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--- |
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license: mit |
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base_model: xlm-roberta-large |
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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_roberta_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: 0.5272727272727272 |
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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: 0.6304347826086956 |
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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_roberta_model |
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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the shipping_label_ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.0623 |
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- Precision: 0.5273 |
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- Recall: 0.7838 |
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- F1: 0.6304 |
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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: 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 |
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- lr_scheduler_type: linear |
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- num_epochs: 50 |
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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 | 14 | 1.1206 | 0.3125 | 0.4054 | 0.3529 | 0.6610 | |
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| No log | 2.0 | 28 | 0.7363 | 0.5128 | 0.5405 | 0.5263 | 0.7119 | |
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| No log | 3.0 | 42 | 0.6219 | 0.5333 | 0.6486 | 0.5854 | 0.7542 | |
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| No log | 4.0 | 56 | 0.7328 | 0.4727 | 0.7027 | 0.5652 | 0.7627 | |
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| No log | 5.0 | 70 | 0.8181 | 0.5 | 0.7297 | 0.5934 | 0.7542 | |
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| No log | 6.0 | 84 | 0.8485 | 0.5185 | 0.7568 | 0.6154 | 0.7627 | |
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| No log | 7.0 | 98 | 0.9692 | 0.5 | 0.7027 | 0.5843 | 0.7542 | |
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| No log | 8.0 | 112 | 0.9842 | 0.4915 | 0.7838 | 0.6042 | 0.7458 | |
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| No log | 9.0 | 126 | 1.1196 | 0.5 | 0.7838 | 0.6105 | 0.7542 | |
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| No log | 10.0 | 140 | 1.2147 | 0.5 | 0.7838 | 0.6105 | 0.7542 | |
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| No log | 11.0 | 154 | 1.4110 | 0.5 | 0.7568 | 0.6022 | 0.7712 | |
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| No log | 12.0 | 168 | 1.2104 | 0.5370 | 0.7838 | 0.6374 | 0.7881 | |
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| No log | 13.0 | 182 | 1.4145 | 0.5283 | 0.7568 | 0.6222 | 0.7797 | |
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| No log | 14.0 | 196 | 1.4939 | 0.5179 | 0.7838 | 0.6237 | 0.7712 | |
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| No log | 15.0 | 210 | 1.5558 | 0.5273 | 0.7838 | 0.6304 | 0.7797 | |
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| No log | 16.0 | 224 | 1.5639 | 0.5273 | 0.7838 | 0.6304 | 0.7797 | |
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| No log | 17.0 | 238 | 1.5208 | 0.5179 | 0.7838 | 0.6237 | 0.7712 | |
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| No log | 18.0 | 252 | 1.4787 | 0.5918 | 0.7838 | 0.6744 | 0.7966 | |
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| No log | 19.0 | 266 | 1.3946 | 0.5283 | 0.7568 | 0.6222 | 0.7797 | |
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| No log | 20.0 | 280 | 1.6672 | 0.5370 | 0.7838 | 0.6374 | 0.7881 | |
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| No log | 21.0 | 294 | 1.5746 | 0.5185 | 0.7568 | 0.6154 | 0.7712 | |
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| No log | 22.0 | 308 | 1.8881 | 0.5091 | 0.7568 | 0.6087 | 0.7712 | |
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| No log | 23.0 | 322 | 1.5084 | 0.5370 | 0.7838 | 0.6374 | 0.7881 | |
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| No log | 24.0 | 336 | 1.7922 | 0.5091 | 0.7568 | 0.6087 | 0.7712 | |
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| No log | 25.0 | 350 | 1.7265 | 0.5273 | 0.7838 | 0.6304 | 0.7797 | |
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| No log | 26.0 | 364 | 1.7467 | 0.5273 | 0.7838 | 0.6304 | 0.7797 | |
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| No log | 27.0 | 378 | 2.0162 | 0.5 | 0.7568 | 0.6022 | 0.7627 | |
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| No log | 28.0 | 392 | 1.9460 | 0.5 | 0.7568 | 0.6022 | 0.7627 | |
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| No log | 29.0 | 406 | 1.8957 | 0.5091 | 0.7568 | 0.6087 | 0.7712 | |
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| No log | 30.0 | 420 | 1.9941 | 0.5 | 0.7568 | 0.6022 | 0.7627 | |
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| No log | 31.0 | 434 | 1.9095 | 0.5 | 0.7568 | 0.6022 | 0.7712 | |
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| No log | 32.0 | 448 | 1.8920 | 0.5273 | 0.7838 | 0.6304 | 0.7797 | |
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| No log | 33.0 | 462 | 1.9310 | 0.5091 | 0.7568 | 0.6087 | 0.7712 | |
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| No log | 34.0 | 476 | 1.9830 | 0.5091 | 0.7568 | 0.6087 | 0.7712 | |
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| No log | 35.0 | 490 | 2.0445 | 0.5091 | 0.7568 | 0.6087 | 0.7712 | |
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| 0.2599 | 36.0 | 504 | 2.1138 | 0.5091 | 0.7568 | 0.6087 | 0.7712 | |
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| 0.2599 | 37.0 | 518 | 2.0024 | 0.5091 | 0.7568 | 0.6087 | 0.7797 | |
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| 0.2599 | 38.0 | 532 | 2.0004 | 0.5091 | 0.7568 | 0.6087 | 0.7712 | |
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| 0.2599 | 39.0 | 546 | 2.0725 | 0.5091 | 0.7568 | 0.6087 | 0.7712 | |
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| 0.2599 | 40.0 | 560 | 2.0507 | 0.5091 | 0.7568 | 0.6087 | 0.7712 | |
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| 0.2599 | 41.0 | 574 | 2.0548 | 0.5091 | 0.7568 | 0.6087 | 0.7712 | |
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| 0.2599 | 42.0 | 588 | 2.1176 | 0.5091 | 0.7568 | 0.6087 | 0.7712 | |
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| 0.2599 | 43.0 | 602 | 2.0946 | 0.5091 | 0.7568 | 0.6087 | 0.7712 | |
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| 0.2599 | 44.0 | 616 | 2.1211 | 0.5 | 0.7568 | 0.6022 | 0.7627 | |
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| 0.2599 | 45.0 | 630 | 2.1103 | 0.5091 | 0.7568 | 0.6087 | 0.7712 | |
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| 0.2599 | 46.0 | 644 | 2.0876 | 0.5 | 0.7568 | 0.6022 | 0.7627 | |
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| 0.2599 | 47.0 | 658 | 2.0910 | 0.5179 | 0.7838 | 0.6237 | 0.7712 | |
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| 0.2599 | 48.0 | 672 | 2.0800 | 0.5179 | 0.7838 | 0.6237 | 0.7712 | |
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| 0.2599 | 49.0 | 686 | 2.0584 | 0.5273 | 0.7838 | 0.6304 | 0.7797 | |
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| 0.2599 | 50.0 | 700 | 2.0623 | 0.5273 | 0.7838 | 0.6304 | 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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