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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/Sep03_15-36-38_55cadda7c6b8/1630683514.3284032/events.out.tfevents.1630683514.55cadda7c6b8.77.1 +3 -0
  4. runs/Sep03_15-36-38_55cadda7c6b8/events.out.tfevents.1630683514.55cadda7c6b8.77.0 +3 -0
  5. runs/Sep03_15-36-38_55cadda7c6b8/events.out.tfevents.1630683740.55cadda7c6b8.77.2 +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: 0.9694323144104804
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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
@@ -41,11 +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: 0.0246
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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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@@ -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 | 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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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9846596356663471
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  - name: Recall
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  type: recall
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+ value: 0.9856046065259118
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  - name: F1
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  type: f1
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+ value: 0.9851318944844125
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9977284874398717
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  ---
36
 
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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: 0.0091
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+ - Precision: 0.9847
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+ - Recall: 0.9856
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+ - F1: 0.9851
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+ - Accuracy: 0.9977
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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 | 174 | 0.0407 | 0.9234 | 0.9367 | 0.9300 | 0.9869 |
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+ | No log | 2.0 | 348 | 0.0201 | 0.9546 | 0.9683 | 0.9614 | 0.9935 |
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+ | 0.103 | 3.0 | 522 | 0.0147 | 0.9714 | 0.9770 | 0.9742 | 0.9955 |
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+ | 0.103 | 4.0 | 696 | 0.0107 | 0.9846 | 0.9846 | 0.9846 | 0.9975 |
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+ | 0.103 | 5.0 | 870 | 0.0129 | 0.9837 | 0.9837 | 0.9837 | 0.9969 |
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+ | 0.0096 | 6.0 | 1044 | 0.0100 | 0.9827 | 0.9837 | 0.9832 | 0.9972 |
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+ | 0.0096 | 7.0 | 1218 | 0.0096 | 0.9856 | 0.9846 | 0.9851 | 0.9972 |
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+ | 0.0096 | 8.0 | 1392 | 0.0098 | 0.9799 | 0.9818 | 0.9808 | 0.9969 |
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+ | 0.0046 | 9.0 | 1566 | 0.0094 | 0.9818 | 0.9837 | 0.9827 | 0.9975 |
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+ | 0.0046 | 10.0 | 1740 | 0.0091 | 0.9847 | 0.9856 | 0.9851 | 0.9977 |
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  ### Framework versions
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