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--- |
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library_name: transformers |
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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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- f1 |
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- precision |
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model-index: |
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- name: bert-fraud-classification-test-mass |
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/sandeshrajx/ultron-nlp/runs/bim0galx) |
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# bert-fraud-classification-test-mass |
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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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- Loss: 0.3963 |
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- F1: 0.8194 |
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- Precision: 0.8445 |
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- Val Accuracy: 0.8375 |
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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-05 |
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- train_batch_size: 44 |
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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: 88 |
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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: 50 |
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- num_epochs: 2 |
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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 | Precision | Val Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:------:|:---------:|:------------:| |
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| 0.5197 | 0.1743 | 40 | 0.5468 | 0.7488 | 0.6907 | 0.7459 | |
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| 0.5208 | 0.3486 | 80 | 0.4667 | 0.7687 | 0.7890 | 0.7911 | |
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| 0.4235 | 0.5229 | 120 | 0.4351 | 0.7986 | 0.7898 | 0.8113 | |
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| 0.404 | 0.6972 | 160 | 0.4577 | 0.7972 | 0.7751 | 0.8066 | |
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| 0.3736 | 0.8715 | 200 | 0.4274 | 0.7914 | 0.8775 | 0.8240 | |
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| 0.419 | 1.0458 | 240 | 0.4058 | 0.7912 | 0.8737 | 0.8232 | |
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| 0.2701 | 1.2200 | 280 | 0.4075 | 0.8124 | 0.8393 | 0.8316 | |
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| 0.4345 | 1.3943 | 320 | 0.4246 | 0.8110 | 0.8088 | 0.8244 | |
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| 0.3258 | 1.5686 | 360 | 0.4023 | 0.7992 | 0.8788 | 0.8294 | |
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| 0.3938 | 1.7429 | 400 | 0.3945 | 0.8174 | 0.8447 | 0.8361 | |
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| 0.2529 | 1.9172 | 440 | 0.3963 | 0.8194 | 0.8445 | 0.8375 | |
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### Framework versions |
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- Transformers 4.46.0.dev0 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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