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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: 0.3836 |
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- Micro f1: 0.4888 |
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- Macro f1: 0.0304 |
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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 | 0.6376 | 0.1870 | 0.0280 | |
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| No log | 2.0 | 8 | 0.5847 | 0.1961 | 0.0154 | |
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| No log | 3.0 | 12 | 0.5465 | 0.1967 | 0.0146 | |
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| No log | 4.0 | 16 | 0.5148 | 0.1982 | 0.0146 | |
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| No log | 5.0 | 20 | 0.4878 | 0.2915 | 0.0187 | |
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| No log | 6.0 | 24 | 0.4653 | 0.4655 | 0.0301 | |
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| No log | 7.0 | 28 | 0.4465 | 0.4862 | 0.0305 | |
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| No log | 8.0 | 32 | 0.4310 | 0.4884 | 0.0305 | |
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| No log | 9.0 | 36 | 0.4179 | 0.4894 | 0.0305 | |
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| No log | 10.0 | 40 | 0.4072 | 0.4894 | 0.0305 | |
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| No log | 11.0 | 44 | 0.3986 | 0.4893 | 0.0305 | |
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| No log | 12.0 | 48 | 0.3921 | 0.4893 | 0.0305 | |
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| No log | 13.0 | 52 | 0.3875 | 0.4888 | 0.0304 | |
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| No log | 14.0 | 56 | 0.3847 | 0.4888 | 0.0304 | |
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| No log | 15.0 | 60 | 0.3836 | 0.4888 | 0.0304 | |
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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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