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--- |
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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: CommitPredictor |
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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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# CommitPredictor |
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This model is a fine-tuned version of [microsoft/codebert-base-mlm](https://huggingface.co/microsoft/codebert-base-mlm) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9935 |
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- Accuracy: 0.6325 |
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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: 21 |
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- eval_batch_size: 21 |
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- seed: 42 |
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- gradient_accumulation_steps: 3 |
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- total_train_batch_size: 63 |
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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: 50 |
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- mixed_precision_training: Native AMP |
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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 | 1.0 | 448 | 2.4744 | 0.5376 | |
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| 2.9007 | 2.0 | 896 | 2.4149 | 0.5473 | |
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| 2.5284 | 3.0 | 1344 | 2.3077 | 0.5639 | |
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| 2.3292 | 4.0 | 1792 | 2.2617 | 0.5640 | |
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| 2.2692 | 5.0 | 2240 | 2.2155 | 0.5719 | |
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| 2.1766 | 6.0 | 2688 | 2.1555 | 0.5792 | |
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| 2.0842 | 7.0 | 3136 | 2.0758 | 0.6030 | |
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| 2.0268 | 8.0 | 3584 | 2.1446 | 0.5942 | |
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| 1.9416 | 9.0 | 4032 | 2.1110 | 0.5840 | |
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| 1.9416 | 10.0 | 4480 | 2.1379 | 0.5888 | |
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| 1.8969 | 11.0 | 4928 | 2.0461 | 0.6082 | |
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| 1.8247 | 12.0 | 5376 | 2.0585 | 0.6007 | |
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| 1.8038 | 13.0 | 5824 | 2.0541 | 0.6022 | |
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| 1.7601 | 14.0 | 6272 | 2.0832 | 0.6043 | |
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| 1.7086 | 15.0 | 6720 | 2.0224 | 0.6096 | |
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| 1.7087 | 16.0 | 7168 | 2.0853 | 0.6057 | |
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| 1.653 | 17.0 | 7616 | 2.0259 | 0.6124 | |
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| 1.5953 | 18.0 | 8064 | 1.9913 | 0.6207 | |
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| 1.6074 | 19.0 | 8512 | 1.9798 | 0.6157 | |
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| 1.6074 | 20.0 | 8960 | 2.0234 | 0.6033 | |
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| 1.5749 | 21.0 | 9408 | 1.9686 | 0.6197 | |
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| 1.535 | 22.0 | 9856 | 2.0068 | 0.6163 | |
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| 1.4942 | 23.0 | 10304 | 1.9486 | 0.6310 | |
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| 1.4765 | 24.0 | 10752 | 1.9502 | 0.6304 | |
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| 1.4558 | 25.0 | 11200 | 1.9509 | 0.6328 | |
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| 1.4617 | 26.0 | 11648 | 1.9903 | 0.6196 | |
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| 1.4224 | 27.0 | 12096 | 1.9849 | 0.6321 | |
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| 1.4019 | 28.0 | 12544 | 1.9781 | 0.6193 | |
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| 1.4019 | 29.0 | 12992 | 2.0661 | 0.6145 | |
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| 1.3624 | 30.0 | 13440 | 1.9948 | 0.6191 | |
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| 1.3517 | 31.0 | 13888 | 1.9117 | 0.6392 | |
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| 1.3613 | 32.0 | 14336 | 2.0300 | 0.6176 | |
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| 1.3428 | 33.0 | 14784 | 2.0005 | 0.6226 | |
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| 1.3257 | 34.0 | 15232 | 2.0079 | 0.6149 | |
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| 1.3127 | 35.0 | 15680 | 2.0231 | 0.6213 | |
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| 1.289 | 36.0 | 16128 | 1.9961 | 0.6296 | |
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| 1.2689 | 37.0 | 16576 | 1.9930 | 0.6221 | |
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| 1.2651 | 38.0 | 17024 | 1.9675 | 0.6314 | |
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| 1.2651 | 39.0 | 17472 | 1.9835 | 0.6220 | |
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| 1.2638 | 40.0 | 17920 | nan | 0.6275 | |
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| 1.235 | 41.0 | 18368 | 2.0100 | 0.6299 | |
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| 1.2239 | 42.0 | 18816 | 2.0384 | 0.6152 | |
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| 1.2147 | 43.0 | 19264 | 2.0421 | 0.6209 | |
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| 1.1961 | 44.0 | 19712 | 2.0041 | 0.6212 | |
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| 1.1988 | 45.0 | 20160 | 1.9905 | 0.6230 | |
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| 1.2007 | 46.0 | 20608 | 2.0222 | 0.6275 | |
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| 1.2029 | 47.0 | 21056 | 1.9856 | 0.6361 | |
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| 1.1779 | 48.0 | 21504 | 2.0348 | 0.6184 | |
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| 1.1779 | 49.0 | 21952 | 1.9196 | 0.6324 | |
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| 1.1973 | 50.0 | 22400 | 1.9935 | 0.6325 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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