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ner_bert_model

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distilbert
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  1. README.md +40 -30
  2. model.safetensors +1 -1
  3. runs/Mar28_05-12-04_7cf60fa1c92f/events.out.tfevents.1711602729.7cf60fa1c92f.682.0 +2 -2
README.md CHANGED
@@ -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: 0.5178571428571429
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  - name: Recall
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  type: recall
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- value: 0.7837837837837838
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  - name: F1
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  type: f1
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- value: 0.6236559139784947
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  - name: Accuracy
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  type: accuracy
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- value: 0.7796610169491526
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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: 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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@@ -73,32 +73,42 @@ The following hyperparameters were used during training:
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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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  - lr_scheduler_type: linear
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- - num_epochs: 20
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  ### Training results
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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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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.8192771084337349
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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: 0.8607594936708859
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  - name: Accuracy
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  type: accuracy
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+ value: 0.903954802259887
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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-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: 0.8193
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+ - Recall: 0.9067
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+ - F1: 0.8608
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+ - Accuracy: 0.9040
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  ## Model description
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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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  - lr_scheduler_type: linear
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+ - num_epochs: 30
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  ### Training results
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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 | 1.9567 | 0.0 | 0.0 | 0.0 | 0.4294 |
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+ | No log | 2.0 | 14 | 1.7382 | 1.0 | 0.0133 | 0.0263 | 0.4350 |
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+ | No log | 3.0 | 21 | 1.5156 | 0.56 | 0.1867 | 0.28 | 0.5424 |
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+ | No log | 4.0 | 28 | 1.3070 | 0.5185 | 0.3733 | 0.4341 | 0.6215 |
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+ | No log | 5.0 | 35 | 1.1073 | 0.6792 | 0.48 | 0.5625 | 0.6667 |
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+ | No log | 6.0 | 42 | 0.9590 | 0.6970 | 0.6133 | 0.6525 | 0.7288 |
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+ | No log | 7.0 | 49 | 0.8036 | 0.7324 | 0.6933 | 0.7123 | 0.7853 |
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+ | No log | 8.0 | 56 | 0.7173 | 0.6860 | 0.7867 | 0.7329 | 0.8305 |
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+ | No log | 9.0 | 63 | 0.5963 | 0.7778 | 0.84 | 0.8077 | 0.8814 |
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+ | No log | 10.0 | 70 | 0.5354 | 0.7901 | 0.8533 | 0.8205 | 0.8870 |
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+ | No log | 11.0 | 77 | 0.5048 | 0.8 | 0.8533 | 0.8258 | 0.8814 |
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+ | No log | 12.0 | 84 | 0.4992 | 0.8293 | 0.9067 | 0.8662 | 0.9096 |
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+ | No log | 13.0 | 91 | 0.4745 | 0.8205 | 0.8533 | 0.8366 | 0.8927 |
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+ | No log | 14.0 | 98 | 0.4489 | 0.8608 | 0.9067 | 0.8831 | 0.9153 |
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+ | No log | 15.0 | 105 | 0.4236 | 0.8608 | 0.9067 | 0.8831 | 0.9153 |
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+ | No log | 16.0 | 112 | 0.4621 | 0.8193 | 0.9067 | 0.8608 | 0.9096 |
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+ | No log | 17.0 | 119 | 0.4417 | 0.85 | 0.9067 | 0.8774 | 0.9209 |
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+ | No log | 18.0 | 126 | 0.4642 | 0.8095 | 0.9067 | 0.8553 | 0.9040 |
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+ | No log | 19.0 | 133 | 0.4244 | 0.85 | 0.9067 | 0.8774 | 0.9096 |
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+ | No log | 20.0 | 140 | 0.4731 | 0.8193 | 0.9067 | 0.8608 | 0.9096 |
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+ | No log | 21.0 | 147 | 0.4697 | 0.8193 | 0.9067 | 0.8608 | 0.9040 |
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+ | No log | 22.0 | 154 | 0.4330 | 0.8293 | 0.9067 | 0.8662 | 0.9096 |
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+ | No log | 23.0 | 161 | 0.4531 | 0.8193 | 0.9067 | 0.8608 | 0.9040 |
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+ | No log | 24.0 | 168 | 0.4433 | 0.8193 | 0.9067 | 0.8608 | 0.9040 |
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+ | No log | 25.0 | 175 | 0.4477 | 0.8095 | 0.9067 | 0.8553 | 0.9040 |
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+ | No log | 26.0 | 182 | 0.4446 | 0.8293 | 0.9067 | 0.8662 | 0.9096 |
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+ | No log | 27.0 | 189 | 0.4578 | 0.8293 | 0.9067 | 0.8662 | 0.9096 |
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+ | No log | 28.0 | 196 | 0.4640 | 0.8293 | 0.9067 | 0.8662 | 0.9096 |
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+ | No log | 29.0 | 203 | 0.4683 | 0.8193 | 0.9067 | 0.8608 | 0.9040 |
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+ | No log | 30.0 | 210 | 0.4675 | 0.8193 | 0.9067 | 0.8608 | 0.9040 |
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  ### Framework versions
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