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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: results_distilbert-base-uncased |
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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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# results_distilbert-base-uncased |
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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: 0.1696 |
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- Accuracy: 0.9277 |
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- Precision: 0.9364 |
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- Recall: 0.9447 |
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- F1: 0.9406 |
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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: 32 |
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- eval_batch_size: 32 |
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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_ratio: 0.1 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.6033 | 0.09 | 50 | 0.3599 | 0.8509 | 0.8622 | 0.8970 | 0.8792 | |
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| 0.3466 | 0.18 | 100 | 0.3466 | 0.8527 | 0.9638 | 0.7862 | 0.8660 | |
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| 0.2446 | 0.28 | 150 | 0.2166 | 0.9073 | 0.9293 | 0.9165 | 0.9229 | |
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| 0.2277 | 0.37 | 200 | 0.2014 | 0.9137 | 0.9153 | 0.9450 | 0.9299 | |
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| 0.2099 | 0.46 | 250 | 0.2183 | 0.9174 | 0.9090 | 0.9596 | 0.9336 | |
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| 0.2276 | 0.55 | 300 | 0.1927 | 0.9195 | 0.9275 | 0.9405 | 0.9340 | |
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| 0.21 | 0.64 | 350 | 0.1807 | 0.9254 | 0.9381 | 0.9387 | 0.9384 | |
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| 0.2009 | 0.74 | 400 | 0.1808 | 0.9236 | 0.9471 | 0.9254 | 0.9361 | |
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| 0.1816 | 0.83 | 450 | 0.1823 | 0.9238 | 0.9173 | 0.9607 | 0.9385 | |
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| 0.1728 | 0.92 | 500 | 0.1696 | 0.9277 | 0.9364 | 0.9447 | 0.9406 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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