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--- |
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license: apache-2.0 |
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base_model: distilbert/distilbert-base-multilingual-cased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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model-index: |
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- name: distilbert-base-multilingual-cased-lora-text-classification |
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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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# distilbert-base-multilingual-cased-lora-text-classification |
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This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-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: 0.5321 |
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- Precision: 0.7883 |
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- Recall: 0.8589 |
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- F1 and accuracy: {'accuracy': 0.7487113402061856, 'f1': 0.8220802919708029} |
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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: 1e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 and accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:----------------------------------------------------------:| |
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| 0.6034 | 1.0 | 1552 | 0.5999 | 0.6781 | 0.9981 | {'accuracy': 0.678479381443299, 'f1': 0.8075588121866564} | |
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| 0.5756 | 2.0 | 3104 | 0.5892 | 0.7067 | 0.9418 | {'accuracy': 0.696520618556701, 'f1': 0.8075194115243155} | |
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| 0.5607 | 3.0 | 4656 | 0.5630 | 0.7449 | 0.8770 | {'accuracy': 0.7139175257731959, 'f1': 0.8056042031523644} | |
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| 0.5458 | 4.0 | 6208 | 0.5549 | 0.7544 | 0.8990 | {'accuracy': 0.7338917525773195, 'f1': 0.8203566768160069} | |
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| 0.5342 | 5.0 | 7760 | 0.5816 | 0.7381 | 0.9457 | {'accuracy': 0.7364690721649485, 'f1': 0.8290848307563727} | |
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| 0.5266 | 6.0 | 9312 | 0.5399 | 0.7705 | 0.8799 | {'accuracy': 0.7416237113402062, 'f1': 0.8215398308856252} | |
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| 0.519 | 7.0 | 10864 | 0.5315 | 0.7932 | 0.8408 | {'accuracy': 0.7442010309278351, 'f1': 0.8162887552059231} | |
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| 0.4878 | 8.0 | 12416 | 0.5318 | 0.7880 | 0.8541 | {'accuracy': 0.7461340206185567, 'f1': 0.8197621225983532} | |
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| 0.485 | 9.0 | 13968 | 0.5332 | 0.7851 | 0.8637 | {'accuracy': 0.7480670103092784, 'f1': 0.8225147526100772} | |
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| 0.5044 | 10.0 | 15520 | 0.5321 | 0.7883 | 0.8589 | {'accuracy': 0.7487113402061856, 'f1': 0.8220802919708029} | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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