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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: |
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- shipping_label_ner |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: ner_bert_model |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: shipping_label_ner |
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type: shipping_label_ner |
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config: shipping_label_ner |
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split: validation |
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args: shipping_label_ner |
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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 |
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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: 0.7118 |
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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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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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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- 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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- Transformers 4.39.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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