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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: multi-label-class-classification-on-github-issues |
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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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# multi-label-class-classification-on-github-issues |
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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: 0.3785 |
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- Micro f1: 0.5031 |
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- Macro f1: 0.0317 |
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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: 3e-05 |
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- train_batch_size: 512 |
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- eval_batch_size: 8 |
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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 | Micro f1 | Macro f1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| |
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| No log | 1.0 | 4 | 0.6548 | 0.2035 | 0.0412 | |
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| No log | 2.0 | 8 | 0.5953 | 0.2747 | 0.0395 | |
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| No log | 3.0 | 12 | 0.5509 | 0.3474 | 0.0376 | |
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| No log | 4.0 | 16 | 0.5159 | 0.4098 | 0.0375 | |
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| No log | 5.0 | 20 | 0.4859 | 0.4842 | 0.0362 | |
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| No log | 6.0 | 24 | 0.4625 | 0.4875 | 0.0315 | |
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| No log | 7.0 | 28 | 0.4434 | 0.4893 | 0.0315 | |
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| No log | 8.0 | 32 | 0.4268 | 0.4906 | 0.0315 | |
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| No log | 9.0 | 36 | 0.4134 | 0.4920 | 0.0306 | |
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| No log | 10.0 | 40 | 0.4027 | 0.4943 | 0.0308 | |
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| No log | 11.0 | 44 | 0.3939 | 0.4952 | 0.0309 | |
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| No log | 12.0 | 48 | 0.3872 | 0.4986 | 0.0312 | |
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| No log | 13.0 | 52 | 0.3824 | 0.5025 | 0.0316 | |
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| No log | 14.0 | 56 | 0.3796 | 0.5033 | 0.0317 | |
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| No log | 15.0 | 60 | 0.3785 | 0.5031 | 0.0317 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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