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--- |
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dataset_info: |
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features: |
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- name: model_type |
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dtype: string |
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- name: namespace |
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dtype: string |
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- name: model_name |
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dtype: string |
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- name: training_method |
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dtype: string |
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- name: model_size |
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dtype: int64 |
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- name: trainable_params |
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dtype: int64 |
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- name: url |
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dtype: string |
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- name: doi |
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dtype: float64 |
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splits: |
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- name: train |
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num_bytes: 6257 |
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num_examples: 40 |
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download_size: 4879 |
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dataset_size: 6257 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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pretty_name: PEFT Unit Test Generation Experiments |
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size_categories: |
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- n<1K |
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--- |
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# PEFT Unit Test Generation Experiments |
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## Dataset description |
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The **PEFT Unit Test Generation Experiments** dataset contains metadata and details about a set of trained models used for generating unit tests with parameter-efficient fine-tuning (PEFT) methods. This dataset includes models from multiple namespaces and various sizes, trained with different tuning methods to provide a comprehensive resource for unit test generation research. |
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## Dataset Structure |
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### Data Fields |
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Each example in the dataset corresponds to a specific trained model variant and includes the following features: |
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| Feature Name | Description | |
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| `model_type` | The type or architecture of the base model (e.g., codegen, starcoder). | |
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| `namespace` | The organization or group that created or published the base model (e.g., Salesforce, meta-llama). | |
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| `model_name` | The specific name or identifier of the model. | |
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| `training_method` | The parameter-efficient fine-tuning method used for training (e.g., full fine-tuning, LoRA, IA³). | |
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| `model_size` | The size of the model, typically measured in number of parameters (e.g., 350M, 7B). | |
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| `trainable_params`| The number of trainable parameters for the specific tuning method and [hyperparameters](#training-hyperparameters). | |
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| `url` | A direct link to the model repository. | |
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| `doi` | The digital object identifier associated with the trained model. | |
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## Dataset Details |
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### Dataset Description |
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<!-- Provide a longer summary of what this dataset is. --> |
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### Training Hyperparameters |
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#### Model-agnostic Hyperparameters |
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<table> |
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<thead> |
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<tr> |
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<th>Hyperparameter</th> |
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<th>Method</th> |
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<th>Value</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr style="font-weight: bold;"> |
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<td colspan="3">Common</td> |
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</tr> |
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<tr> |
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<td>Optimizer</td> |
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<td>-</td> |
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<td>AdamW</td> |
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</tr> |
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<tr> |
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<td>LR schedule</td> |
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<td>-</td> |
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<td>Linear</td> |
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</tr> |
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<tr> |
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<td>LR warmup ratio</td> |
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<td>-</td> |
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<td>0.1</td> |
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</tr> |
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<tr> |
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<td>Batch size</td> |
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<td>-</td> |
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<td>1</td> |
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</tr> |
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<tr> |
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<td>Gradient accumulation steps</td> |
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<td>-</td> |
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<td>8</td> |
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</tr> |
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<tr> |
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<td># Epochs</td> |
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<td>-</td> |
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<td>3</td> |
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</tr> |
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<tr> |
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<td>Precision</td> |
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<td>-</td> |
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<td>Mixed</td> |
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</tr> |
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<tr> |
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<td style="vertical-align: middle;" rowspan="4">Learning rate</td> |
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<td>Full fine-tuning</td> |
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<td>5E-5</td> |
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</tr> |
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<tr> |
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<td>LoRA</td> |
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<td>3E-4</td> |
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</tr> |
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<tr> |
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<td>(IA)<sup>3</sup></td> |
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<td>3E-4</td> |
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</tr> |
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<tr> |
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<td>Prompt tuning</td> |
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<td>3E-3</td> |
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</tr> |
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<tr style="font-weight: bold;"> |
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<td colspan="3">Method specific</td> |
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</tr> |
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<tr> |
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<td>Alpha</td> |
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<td>LoRA</td> |
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<td>32</td> |
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</tr> |
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<tr> |
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<td>Dropout</td> |
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<td>LoRA</td> |
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<td>0.1</td> |
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</tr> |
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<tr> |
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<td>Rank</td> |
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<td>LoRA</td> |
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<td>16</td> |
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</tr> |
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<tr> |
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<td>Virtual tokens</td> |
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<td>Prompt tuning</td> |
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<td>20</td> |
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</tr> |
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</tbody> |
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</table> |
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#### Model-specific Hyperparameters |
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<table> |
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<thead> |
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<tr> |
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<th>Hyperparameter</th> |
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<th>Method</th> |
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<th>Model</th> |
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<th>Value</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr> |
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<td rowspan="10" style="vertical-align: middle;">Targeted attention modules</td> |
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<td rowspan="10" style="vertical-align: middle;">LoRA, (IA)<sup>3</sup></td> |
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<td>codegen-350M-multi</td> |
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<td>qkv_proj</td> |
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</tr> |
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<tr><td>Salesforce/codegen2-1B_P</td><td>qkv_proj</td></tr> |
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<tr><td>Salesforce/codegen2-3_7B_P</td><td>qkv_proj</td></tr> |
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<tr><td>Salesforce/codegen2-7B_P</td><td>qkv_proj</td></tr> |
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<tr><td>Salesforce/codegen2-16B_P</td><td>qkv_proj</td></tr> |
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<tr><td>meta-llama/CodeLlama-7b-hf</td><td>q_proj, v_proj</td></tr> |
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<tr><td>bigcode/starcoderbase</td><td>c_attn</td></tr> |
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<tr><td>bigcode/starcoder2-3b</td><td>q_proj, v_proj</td></tr> |
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<tr><td>bigcode/starcoder2-7b</td><td>q_proj, v_proj</td></tr> |
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<tr><td>bigcode/starcoder2-15b</td><td>q_proj, v_proj</td></tr> |
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<tr> |
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<td rowspan="10" style="vertical-align: middle;">Targeted feedforward modules</td> |
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<td rowspan="10" style="vertical-align: middle;">(IA)<sup>3</sup></td> |
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<td>codegen-350M-multi</td> |
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<td>fc_out</td> |
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</tr> |
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<tr><td>Salesforce/codegen2-1B_P</td><td>fc_out</td></tr> |
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<tr><td>Salesforce/codegen2-3_7B_P</td><td>fc_out</td></tr> |
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<tr><td>Salesforce/codegen2-7B_P</td><td>fc_out</td></tr> |
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<tr><td>Salesforce/codegen2-16B_P</td><td>fc_out</td></tr> |
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<tr><td>meta-llama/CodeLlama-7b-hf</td><td>down_proj</td></tr> |
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<tr><td>bigcode/starcoderbase</td><td>mlp.c_proj</td></tr> |
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<tr><td>bigcode/starcoder2-3b</td><td>q_proj, c_proj</td></tr> |
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<tr><td>bigcode/starcoder2-7b</td><td>q_proj, c_proj</td></tr> |
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<tr><td>bigcode/starcoder2-15b</td><td>q_proj, c_proj</td></tr> |
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</tbody> |
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</table> |
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## Training Runs |
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