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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- ingredients_yes_no |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: distilbert-base-uncased-finetuned-ingredients |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: ingredients_yes_no |
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type: ingredients_yes_no |
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args: IngredientsYesNo |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9898648648648649 |
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- name: Recall |
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type: recall |
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value: 0.9932203389830508 |
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- name: F1 |
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type: f1 |
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value: 0.9915397631133671 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9978308026030369 |
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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-uncased-finetuned-ingredients |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ingredients_yes_no dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0105 |
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- Precision: 0.9899 |
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- Recall: 0.9932 |
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- F1: 0.9915 |
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- Accuracy: 0.9978 |
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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: 16 |
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- eval_batch_size: 16 |
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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 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 47 | 0.2783 | 0.4 | 0.5492 | 0.4629 | 0.8910 | |
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| No log | 2.0 | 94 | 0.1089 | 0.8145 | 0.8780 | 0.8450 | 0.9718 | |
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| No log | 3.0 | 141 | 0.0273 | 0.9865 | 0.9932 | 0.9899 | 0.9973 | |
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| No log | 4.0 | 188 | 0.0168 | 0.9865 | 0.9932 | 0.9899 | 0.9973 | |
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| No log | 5.0 | 235 | 0.0156 | 0.9865 | 0.9898 | 0.9882 | 0.9957 | |
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| No log | 6.0 | 282 | 0.0129 | 0.9865 | 0.9932 | 0.9899 | 0.9973 | |
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| No log | 7.0 | 329 | 0.0121 | 0.9899 | 0.9932 | 0.9915 | 0.9978 | |
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| No log | 8.0 | 376 | 0.0115 | 0.9899 | 0.9932 | 0.9915 | 0.9978 | |
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| No log | 9.0 | 423 | 0.0108 | 0.9899 | 0.9932 | 0.9915 | 0.9978 | |
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| No log | 10.0 | 470 | 0.0105 | 0.9899 | 0.9932 | 0.9915 | 0.9978 | |
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### Framework versions |
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- Transformers 4.10.2 |
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- Pytorch 1.9.0+cu102 |
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- Datasets 1.11.0 |
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- Tokenizers 0.10.3 |
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