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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: 0.8192771084337349 |
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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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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: 0.8193 |
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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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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: 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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- 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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