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electra-base-ner-food-recipe-v2

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update model card README.md

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  tags:
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  - generated_from_trainer
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  metrics:
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  # electra-base-ner-food-recipe-v2
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- This model was trained from scratch on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1709
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- - Precision: 0.8007
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- - Recall: 0.8867
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- - F1: 0.8415
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- - Accuracy: 0.9669
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 5e-06
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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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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 0.5 | 400 | 0.1420 | 0.7862 | 0.8871 | 0.8336 | 0.9655 |
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- | 0.101 | 1.01 | 800 | 0.1580 | 0.8317 | 0.8817 | 0.8559 | 0.9716 |
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- | 0.0966 | 1.51 | 1200 | 0.1467 | 0.8105 | 0.8917 | 0.8492 | 0.9693 |
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- | 0.0849 | 2.01 | 1600 | 0.1408 | 0.7966 | 0.8771 | 0.8349 | 0.9669 |
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- | 0.085 | 2.51 | 2000 | 0.1487 | 0.7941 | 0.8880 | 0.8384 | 0.9662 |
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- | 0.085 | 3.02 | 2400 | 0.1477 | 0.7773 | 0.8867 | 0.8284 | 0.9635 |
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- | 0.0766 | 3.52 | 2800 | 0.1852 | 0.8298 | 0.8807 | 0.8545 | 0.9710 |
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- | 0.0725 | 4.02 | 3200 | 0.1674 | 0.8073 | 0.8830 | 0.8435 | 0.9679 |
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- | 0.069 | 4.52 | 3600 | 0.1709 | 0.8007 | 0.8867 | 0.8415 | 0.9669 |
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  ### Framework versions
 
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+ license: apache-2.0
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  tags:
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  - generated_from_trainer
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  metrics:
 
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  # electra-base-ner-food-recipe-v2
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+ This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1500
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+ - Precision: 0.7191
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+ - Recall: 0.8739
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+ - F1: 0.7890
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+ - Accuracy: 0.9568
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 5e-07
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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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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 0.5 | 400 | 0.4360 | 0.4354 | 0.7533 | 0.5519 | 0.8775 |
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+ | 0.5627 | 1.01 | 800 | 0.2274 | 0.6971 | 0.8525 | 0.7670 | 0.9508 |
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+ | 0.2799 | 1.51 | 1200 | 0.1791 | 0.6728 | 0.8762 | 0.7612 | 0.9492 |
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+ | 0.1983 | 2.01 | 1600 | 0.1652 | 0.6958 | 0.8757 | 0.7755 | 0.9535 |
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+ | 0.1821 | 2.51 | 2000 | 0.1610 | 0.7171 | 0.8766 | 0.7889 | 0.9568 |
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+ | 0.1821 | 3.02 | 2400 | 0.1550 | 0.7001 | 0.8757 | 0.7782 | 0.9539 |
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+ | 0.1726 | 3.52 | 2800 | 0.1537 | 0.7211 | 0.8744 | 0.7904 | 0.9573 |
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+ | 0.1674 | 4.02 | 3200 | 0.1510 | 0.7170 | 0.8739 | 0.7877 | 0.9565 |
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+ | 0.1682 | 4.52 | 3600 | 0.1501 | 0.7147 | 0.8744 | 0.7865 | 0.9564 |
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  ### Framework versions