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distilbert-base-uncased-finetuned-ingredients

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Files changed (6) hide show
  1. README.md +17 -17
  2. pytorch_model.bin +1 -1
  3. runs/Sep11_09-17-15_7da8e3196255/1631351966.3614156/events.out.tfevents.1631351966.7da8e3196255.76.1 +3 -0
  4. runs/Sep11_09-17-15_7da8e3196255/events.out.tfevents.1631351966.7da8e3196255.76.0 +3 -0
  5. runs/Sep11_09-17-15_7da8e3196255/events.out.tfevents.1631352026.7da8e3196255.76.2 +3 -0
  6. training_args.bin +1 -1
README.md CHANGED
@@ -22,16 +22,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.9865319865319865
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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: 0.9898648648648648
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  - name: Accuracy
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  type: accuracy
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- value: 0.9972885032537961
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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
@@ -41,11 +41,11 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.0100
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- - Precision: 0.9865
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  - Recall: 0.9932
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- - F1: 0.9899
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- - Accuracy: 0.9973
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  ## Model description
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@@ -76,16 +76,16 @@ The following hyperparameters were used during training:
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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 | 0.2822 | 0.4075 | 0.5525 | 0.4691 | 0.8932 |
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- | No log | 2.0 | 94 | 0.0972 | 0.8429 | 0.8915 | 0.8666 | 0.9783 |
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- | No log | 3.0 | 141 | 0.0223 | 0.9865 | 0.9932 | 0.9899 | 0.9973 |
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- | No log | 4.0 | 188 | 0.0170 | 0.9798 | 0.9864 | 0.9831 | 0.9962 |
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- | No log | 5.0 | 235 | 0.0136 | 0.9865 | 0.9932 | 0.9899 | 0.9973 |
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- | No log | 6.0 | 282 | 0.0124 | 0.9865 | 0.9932 | 0.9899 | 0.9973 |
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- | No log | 7.0 | 329 | 0.0121 | 0.9865 | 0.9932 | 0.9899 | 0.9973 |
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- | No log | 8.0 | 376 | 0.0118 | 0.9865 | 0.9932 | 0.9899 | 0.9973 |
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- | No log | 9.0 | 423 | 0.0107 | 0.9865 | 0.9932 | 0.9899 | 0.9973 |
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- | No log | 10.0 | 470 | 0.0100 | 0.9865 | 0.9932 | 0.9899 | 0.9973 |
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  ### Framework versions
 
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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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  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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  | 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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