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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: finetuning-sentiment |
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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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# finetuning-sentiment |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8125 |
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- Accuracy@en: 0.9033 |
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- F1@en: 0.9002 |
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- Precision@en: 0.8989 |
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- Recall@en: 0.9018 |
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- Loss@en: 0.8125 |
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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: 8 |
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- eval_batch_size: 4 |
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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: 13 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy@en | F1@en | Precision@en | Recall@en | Loss@en | |
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|:-------------:|:-----:|:----:|:---------------:|:-----------:|:------:|:------------:|:---------:|:-------:| |
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| No log | 1.0 | 375 | 0.4653 | 0.8933 | 0.8895 | 0.8895 | 0.8895 | 0.4653 | |
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| 0.2086 | 2.0 | 750 | 0.4367 | 0.9033 | 0.9011 | 0.8979 | 0.9069 | 0.4367 | |
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| 0.1622 | 3.0 | 1125 | 0.4866 | 0.91 | 0.9081 | 0.9047 | 0.9151 | 0.4866 | |
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| 0.0622 | 4.0 | 1500 | 0.6156 | 0.9 | 0.8982 | 0.8951 | 0.9067 | 0.6156 | |
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| 0.0622 | 5.0 | 1875 | 0.6790 | 0.9133 | 0.9102 | 0.9102 | 0.9102 | 0.6790 | |
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| 0.0193 | 6.0 | 2250 | 0.6822 | 0.9 | 0.8978 | 0.8945 | 0.9041 | 0.6822 | |
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| 0.0202 | 7.0 | 2625 | 0.6595 | 0.91 | 0.9077 | 0.9047 | 0.9126 | 0.6595 | |
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| 0.0148 | 8.0 | 3000 | 0.6538 | 0.9067 | 0.9042 | 0.9014 | 0.9085 | 0.6538 | |
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| 0.0148 | 9.0 | 3375 | 0.6869 | 0.9067 | 0.9050 | 0.9018 | 0.9136 | 0.6869 | |
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| 0.0036 | 10.0 | 3750 | 0.7016 | 0.9033 | 0.9007 | 0.8981 | 0.9044 | 0.7016 | |
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| 0.0038 | 11.0 | 4125 | 0.8170 | 0.9 | 0.8961 | 0.8972 | 0.8951 | 0.8170 | |
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| 0.008 | 12.0 | 4500 | 0.8169 | 0.9033 | 0.9002 | 0.8989 | 0.9018 | 0.8169 | |
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| 0.008 | 13.0 | 4875 | 0.8125 | 0.9033 | 0.9002 | 0.8989 | 0.9018 | 0.8125 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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