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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: distilbert-base-uncased-distilled-squad |
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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: gmra_model_distilbert-base-uncased-distilled-squad_07112024T110436 |
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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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# gmra_model_distilbert-base-uncased-distilled-squad_07112024T110436 |
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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: 0.3023 |
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- Accuracy: 94.1125 |
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- F1: 0.9587 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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 | 0.9982 | 142 | 0.3683 | 88.0492 | 0.7519 | |
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| No log | 1.9965 | 284 | 0.2634 | 91.5641 | 0.9238 | |
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| No log | 2.9947 | 426 | 0.2386 | 92.8822 | 0.9432 | |
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| 0.3507 | 4.0 | 569 | 0.2321 | 93.9367 | 0.9579 | |
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| 0.3507 | 4.9982 | 711 | 0.2897 | 93.4095 | 0.9536 | |
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| 0.3507 | 5.9965 | 853 | 0.2745 | 94.2882 | 0.9606 | |
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| 0.3507 | 6.9947 | 995 | 0.2892 | 94.3761 | 0.9616 | |
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| 0.0379 | 8.0 | 1138 | 0.3055 | 94.0246 | 0.9579 | |
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| 0.0379 | 8.9982 | 1280 | 0.3144 | 93.7610 | 0.9562 | |
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| 0.0379 | 9.9824 | 1420 | 0.3023 | 94.1125 | 0.9587 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.19.1 |
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