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--- |
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library_name: transformers |
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license: mit |
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base_model: intfloat/multilingual-e5-large-instruct |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: e5_Dechets_MultiLabel_08092025 |
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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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# e5_Dechets_MultiLabel_08092025 |
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This model is a fine-tuned version of [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1811 |
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- F1 Weighted: 0.9499 |
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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-06 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 15 |
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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 | F1 Weighted | |
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|:-------------:|:-----:|:----:|:---------------:|:-----------:| |
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| 0.8583 | 1.0 | 199 | 0.5295 | 0.7662 | |
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| 0.4704 | 2.0 | 398 | 0.3538 | 0.8660 | |
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| 0.3289 | 3.0 | 597 | 0.2997 | 0.8879 | |
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| 0.2543 | 4.0 | 796 | 0.2687 | 0.9014 | |
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| 0.2075 | 5.0 | 995 | 0.2480 | 0.9182 | |
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| 0.1755 | 6.0 | 1194 | 0.2326 | 0.9248 | |
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| 0.1486 | 7.0 | 1393 | 0.2227 | 0.9290 | |
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| 0.1285 | 8.0 | 1592 | 0.2051 | 0.9391 | |
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| 0.1114 | 9.0 | 1791 | 0.1907 | 0.9434 | |
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| 0.1007 | 10.0 | 1990 | 0.1889 | 0.9459 | |
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| 0.0896 | 11.0 | 2189 | 0.1811 | 0.9499 | |
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### Framework versions |
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- Transformers 4.56.0 |
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- Pytorch 2.8.0+cu126 |
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- Datasets 4.0.0 |
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- Tokenizers 0.22.0 |
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