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README.md
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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: google-bert/bert-base-multilingual-cased
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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: germanic_antielite
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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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# germanic_antielite
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This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2980
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- Accuracy: 0.9076
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- F1: 0.4851
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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: 32
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- eval_batch_size: 32
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- seed: 42
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- optimizer: Use 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: 3
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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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| 0.2286 | 1.0 | 351 | 0.2263 | 0.9090 | 0.4990 |
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| 0.1729 | 2.0 | 702 | 0.3197 | 0.9065 | 0.3105 |
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| 0.1106 | 3.0 | 1053 | 0.2980 | 0.9076 | 0.4851 |
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### Framework versions
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- Transformers 4.46.2
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- Pytorch 2.5.0+cu121
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- Datasets 3.1.0
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- Tokenizers 0.20.3
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