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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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model-index: |
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- name: memobert3_ED1 |
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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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# memobert3_ED1 |
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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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- Loss: 0.6065 |
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- F1-score: 0.9098 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1-score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 69 | 0.3808 | 0.8439 | |
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| No log | 2.0 | 138 | 0.4234 | 0.8432 | |
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| No log | 3.0 | 207 | 0.4577 | 0.9013 | |
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| No log | 4.0 | 276 | 0.6278 | 0.8851 | |
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| No log | 5.0 | 345 | 0.6449 | 0.8517 | |
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| No log | 6.0 | 414 | 0.7495 | 0.8678 | |
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| No log | 7.0 | 483 | 0.6065 | 0.9098 | |
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| 0.1663 | 8.0 | 552 | 0.6217 | 0.9098 | |
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| 0.1663 | 9.0 | 621 | 0.6420 | 0.9098 | |
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| 0.1663 | 10.0 | 690 | 0.6514 | 0.9098 | |
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| 0.1663 | 11.0 | 759 | 0.6627 | 0.9098 | |
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| 0.1663 | 12.0 | 828 | 0.6726 | 0.9098 | |
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| 0.1663 | 13.0 | 897 | 0.6828 | 0.9016 | |
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| 0.1663 | 14.0 | 966 | 0.6904 | 0.9016 | |
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| 0.0001 | 15.0 | 1035 | 0.6942 | 0.9016 | |
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| 0.0001 | 16.0 | 1104 | 0.6976 | 0.9016 | |
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| 0.0001 | 17.0 | 1173 | 0.7007 | 0.9016 | |
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| 0.0001 | 18.0 | 1242 | 0.7027 | 0.9016 | |
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| 0.0001 | 19.0 | 1311 | 0.7038 | 0.9016 | |
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| 0.0001 | 20.0 | 1380 | 0.7037 | 0.9016 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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