| | --- |
| | license: apache-2.0 |
| | base_model: bert-base-uncased |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - shipping_label_ner |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: ner_bert_model |
| | results: |
| | - task: |
| | name: Token Classification |
| | type: token-classification |
| | dataset: |
| | name: shipping_label_ner |
| | type: shipping_label_ner |
| | config: shipping_label_ner |
| | split: validation |
| | args: shipping_label_ner |
| | metrics: |
| | - name: Precision |
| | type: precision |
| | value: 0.8235294117647058 |
| | - name: Recall |
| | type: recall |
| | value: 0.9333333333333333 |
| | - name: F1 |
| | type: f1 |
| | value: 0.8749999999999999 |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9096045197740112 |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # ner_bert_model |
| |
|
| | This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the shipping_label_ner dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.4145 |
| | - Precision: 0.8235 |
| | - Recall: 0.9333 |
| | - F1: 0.8750 |
| | - Accuracy: 0.9096 |
| |
|
| | ## Model description |
| |
|
| | More information needed |
| |
|
| | ## Intended uses & limitations |
| |
|
| | More information needed |
| |
|
| | ## Training and evaluation data |
| |
|
| | More information needed |
| |
|
| | ## Training procedure |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 2e-05 |
| | - train_batch_size: 8 |
| | - eval_batch_size: 2 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 20 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | No log | 1.0 | 7 | 1.7796 | 0.0 | 0.0 | 0.0 | 0.4294 | |
| | | No log | 2.0 | 14 | 1.4530 | 0.5 | 0.2667 | 0.3478 | 0.5650 | |
| | | No log | 3.0 | 21 | 1.1854 | 0.5510 | 0.36 | 0.4355 | 0.6384 | |
| | | No log | 4.0 | 28 | 0.9850 | 0.6667 | 0.5867 | 0.6241 | 0.7345 | |
| | | No log | 5.0 | 35 | 0.8189 | 0.6622 | 0.6533 | 0.6577 | 0.7797 | |
| | | No log | 6.0 | 42 | 0.7194 | 0.6914 | 0.7467 | 0.7179 | 0.8192 | |
| | | No log | 7.0 | 49 | 0.6126 | 0.7262 | 0.8133 | 0.7673 | 0.8588 | |
| | | No log | 8.0 | 56 | 0.5760 | 0.75 | 0.88 | 0.8098 | 0.8701 | |
| | | No log | 9.0 | 63 | 0.4819 | 0.8 | 0.9067 | 0.8500 | 0.8927 | |
| | | No log | 10.0 | 70 | 0.4610 | 0.7907 | 0.9067 | 0.8447 | 0.8983 | |
| | | No log | 11.0 | 77 | 0.4471 | 0.8 | 0.9067 | 0.8500 | 0.8927 | |
| | | No log | 12.0 | 84 | 0.4203 | 0.7931 | 0.92 | 0.8519 | 0.9040 | |
| | | No log | 13.0 | 91 | 0.4281 | 0.8256 | 0.9467 | 0.8820 | 0.9153 | |
| | | No log | 14.0 | 98 | 0.3913 | 0.8256 | 0.9467 | 0.8820 | 0.9153 | |
| | | No log | 15.0 | 105 | 0.3966 | 0.8235 | 0.9333 | 0.8750 | 0.9096 | |
| | | No log | 16.0 | 112 | 0.4033 | 0.8235 | 0.9333 | 0.8750 | 0.9096 | |
| | | No log | 17.0 | 119 | 0.4149 | 0.8140 | 0.9333 | 0.8696 | 0.9040 | |
| | | No log | 18.0 | 126 | 0.4150 | 0.8140 | 0.9333 | 0.8696 | 0.9040 | |
| | | No log | 19.0 | 133 | 0.4122 | 0.8235 | 0.9333 | 0.8750 | 0.9096 | |
| | | No log | 20.0 | 140 | 0.4145 | 0.8235 | 0.9333 | 0.8750 | 0.9096 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.39.1 |
| | - Pytorch 2.2.1+cu121 |
| | - Datasets 2.18.0 |
| | - Tokenizers 0.15.2 |
| |
|
