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ner_bert_model

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distilbert
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  1. README.md +24 -24
  2. model.safetensors +1 -1
  3. runs/Mar27_06-23-29_4912d2a8deb3/events.out.tfevents.1711520612.4912d2a8deb3.761.6 +2 -2
README.md CHANGED
@@ -34,7 +34,7 @@ model-index:
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  value: 0.6236559139784947
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  - name: Accuracy
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  type: accuracy
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- value: 0.7627118644067796
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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
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-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: 1.2010
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  - Precision: 0.5179
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  - Recall: 0.7838
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  - F1: 0.6237
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- - Accuracy: 0.7627
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  ## Model description
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@@ -68,7 +68,7 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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- - train_batch_size: 4
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  - eval_batch_size: 2
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
@@ -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 | 14 | 0.6828 | 0.5179 | 0.7838 | 0.6237 | 0.7627 |
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- | No log | 2.0 | 28 | 0.8587 | 0.5273 | 0.7838 | 0.6304 | 0.7712 |
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- | No log | 3.0 | 42 | 0.7206 | 0.5577 | 0.7838 | 0.6517 | 0.8136 |
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- | No log | 4.0 | 56 | 0.8983 | 0.5370 | 0.7838 | 0.6374 | 0.7797 |
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- | No log | 5.0 | 70 | 0.6964 | 0.5472 | 0.7838 | 0.6444 | 0.8051 |
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- | No log | 6.0 | 84 | 0.9793 | 0.5179 | 0.7838 | 0.6237 | 0.7627 |
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- | No log | 7.0 | 98 | 0.6047 | 0.5472 | 0.7838 | 0.6444 | 0.8051 |
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- | No log | 8.0 | 112 | 1.0809 | 0.5179 | 0.7838 | 0.6237 | 0.7797 |
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- | No log | 9.0 | 126 | 1.1726 | 0.5179 | 0.7838 | 0.6237 | 0.7627 |
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- | No log | 10.0 | 140 | 1.0067 | 0.5179 | 0.7838 | 0.6237 | 0.7627 |
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- | No log | 11.0 | 154 | 1.1439 | 0.5088 | 0.7838 | 0.6170 | 0.7627 |
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- | No log | 12.0 | 168 | 0.8971 | 0.5370 | 0.7838 | 0.6374 | 0.7881 |
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- | No log | 13.0 | 182 | 1.0603 | 0.5179 | 0.7838 | 0.6237 | 0.7542 |
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- | No log | 14.0 | 196 | 1.2095 | 0.5179 | 0.7838 | 0.6237 | 0.7627 |
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- | No log | 15.0 | 210 | 1.2395 | 0.5179 | 0.7838 | 0.6237 | 0.7627 |
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- | No log | 16.0 | 224 | 1.2509 | 0.5179 | 0.7838 | 0.6237 | 0.7627 |
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- | No log | 17.0 | 238 | 1.2317 | 0.5179 | 0.7838 | 0.6237 | 0.7542 |
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- | No log | 18.0 | 252 | 1.2656 | 0.5179 | 0.7838 | 0.6237 | 0.7542 |
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- | No log | 19.0 | 266 | 1.1950 | 0.5179 | 0.7838 | 0.6237 | 0.7627 |
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- | No log | 20.0 | 280 | 1.2010 | 0.5179 | 0.7838 | 0.6237 | 0.7627 |
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  ### Framework versions
 
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  value: 0.6236559139784947
35
  - name: Accuracy
36
  type: accuracy
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+ value: 0.7796610169491526
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  ---
39
 
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
44
 
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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: 0.7118
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  - Precision: 0.5179
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  - Recall: 0.7838
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  - F1: 0.6237
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+ - Accuracy: 0.7797
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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+ - train_batch_size: 8
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  - eval_batch_size: 2
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 
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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 | 1.8106 | 0.0 | 0.0 | 0.0 | 0.5169 |
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+ | No log | 2.0 | 14 | 1.6175 | 0.5556 | 0.1351 | 0.2174 | 0.5932 |
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+ | No log | 3.0 | 21 | 1.3124 | 0.6 | 0.2432 | 0.3462 | 0.6441 |
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+ | No log | 4.0 | 28 | 1.1318 | 0.6471 | 0.5946 | 0.6197 | 0.8051 |
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+ | No log | 5.0 | 35 | 0.9306 | 0.6176 | 0.5676 | 0.5915 | 0.7881 |
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+ | No log | 6.0 | 42 | 0.8279 | 0.5476 | 0.6216 | 0.5823 | 0.7712 |
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+ | No log | 7.0 | 49 | 0.7609 | 0.5952 | 0.6757 | 0.6329 | 0.7881 |
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+ | No log | 8.0 | 56 | 0.7484 | 0.6327 | 0.8378 | 0.7209 | 0.8220 |
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+ | No log | 9.0 | 63 | 0.7035 | 0.6596 | 0.8378 | 0.7381 | 0.8220 |
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+ | No log | 10.0 | 70 | 0.7281 | 0.5741 | 0.8378 | 0.6813 | 0.7881 |
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+ | No log | 11.0 | 77 | 0.6970 | 0.5741 | 0.8378 | 0.6813 | 0.7881 |
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+ | No log | 12.0 | 84 | 0.6790 | 0.5 | 0.7568 | 0.6022 | 0.7881 |
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+ | No log | 13.0 | 91 | 0.7124 | 0.4828 | 0.7568 | 0.5895 | 0.7712 |
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+ | No log | 14.0 | 98 | 0.6770 | 0.5 | 0.7568 | 0.6022 | 0.7797 |
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+ | No log | 15.0 | 105 | 0.7219 | 0.5179 | 0.7838 | 0.6237 | 0.7797 |
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+ | No log | 16.0 | 112 | 0.6695 | 0.5273 | 0.7838 | 0.6304 | 0.7881 |
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+ | No log | 17.0 | 119 | 0.6885 | 0.5179 | 0.7838 | 0.6237 | 0.7797 |
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+ | No log | 18.0 | 126 | 0.7138 | 0.5088 | 0.7838 | 0.6170 | 0.7712 |
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+ | No log | 19.0 | 133 | 0.7113 | 0.5179 | 0.7838 | 0.6237 | 0.7797 |
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+ | No log | 20.0 | 140 | 0.7118 | 0.5179 | 0.7838 | 0.6237 | 0.7797 |
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  ### Framework versions
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