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--- |
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base_model: bert-base-uncased |
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library_name: transformers |
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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: results |
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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 |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on an unknown dataset. |
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## Model description |
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This model is fine-tuned for classifying GitHub issues into four categories: New Feature, Improvement, Bug, and Task. The base model used is bert-large-uncased, and it has been trained on an open-source dataset of GitHub issues containing titles and descriptions. This model can efficiently predict the type of issue based on the input of the issue’s title and description. |
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### Fine-Tuning Details |
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Base Model: bert-large-uncased |
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Fine-Tuning Dataset: GitHub Issues with labels mapped to four categories: |
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- New Feature |
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- Improvement |
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- Bug |
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- Task |
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Training Framework: Hugging Face Transformers, PyTorch |
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Training Setup: The model was fine-tuned using a batch size of 64 for a few epochs, with a learning rate of 6e-5. |
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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: 6e-05 |
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- train_batch_size: 8 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 4 |
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### Training results |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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