End of training
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README.md
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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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