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distilbert-base-uncased-finetuned-ingredients

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  1. README.md +19 -19
  2. pytorch_model.bin +1 -1
  3. runs/Sep01_13-56-51_b0698074cf9b/1630504739.460997/events.out.tfevents.1630504739.b0698074cf9b.77.1 +3 -0
  4. runs/Sep01_13-56-51_b0698074cf9b/events.out.tfevents.1630504739.b0698074cf9b.77.0 +3 -0
  5. runs/Sep01_13-56-51_b0698074cf9b/events.out.tfevents.1630504980.b0698074cf9b.77.2 +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: 0.9740840035746202
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  - name: Recall
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  type: recall
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- value: 0.9784560143626571
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  - name: F1
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  type: f1
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- value: 0.9762651141961486
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  - name: Accuracy
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  type: accuracy
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- value: 0.9946558086270518
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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: 0.0164
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- - Precision: 0.9741
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- - Recall: 0.9785
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- - F1: 0.9763
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- - Accuracy: 0.9947
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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 | 184 | 0.0439 | 0.9299 | 0.9408 | 0.9353 | 0.9855 |
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- | No log | 2.0 | 368 | 0.0210 | 0.9650 | 0.9650 | 0.9650 | 0.9921 |
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- | 0.1021 | 3.0 | 552 | 0.0208 | 0.9669 | 0.9704 | 0.9686 | 0.9930 |
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- | 0.1021 | 4.0 | 736 | 0.0205 | 0.9677 | 0.9695 | 0.9686 | 0.9930 |
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- | 0.1021 | 5.0 | 920 | 0.0159 | 0.9768 | 0.9829 | 0.9799 | 0.9957 |
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- | 0.009 | 6.0 | 1104 | 0.0134 | 0.9777 | 0.9820 | 0.9798 | 0.9957 |
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- | 0.009 | 7.0 | 1288 | 0.0149 | 0.9750 | 0.9785 | 0.9767 | 0.9947 |
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- | 0.009 | 8.0 | 1472 | 0.0188 | 0.9723 | 0.9758 | 0.9740 | 0.9938 |
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- | 0.0042 | 9.0 | 1656 | 0.0161 | 0.9759 | 0.9803 | 0.9781 | 0.9950 |
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- | 0.0042 | 10.0 | 1840 | 0.0164 | 0.9741 | 0.9785 | 0.9763 | 0.9947 |
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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: 0.9694323144104804
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  - name: Recall
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  type: recall
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+ value: 0.9660574412532638
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  - name: F1
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  type: f1
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+ value: 0.9677419354838711
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  - name: Accuracy
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  type: accuracy
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+ value: 0.993223742000251
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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
 
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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: 0.0246
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+ - Precision: 0.9694
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+ - Recall: 0.9661
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+ - F1: 0.9677
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+ - Accuracy: 0.9932
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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 | 183 | 0.0619 | 0.9175 | 0.9286 | 0.9230 | 0.9809 |
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+ | No log | 2.0 | 366 | 0.0319 | 0.9576 | 0.9634 | 0.9605 | 0.9902 |
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+ | 0.1175 | 3.0 | 549 | 0.0276 | 0.9548 | 0.9565 | 0.9557 | 0.9918 |
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+ | 0.1175 | 4.0 | 732 | 0.0235 | 0.9609 | 0.9617 | 0.9613 | 0.9915 |
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+ | 0.1175 | 5.0 | 915 | 0.0288 | 0.9643 | 0.9643 | 0.9643 | 0.9917 |
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+ | 0.0134 | 6.0 | 1098 | 0.0272 | 0.9617 | 0.9626 | 0.9622 | 0.9917 |
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+ | 0.0134 | 7.0 | 1281 | 0.0282 | 0.9651 | 0.9626 | 0.9638 | 0.9913 |
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+ | 0.0134 | 8.0 | 1464 | 0.0248 | 0.9599 | 0.9591 | 0.9595 | 0.9921 |
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+ | 0.0085 | 9.0 | 1647 | 0.0270 | 0.9695 | 0.9669 | 0.9682 | 0.9933 |
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+ | 0.0085 | 10.0 | 1830 | 0.0246 | 0.9694 | 0.9661 | 0.9677 | 0.9932 |
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  ### Framework versions
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