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--- |
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license: mit |
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base_model: microsoft/deberta-v3-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: deberta-v3-base_finetuned_bluegennx_run2.19_2e |
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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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# deberta-v3-base_finetuned_bluegennx_run2.19_2e |
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0201 |
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- Overall Precision: 0.9745 |
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- Overall Recall: 0.9862 |
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- Overall F1: 0.9803 |
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- Overall Accuracy: 0.9952 |
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- Aadhar Card F1: 0.9837 |
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- Age F1: 0.9633 |
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- City F1: 0.9842 |
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- Country F1: 0.9843 |
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- Creditcardcvv F1: 0.9879 |
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- Creditcardnumber F1: 0.9416 |
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- Date F1: 0.9600 |
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- Dateofbirth F1: 0.9023 |
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- Email F1: 0.9900 |
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- Expirydate F1: 0.9912 |
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- Organization F1: 0.9910 |
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- Pan Card F1: 0.9867 |
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- Person F1: 0.9878 |
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- Phonenumber F1: 0.9858 |
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- Pincode F1: 0.9907 |
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- Secondaryaddress F1: 0.9878 |
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- State F1: 0.9909 |
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- Time F1: 0.9820 |
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- Url F1: 0.9949 |
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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: 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: cosine_with_restarts |
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- lr_scheduler_warmup_ratio: 0.2 |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Aadhar Card F1 | Age F1 | City F1 | Country F1 | Creditcardcvv F1 | Creditcardnumber F1 | Date F1 | Dateofbirth F1 | Email F1 | Expirydate F1 | Organization F1 | Pan Card F1 | Person F1 | Phonenumber F1 | Pincode F1 | Secondaryaddress F1 | State F1 | Time F1 | Url F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:------:|:-------:|:----------:|:----------------:|:-------------------:|:-------:|:--------------:|:--------:|:-------------:|:---------------:|:-----------:|:---------:|:--------------:|:----------:|:-------------------:|:--------:|:-------:|:------:| |
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| 0.0261 | 1.0 | 15321 | 0.0287 | 0.9619 | 0.9781 | 0.9700 | 0.9934 | 0.9613 | 0.9463 | 0.9541 | 0.9832 | 0.9793 | 0.9270 | 0.9481 | 0.8767 | 0.9793 | 0.9809 | 0.9882 | 0.9751 | 0.9840 | 0.9747 | 0.9835 | 0.9831 | 0.9620 | 0.9780 | 0.9873 | |
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| 0.0152 | 2.0 | 30642 | 0.0201 | 0.9745 | 0.9862 | 0.9803 | 0.9952 | 0.9837 | 0.9633 | 0.9842 | 0.9843 | 0.9879 | 0.9416 | 0.9600 | 0.9023 | 0.9900 | 0.9912 | 0.9910 | 0.9867 | 0.9878 | 0.9858 | 0.9907 | 0.9878 | 0.9909 | 0.9820 | 0.9949 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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