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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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model-index: |
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- name: distilbert-base-uncased-finetuned |
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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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# distilbert-base-uncased-finetuned |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0098 |
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- Accuracy: 0.9009 |
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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: 15 |
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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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| 0.3073 | 1.0 | 5250 | 0.2758 | 0.8925 | |
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| 0.2356 | 2.0 | 10500 | 0.2988 | 0.8988 | |
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| 0.1834 | 3.0 | 15750 | 0.3662 | 0.8989 | |
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| 0.1403 | 4.0 | 21000 | 0.4688 | 0.8955 | |
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| 0.1038 | 5.0 | 26250 | 0.5136 | 0.8925 | |
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| 0.0788 | 6.0 | 31500 | 0.6189 | 0.8954 | |
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| 0.0687 | 7.0 | 36750 | 0.6439 | 0.8947 | |
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| 0.0439 | 8.0 | 42000 | 0.7104 | 0.8991 | |
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| 0.035 | 9.0 | 47250 | 0.7527 | 0.8983 | |
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| 0.0205 | 10.0 | 52500 | 0.8317 | 0.9011 | |
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| 0.0258 | 11.0 | 57750 | 0.8488 | 0.9003 | |
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| 0.0174 | 12.0 | 63000 | 0.8577 | 0.9027 | |
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| 0.0095 | 13.0 | 68250 | 0.9242 | 0.9007 | |
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| 0.0096 | 14.0 | 73500 | 1.0134 | 0.9003 | |
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| 0.0083 | 15.0 | 78750 | 1.0098 | 0.9009 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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