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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- emotion |
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metrics: |
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- accuracy |
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model-index: |
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- name: DBERT_Emotions_tuned |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: emotion |
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type: emotion |
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config: split |
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split: validation |
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args: split |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.925 |
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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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# DBERT_Emotions_tuned |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1828 |
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- Accuracy: 0.925 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 0.1 | 100 | 0.7513 | 0.7365 | |
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| No log | 0.2 | 200 | 0.3693 | 0.8895 | |
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| No log | 0.3 | 300 | 0.3118 | 0.906 | |
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| No log | 0.4 | 400 | 0.3048 | 0.9055 | |
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| 0.5368 | 0.5 | 500 | 0.2649 | 0.9225 | |
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| 0.5368 | 0.6 | 600 | 0.2192 | 0.9235 | |
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| 0.5368 | 0.7 | 700 | 0.2254 | 0.9245 | |
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| 0.5368 | 0.8 | 800 | 0.2016 | 0.931 | |
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| 0.5368 | 0.9 | 900 | 0.1685 | 0.935 | |
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| 0.2254 | 1.0 | 1000 | 0.1926 | 0.9295 | |
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| 0.2254 | 1.1 | 1100 | 0.2128 | 0.928 | |
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| 0.2254 | 1.2 | 1200 | 0.2008 | 0.9325 | |
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| 0.2254 | 1.3 | 1300 | 0.1662 | 0.9385 | |
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| 0.2254 | 1.4 | 1400 | 0.1945 | 0.939 | |
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| 0.1315 | 1.5 | 1500 | 0.1652 | 0.939 | |
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| 0.1315 | 1.6 | 1600 | 0.1820 | 0.938 | |
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| 0.1315 | 1.7 | 1700 | 0.1660 | 0.938 | |
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| 0.1315 | 1.8 | 1800 | 0.1590 | 0.93 | |
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| 0.1315 | 1.9 | 1900 | 0.1601 | 0.935 | |
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| 0.1295 | 2.0 | 2000 | 0.1645 | 0.9345 | |
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| 0.1295 | 2.1 | 2100 | 0.1845 | 0.9305 | |
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| 0.1295 | 2.2 | 2200 | 0.1784 | 0.9355 | |
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| 0.1295 | 2.3 | 2300 | 0.2042 | 0.9365 | |
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| 0.1295 | 2.4 | 2400 | 0.1852 | 0.9365 | |
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| 0.0891 | 2.5 | 2500 | 0.1797 | 0.94 | |
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| 0.0891 | 2.6 | 2600 | 0.1741 | 0.9365 | |
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| 0.0891 | 2.7 | 2700 | 0.1758 | 0.9385 | |
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| 0.0891 | 2.8 | 2800 | 0.1771 | 0.944 | |
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| 0.0891 | 2.9 | 2900 | 0.1688 | 0.9385 | |
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| 0.0848 | 3.0 | 3000 | 0.1671 | 0.94 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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