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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: bert-base-uncased-issues-128 |
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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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# bert-base-uncased-issues-128 |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GitHub issues dataset. The model is used in Chapter 9: Dealing with Few to No Labels in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/09_few-to-no-labels.ipynb). |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2520 |
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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: 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: 16 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 2.0949 | 1.0 | 291 | 1.7072 | |
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| 1.649 | 2.0 | 582 | 1.4409 | |
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| 1.4835 | 3.0 | 873 | 1.4099 | |
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| 1.3938 | 4.0 | 1164 | 1.3858 | |
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| 1.3326 | 5.0 | 1455 | 1.2004 | |
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| 1.2949 | 6.0 | 1746 | 1.2955 | |
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| 1.2451 | 7.0 | 2037 | 1.2682 | |
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| 1.1992 | 8.0 | 2328 | 1.1938 | |
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| 1.1784 | 9.0 | 2619 | 1.1686 | |
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| 1.1397 | 10.0 | 2910 | 1.2050 | |
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| 1.1293 | 11.0 | 3201 | 1.2058 | |
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| 1.1006 | 12.0 | 3492 | 1.1680 | |
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| 1.0835 | 13.0 | 3783 | 1.2414 | |
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| 1.0757 | 14.0 | 4074 | 1.1522 | |
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| 1.062 | 15.0 | 4365 | 1.1176 | |
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| 1.0535 | 16.0 | 4656 | 1.2520 | |
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### Framework versions |
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- Transformers 4.11.3 |
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- Pytorch 1.10.0+cu102 |
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- Datasets 1.13.0 |
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- Tokenizers 0.10.3 |
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