| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: gitlab-mr-analysis-default-model |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # gitlab-mr-analysis-default-model |
|
|
| This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3356 |
| - Accuracy: 94.72759226713534% |
| - F1: 0.9612 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 16 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:------------------:|:------:| |
| | No log | 1.0 | 284 | 0.4056 | 85.76449912126537% | 0.7346 | |
| | 0.4789 | 2.0 | 568 | 0.2722 | 91.47627416520211% | 0.9335 | |
| | 0.4789 | 3.0 | 852 | 0.2413 | 94.37609841827768% | 0.9592 | |
| | 0.1185 | 4.0 | 1137 | 0.2776 | 94.37609841827768% | 0.9574 | |
| | 0.1185 | 5.0 | 1421 | 0.3132 | 93.84885764499121% | 0.9472 | |
| | 0.0378 | 6.0 | 1705 | 0.3323 | 94.28822495606327% | 0.9582 | |
| | 0.0378 | 7.0 | 1989 | 0.3393 | 94.28822495606327% | 0.9575 | |
| | 0.0123 | 8.0 | 2274 | 0.3363 | 94.5518453427065% | 0.9607 | |
| | 0.0077 | 9.0 | 2558 | 0.3333 | 94.63971880492092% | 0.9606 | |
| | 0.0077 | 9.99 | 2840 | 0.3356 | 94.72759226713534% | 0.9612 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.29.2 |
| - Pytorch 2.0.1 |
| - Datasets 2.14.4 |
| - Tokenizers 0.13.2 |
| |
