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--- |
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library_name: peft |
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license: apache-2.0 |
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base_model: google-bert/bert-base-multilingual-uncased |
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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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- precision |
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- recall |
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model-index: |
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- name: MBERT_uncased_CrossEntropyLoss_adalora |
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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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# MBERT_uncased_CrossEntropyLoss_adalora |
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This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Accuracy: 0.67 |
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- F1: 0.8005 |
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- Precision: 0.7118 |
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- Recall: 0.9144 |
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- Roc Auc: 0.4717 |
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- Loss: 0.6634 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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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 | Accuracy | F1 | Precision | Recall | Roc Auc | Validation Loss | |
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|:-------------:|:-----:|:----:|:--------:|:------:|:---------:|:------:|:-------:|:---------------:| |
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| No log | 0.992 | 31 | 0.636 | 0.7745 | 0.7022 | 0.8633 | 0.4516 | 0.6726 | |
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| No log | 1.984 | 62 | 0.662 | 0.7947 | 0.7093 | 0.9033 | 0.4662 | 0.6656 | |
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| No log | 2.976 | 93 | 0.67 | 0.8005 | 0.7118 | 0.9144 | 0.4717 | 0.6634 | |
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### Framework versions |
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- PEFT 0.13.3.dev0 |
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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 |
