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distilbert-base-uncased-finetuned-ingredients

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Files changed (6) hide show
  1. README.md +19 -19
  2. pytorch_model.bin +1 -1
  3. runs/Sep08_12-07-34_f7d335feb95c/1631103002.420842/events.out.tfevents.1631103002.f7d335feb95c.74.1 +3 -0
  4. runs/Sep08_12-07-34_f7d335feb95c/events.out.tfevents.1631103002.f7d335feb95c.74.0 +3 -0
  5. runs/Sep08_12-07-34_f7d335feb95c/events.out.tfevents.1631103114.f7d335feb95c.74.2 +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: 0.9871794871794872
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  - name: Recall
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  type: recall
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- value: 0.992633517495396
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  - name: F1
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  type: f1
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- value: 0.98989898989899
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  - name: Accuracy
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  type: accuracy
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- value: 0.9953393533352752
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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: 0.0308
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- - Precision: 0.9872
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- - Recall: 0.9926
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- - F1: 0.9899
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- - Accuracy: 0.9953
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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 | 80 | 0.1360 | 0.7993 | 0.8582 | 0.8277 | 0.9645 |
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- | No log | 2.0 | 160 | 0.0396 | 0.9762 | 0.9834 | 0.9798 | 0.9924 |
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- | No log | 3.0 | 240 | 0.0410 | 0.9745 | 0.9834 | 0.9789 | 0.9916 |
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- | No log | 4.0 | 320 | 0.0382 | 0.9817 | 0.9890 | 0.9853 | 0.9933 |
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- | No log | 5.0 | 400 | 0.0326 | 0.9818 | 0.9908 | 0.9863 | 0.9945 |
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- | No log | 6.0 | 480 | 0.0366 | 0.9781 | 0.9890 | 0.9835 | 0.9936 |
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- | 0.0953 | 7.0 | 560 | 0.0351 | 0.9781 | 0.9853 | 0.9817 | 0.9936 |
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- | 0.0953 | 8.0 | 640 | 0.0314 | 0.9854 | 0.9926 | 0.9890 | 0.9950 |
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- | 0.0953 | 9.0 | 720 | 0.0299 | 0.9872 | 0.9926 | 0.9899 | 0.9953 |
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- | 0.0953 | 10.0 | 800 | 0.0308 | 0.9872 | 0.9926 | 0.9899 | 0.9953 |
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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: 0.9847619047619047
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  - name: Recall
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  type: recall
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+ value: 0.988527724665392
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  - name: F1
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  type: f1
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+ value: 0.9866412213740458
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9974453590689754
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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: 0.0138
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+ - Precision: 0.9848
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+ - Recall: 0.9885
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+ - F1: 0.9866
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+ - Accuracy: 0.9974
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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 | 83 | 0.1004 | 0.8978 | 0.9235 | 0.9105 | 0.9807 |
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+ | No log | 2.0 | 166 | 0.0237 | 0.9714 | 0.9751 | 0.9733 | 0.9940 |
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+ | No log | 3.0 | 249 | 0.0204 | 0.9715 | 0.9771 | 0.9743 | 0.9929 |
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+ | No log | 4.0 | 332 | 0.0138 | 0.9773 | 0.9866 | 0.9819 | 0.9969 |
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+ | No log | 5.0 | 415 | 0.0137 | 0.9829 | 0.9866 | 0.9847 | 0.9969 |
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+ | No log | 6.0 | 498 | 0.0134 | 0.9847 | 0.9866 | 0.9857 | 0.9972 |
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+ | 0.0923 | 7.0 | 581 | 0.0160 | 0.9866 | 0.9885 | 0.9876 | 0.9972 |
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+ | 0.0923 | 8.0 | 664 | 0.0147 | 0.9848 | 0.9885 | 0.9866 | 0.9974 |
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+ | 0.0923 | 9.0 | 747 | 0.0139 | 0.9848 | 0.9885 | 0.9866 | 0.9974 |
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+ | 0.0923 | 10.0 | 830 | 0.0138 | 0.9848 | 0.9885 | 0.9866 | 0.9974 |
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  ### Framework versions
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