9339 | 0.9607 | 0.9251 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.0 |
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- Tokenizers 0.15.0 |
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