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distilbert-base-uncased-finetuned-ingredients

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add model

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Files changed (6) hide show
  1. README.md +19 -19
  2. pytorch_model.bin +1 -1
  3. runs/Sep06_15-17-53_32c9b5098965/1630941604.153061/events.out.tfevents.1630941604.32c9b5098965.78.1 +3 -0
  4. runs/Sep06_15-17-53_32c9b5098965/events.out.tfevents.1630941604.32c9b5098965.78.0 +3 -0
  5. runs/Sep06_15-17-53_32c9b5098965/events.out.tfevents.1630941867.32c9b5098965.78.2 +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: 0.9944576405384006
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  - name: Recall
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  type: recall
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- value: 0.9960348929421095
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  - name: F1
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  type: f1
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- value: 0.9952456418383517
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  - name: Accuracy
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  type: accuracy
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- value: 0.9984752521698335
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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: 0.0112
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- - Precision: 0.9945
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- - Recall: 0.9960
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- - F1: 0.9952
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- - Accuracy: 0.9985
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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 | 198 | 0.0264 | 0.9771 | 0.9794 | 0.9782 | 0.9937 |
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- | No log | 2.0 | 396 | 0.0149 | 0.9851 | 0.9937 | 0.9893 | 0.9965 |
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- | 0.1007 | 3.0 | 594 | 0.0116 | 0.9937 | 0.9952 | 0.9945 | 0.9979 |
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- | 0.1007 | 4.0 | 792 | 0.0099 | 0.9960 | 0.9968 | 0.9964 | 0.9986 |
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- | 0.1007 | 5.0 | 990 | 0.0096 | 0.9945 | 0.9968 | 0.9956 | 0.9986 |
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- | 0.0057 | 6.0 | 1188 | 0.0102 | 0.9937 | 0.9960 | 0.9949 | 0.9986 |
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- | 0.0057 | 7.0 | 1386 | 0.0105 | 0.9937 | 0.9960 | 0.9949 | 0.9986 |
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- | 0.0014 | 8.0 | 1584 | 0.0108 | 0.9945 | 0.9960 | 0.9952 | 0.9985 |
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- | 0.0014 | 9.0 | 1782 | 0.0111 | 0.9945 | 0.9960 | 0.9952 | 0.9985 |
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- | 0.0014 | 10.0 | 1980 | 0.0112 | 0.9945 | 0.9960 | 0.9952 | 0.9985 |
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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: 0.9916855631141346
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  - name: Recall
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  type: recall
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+ value: 0.9977186311787072
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  - name: F1
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  type: f1
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+ value: 0.9946929492039424
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  - name: Accuracy
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  type: accuracy
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+ value: 0.998049340218015
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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: 0.0117
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+ - Precision: 0.9917
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+ - Recall: 0.9977
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+ - F1: 0.9947
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+ - Accuracy: 0.9980
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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 | 199 | 0.0387 | 0.9431 | 0.9703 | 0.9565 | 0.9900 |
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+ | No log | 2.0 | 398 | 0.0195 | 0.9805 | 0.9916 | 0.9860 | 0.9960 |
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+ | 0.1062 | 3.0 | 597 | 0.0187 | 0.9842 | 0.9939 | 0.9890 | 0.9963 |
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+ | 0.1062 | 4.0 | 796 | 0.0120 | 0.9887 | 0.9954 | 0.9920 | 0.9976 |
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+ | 0.1062 | 5.0 | 995 | 0.0131 | 0.9887 | 0.9962 | 0.9924 | 0.9975 |
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+ | 0.0067 | 6.0 | 1194 | 0.0106 | 0.9917 | 0.9970 | 0.9943 | 0.9980 |
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+ | 0.0067 | 7.0 | 1393 | 0.0116 | 0.9909 | 0.9977 | 0.9943 | 0.9979 |
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+ | 0.002 | 8.0 | 1592 | 0.0118 | 0.9909 | 0.9977 | 0.9943 | 0.9979 |
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+ | 0.002 | 9.0 | 1791 | 0.0118 | 0.9917 | 0.9977 | 0.9947 | 0.9980 |
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+ | 0.002 | 10.0 | 1990 | 0.0117 | 0.9917 | 0.9977 | 0.9947 | 0.9980 |
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  ### Framework versions
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