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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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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: gitlab-mr-analysis-default-model |
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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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# gitlab-mr-analysis-default-model |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3356 |
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- Accuracy: 94.72759226713534% |
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- F1: 0.9612 |
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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: 2e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------------------:|:------:| |
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| No log | 1.0 | 284 | 0.4056 | 85.76449912126537% | 0.7346 | |
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| 0.4789 | 2.0 | 568 | 0.2722 | 91.47627416520211% | 0.9335 | |
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| 0.4789 | 3.0 | 852 | 0.2413 | 94.37609841827768% | 0.9592 | |
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| 0.1185 | 4.0 | 1137 | 0.2776 | 94.37609841827768% | 0.9574 | |
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| 0.1185 | 5.0 | 1421 | 0.3132 | 93.84885764499121% | 0.9472 | |
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| 0.0378 | 6.0 | 1705 | 0.3323 | 94.28822495606327% | 0.9582 | |
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| 0.0378 | 7.0 | 1989 | 0.3393 | 94.28822495606327% | 0.9575 | |
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| 0.0123 | 8.0 | 2274 | 0.3363 | 94.5518453427065% | 0.9607 | |
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| 0.0077 | 9.0 | 2558 | 0.3333 | 94.63971880492092% | 0.9606 | |
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| 0.0077 | 9.99 | 2840 | 0.3356 | 94.72759226713534% | 0.9612 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.2 |
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