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ner_bert_model

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  1. README.md +31 -31
  2. model.safetensors +1 -1
  3. runs/Mar28_06-55-59_9f7108f719c8/events.out.tfevents.1711608963.9f7108f719c8.203.3 +2 -2
README.md CHANGED
@@ -1,6 +1,6 @@
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  ---
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  license: apache-2.0
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- base_model: distilbert-base-cased
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  tags:
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  - generated_from_trainer
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  datasets:
@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.8095238095238095
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  - name: Recall
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  type: recall
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- value: 0.9066666666666666
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  - name: F1
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  type: f1
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- value: 0.8553459119496856
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  - name: Accuracy
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  type: accuracy
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- value: 0.8926553672316384
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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
@@ -42,13 +42,13 @@ should probably proofread and complete it, then remove this comment. -->
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  # ner_bert_model
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- This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the shipping_label_ner dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4675
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- - Precision: 0.8095
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- - Recall: 0.9067
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- - F1: 0.8553
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- - Accuracy: 0.8927
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  ## Model description
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@@ -79,26 +79,26 @@ 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 | 7 | 1.9550 | 0.0 | 0.0 | 0.0 | 0.4294 |
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- | No log | 2.0 | 14 | 1.7431 | 0.0 | 0.0 | 0.0 | 0.4407 |
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- | No log | 3.0 | 21 | 1.5315 | 0.2632 | 0.0667 | 0.1064 | 0.5198 |
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- | No log | 4.0 | 28 | 1.3289 | 0.5490 | 0.3733 | 0.4444 | 0.6215 |
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- | No log | 5.0 | 35 | 1.1498 | 0.5246 | 0.4267 | 0.4706 | 0.6497 |
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- | No log | 6.0 | 42 | 1.0278 | 0.5921 | 0.6 | 0.5960 | 0.7175 |
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- | No log | 7.0 | 49 | 0.8915 | 0.6579 | 0.6667 | 0.6623 | 0.7684 |
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- | No log | 8.0 | 56 | 0.8158 | 0.6786 | 0.76 | 0.7170 | 0.8023 |
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- | No log | 9.0 | 63 | 0.7012 | 0.7342 | 0.7733 | 0.7532 | 0.8249 |
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- | No log | 10.0 | 70 | 0.6421 | 0.7590 | 0.84 | 0.7975 | 0.8475 |
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- | No log | 11.0 | 77 | 0.5944 | 0.8025 | 0.8667 | 0.8333 | 0.8757 |
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- | No log | 12.0 | 84 | 0.5570 | 0.7976 | 0.8933 | 0.8428 | 0.8870 |
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- | No log | 13.0 | 91 | 0.5088 | 0.8148 | 0.88 | 0.8462 | 0.8927 |
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- | No log | 14.0 | 98 | 0.5156 | 0.8193 | 0.9067 | 0.8608 | 0.8983 |
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- | No log | 15.0 | 105 | 0.4958 | 0.8171 | 0.8933 | 0.8535 | 0.8927 |
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- | No log | 16.0 | 112 | 0.4646 | 0.8171 | 0.8933 | 0.8535 | 0.8927 |
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- | No log | 17.0 | 119 | 0.4745 | 0.8095 | 0.9067 | 0.8553 | 0.8927 |
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- | No log | 18.0 | 126 | 0.4749 | 0.8095 | 0.9067 | 0.8553 | 0.8927 |
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- | No log | 19.0 | 133 | 0.4720 | 0.8095 | 0.9067 | 0.8553 | 0.8927 |
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- | No log | 20.0 | 140 | 0.4675 | 0.8095 | 0.9067 | 0.8553 | 0.8927 |
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  ### Framework versions
 
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  ---
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  license: apache-2.0
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+ base_model: bert-base-uncased
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  tags:
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  - generated_from_trainer
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  datasets:
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.8235294117647058
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  - name: Recall
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  type: recall
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+ value: 0.9333333333333333
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  - name: F1
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  type: f1
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+ value: 0.8749999999999999
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9096045197740112
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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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  # ner_bert_model
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+ This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the shipping_label_ner dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4145
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+ - Precision: 0.8235
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+ - Recall: 0.9333
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+ - F1: 0.8750
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+ - Accuracy: 0.9096
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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 | 7 | 1.7796 | 0.0 | 0.0 | 0.0 | 0.4294 |
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+ | No log | 2.0 | 14 | 1.4530 | 0.5 | 0.2667 | 0.3478 | 0.5650 |
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+ | No log | 3.0 | 21 | 1.1854 | 0.5510 | 0.36 | 0.4355 | 0.6384 |
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+ | No log | 4.0 | 28 | 0.9850 | 0.6667 | 0.5867 | 0.6241 | 0.7345 |
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+ | No log | 5.0 | 35 | 0.8189 | 0.6622 | 0.6533 | 0.6577 | 0.7797 |
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+ | No log | 6.0 | 42 | 0.7194 | 0.6914 | 0.7467 | 0.7179 | 0.8192 |
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+ | No log | 7.0 | 49 | 0.6126 | 0.7262 | 0.8133 | 0.7673 | 0.8588 |
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+ | No log | 8.0 | 56 | 0.5760 | 0.75 | 0.88 | 0.8098 | 0.8701 |
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+ | No log | 9.0 | 63 | 0.4819 | 0.8 | 0.9067 | 0.8500 | 0.8927 |
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+ | No log | 10.0 | 70 | 0.4610 | 0.7907 | 0.9067 | 0.8447 | 0.8983 |
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+ | No log | 11.0 | 77 | 0.4471 | 0.8 | 0.9067 | 0.8500 | 0.8927 |
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+ | No log | 12.0 | 84 | 0.4203 | 0.7931 | 0.92 | 0.8519 | 0.9040 |
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+ | No log | 13.0 | 91 | 0.4281 | 0.8256 | 0.9467 | 0.8820 | 0.9153 |
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+ | No log | 14.0 | 98 | 0.3913 | 0.8256 | 0.9467 | 0.8820 | 0.9153 |
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+ | No log | 15.0 | 105 | 0.3966 | 0.8235 | 0.9333 | 0.8750 | 0.9096 |
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+ | No log | 16.0 | 112 | 0.4033 | 0.8235 | 0.9333 | 0.8750 | 0.9096 |
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+ | No log | 17.0 | 119 | 0.4149 | 0.8140 | 0.9333 | 0.8696 | 0.9040 |
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+ | No log | 18.0 | 126 | 0.4150 | 0.8140 | 0.9333 | 0.8696 | 0.9040 |
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+ | No log | 19.0 | 133 | 0.4122 | 0.8235 | 0.9333 | 0.8750 | 0.9096 |
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+ | No log | 20.0 | 140 | 0.4145 | 0.8235 | 0.9333 | 0.8750 | 0.9096 |
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  ### Framework versions
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