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--- |
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base_model: intfloat/multilingual-e5-base |
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license: mit |
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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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tags: |
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- generated_from_trainer |
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model-index: |
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- name: multilingual-e5-base_censor_v0.1 |
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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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# multilingual-e5-base_censor_v0.1 |
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This model is a fine-tuned version of [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5077 |
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- Accuracy: 0.7695 |
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- Precision: 0.7729 |
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- Recall: 0.7695 |
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- F1: 0.7708 |
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- Roc Auc: 0.8424 |
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- Per Class F1: [0.8104358705451601, 0.7061407261629732] |
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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: 64 |
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- eval_batch_size: 64 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 3 |
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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 | Precision | Recall | F1 | Roc Auc | Per Class F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|:----------------------------------------:| |
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| 0.5485 | 1.0 | 2795 | 0.5249 | 0.7349 | 0.7498 | 0.7349 | 0.7383 | 0.8169 | [0.7723692804179954, 0.6827648980028912] | |
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| 0.4871 | 2.0 | 5590 | 0.5059 | 0.7610 | 0.7667 | 0.7610 | 0.7629 | 0.8362 | [0.8013980033834657, 0.7000926419808541] | |
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| 0.4392 | 3.0 | 8385 | 0.5077 | 0.7695 | 0.7729 | 0.7695 | 0.7708 | 0.8424 | [0.8104358705451601, 0.7061407261629732] | |
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### Framework versions |
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- Transformers 4.43.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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