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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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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: my_new_model |
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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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# my_new_model |
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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: 0.4151 |
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- Accuracy: 0.882 |
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- F1: 0.8815 |
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- Precision: 0.8825 |
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- Recall: 0.882 |
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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: 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: 10 |
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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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| No log | 1.0 | 125 | 0.5276 | 0.846 | 0.8496 | 0.8591 | 0.846 | |
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| No log | 2.0 | 250 | 0.3993 | 0.874 | 0.8755 | 0.8801 | 0.874 | |
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| No log | 3.0 | 375 | 0.3623 | 0.878 | 0.8808 | 0.8896 | 0.878 | |
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| 0.5033 | 4.0 | 500 | 0.3386 | 0.898 | 0.8985 | 0.9005 | 0.898 | |
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| 0.5033 | 5.0 | 625 | 0.3791 | 0.884 | 0.8840 | 0.8850 | 0.884 | |
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| 0.5033 | 6.0 | 750 | 0.3490 | 0.898 | 0.8993 | 0.9020 | 0.898 | |
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| 0.5033 | 7.0 | 875 | 0.3899 | 0.89 | 0.8898 | 0.8897 | 0.89 | |
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| 0.1244 | 8.0 | 1000 | 0.4148 | 0.87 | 0.8690 | 0.8686 | 0.87 | |
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| 0.1244 | 9.0 | 1125 | 0.4030 | 0.888 | 0.8880 | 0.8887 | 0.888 | |
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| 0.1244 | 10.0 | 1250 | 0.4151 | 0.882 | 0.8815 | 0.8825 | 0.882 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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