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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: 0.6038 |
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- Precision: 0.7039 |
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- Recall: 0.9283 |
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- F1 and accuracy: {'accuracy': 0.7017994858611826, 'f1': 0.8006872852233677} |
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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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| No log | 1.0 | 388 | 0.6457 | 0.6452 | 1.0 | {'accuracy': 0.6452442159383034, 'f1': 0.784375} | |
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| 0.6427 | 2.0 | 776 | 0.6410 | 0.6452 | 1.0 | {'accuracy': 0.6452442159383034, 'f1': 0.784375} | |
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| 0.6298 | 3.0 | 1164 | 0.6290 | 0.6486 | 1.0 | {'accuracy': 0.6503856041131105, 'f1': 0.786833855799373} | |
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| 0.6216 | 4.0 | 1552 | 0.6243 | 0.6552 | 0.9841 | {'accuracy': 0.6555269922879178, 'f1': 0.7866242038216561} | |
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| 0.6216 | 5.0 | 1940 | 0.6148 | 0.6799 | 0.9562 | {'accuracy': 0.6812339331619537, 'f1': 0.7947019867549668} | |
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| 0.6091 | 6.0 | 2328 | 0.6131 | 0.6791 | 0.9442 | {'accuracy': 0.6760925449871465, 'f1': 0.79} | |
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| 0.5892 | 7.0 | 2716 | 0.6102 | 0.6871 | 0.9363 | {'accuracy': 0.6838046272493573, 'f1': 0.7925801011804384} | |
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| 0.5818 | 8.0 | 3104 | 0.6051 | 0.6997 | 0.9283 | {'accuracy': 0.6966580976863753, 'f1': 0.7979452054794521} | |
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| 0.5818 | 9.0 | 3492 | 0.6061 | 0.6953 | 0.9363 | {'accuracy': 0.6940874035989717, 'f1': 0.797962648556876} | |
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| 0.5766 | 10.0 | 3880 | 0.6038 | 0.7039 | 0.9283 | {'accuracy': 0.7017994858611826, 'f1': 0.8006872852233677} | |
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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 0.15.1 |
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