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--- |
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library_name: transformers |
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base_model: MiMe-MeMo/MeMo-BERT-03 |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: memo3_indirect_speech |
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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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# memo3_indirect_speech |
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This model is a fine-tuned version of [MiMe-MeMo/MeMo-BERT-03](https://huggingface.co/MiMe-MeMo/MeMo-BERT-03) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Accuracy: 0.6840 |
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- Precision: 0.7036 |
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- Recall: 0.6840 |
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- F1: 0.6798 |
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- Loss: 0.7225 |
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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: 5e-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: 2 |
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- total_train_batch_size: 8 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | Precision | Recall | F1 | Validation Loss | |
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|:-------------:|:-------:|:----:|:--------:|:---------:|:------:|:------:|:---------------:| |
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| No log | 1.0 | 9 | 0.3820 | 0.1459 | 0.3820 | 0.2112 | 1.3936 | |
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| No log | 2.0 | 18 | 0.3820 | 0.1459 | 0.3820 | 0.2112 | 1.5289 | |
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| No log | 3.0 | 27 | 0.5385 | 0.2900 | 0.5385 | 0.3770 | 1.0207 | |
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| No log | 4.0 | 36 | 0.3820 | 0.1459 | 0.3820 | 0.2112 | 1.3341 | |
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| No log | 5.0 | 45 | 0.5385 | 0.2900 | 0.5385 | 0.3770 | 1.0219 | |
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| No log | 6.0 | 54 | 0.3829 | 0.1825 | 0.3829 | 0.2130 | 1.0394 | |
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| No log | 7.0 | 63 | 0.6020 | 0.5949 | 0.6020 | 0.5650 | 0.8312 | |
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| No log | 8.0 | 72 | 0.6250 | 0.6270 | 0.6250 | 0.5846 | 0.8301 | |
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| No log | 9.0 | 81 | 0.6821 | 0.7037 | 0.6821 | 0.6491 | 0.7462 | |
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| No log | 10.0 | 90 | 0.5620 | 0.6878 | 0.5620 | 0.5268 | 0.8255 | |
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| No log | 11.0 | 99 | 0.5968 | 0.6945 | 0.5968 | 0.5751 | 0.7890 | |
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| No log | 12.0 | 108 | 0.6901 | 0.6877 | 0.6901 | 0.6868 | 0.7190 | |
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| No log | 13.0 | 117 | 0.6020 | 0.7003 | 0.6020 | 0.5805 | 0.8426 | |
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| No log | 14.0 | 126 | 0.6932 | 0.6919 | 0.6932 | 0.6899 | 0.7213 | |
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| No log | 15.0 | 135 | 0.7158 | 0.7300 | 0.7158 | 0.6928 | 0.7416 | |
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| No log | 16.0 | 144 | 0.6503 | 0.7054 | 0.6503 | 0.6417 | 0.7546 | |
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| No log | 17.0 | 153 | 0.7031 | 0.7104 | 0.7031 | 0.6993 | 0.6824 | |
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| No log | 17.8235 | 160 | 0.6840 | 0.7036 | 0.6840 | 0.6798 | 0.7225 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.5.1+cu124 |
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- Tokenizers 0.21.0 |
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