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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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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_column_TQ |
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results: [] |
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language: |
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- en |
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widget: |
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- india 0S0308Z8 trudeau 3000 Ravensburger Hamnoy, Lofoten of gold bestseller 620463000001 |
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- other china lc waikiki mağazacilik hi̇zmetleri̇ ti̇c aş 630140000000 hilti 6204699090_BD 55L Toaster Oven with Double Glass |
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- 611020000001 italy Apparel other games 9W1964Z8 debenhams guangzhou hec fashion leather co ltd |
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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_column_TQ |
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This model is a fine-tuned version of [Gladiator/microsoft-deberta-v3-large_ner_conll2003](https://huggingface.co/Gladiator/microsoft-deberta-v3-large_ner_conll2003) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2111 |
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- Precision: 0.8593 |
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- Recall: 0.8587 |
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- F1: 0.8590 |
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- Accuracy: 0.9163 |
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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: 64 |
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- eval_batch_size: 64 |
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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 | 702 | 0.1938 | 0.7778 | 0.7851 | 0.7815 | 0.8957 | |
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| 0.3587 | 2.0 | 1404 | 0.1562 | 0.8216 | 0.8219 | 0.8217 | 0.9098 | |
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| 0.1645 | 3.0 | 2106 | 0.1472 | 0.8161 | 0.8268 | 0.8214 | 0.9114 | |
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| 0.1645 | 4.0 | 2808 | 0.1528 | 0.8357 | 0.8195 | 0.8275 | 0.9097 | |
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| 0.1372 | 5.0 | 3510 | 0.1411 | 0.8301 | 0.8349 | 0.8325 | 0.9141 | |
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| 0.1259 | 6.0 | 4212 | 0.1396 | 0.8341 | 0.8431 | 0.8386 | 0.9149 | |
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| 0.1259 | 7.0 | 4914 | 0.1470 | 0.8178 | 0.8323 | 0.8250 | 0.9126 | |
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| 0.1205 | 8.0 | 5616 | 0.1413 | 0.8421 | 0.8480 | 0.8451 | 0.9156 | |
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| 0.1152 | 9.0 | 6318 | 0.1417 | 0.8342 | 0.8481 | 0.8411 | 0.9158 | |
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| 0.1126 | 10.0 | 7020 | 0.1475 | 0.8427 | 0.8493 | 0.8460 | 0.9154 | |
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| 0.1126 | 11.0 | 7722 | 0.1490 | 0.8477 | 0.8510 | 0.8493 | 0.9155 | |
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| 0.108 | 12.0 | 8424 | 0.1535 | 0.8511 | 0.8540 | 0.8526 | 0.9160 | |
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| 0.1035 | 13.0 | 9126 | 0.1569 | 0.8515 | 0.8552 | 0.8533 | 0.9160 | |
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| 0.1035 | 14.0 | 9828 | 0.1677 | 0.8530 | 0.8537 | 0.8534 | 0.9158 | |
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| 0.097 | 15.0 | 10530 | 0.1721 | 0.8549 | 0.8557 | 0.8553 | 0.9159 | |
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| 0.0912 | 16.0 | 11232 | 0.1822 | 0.8573 | 0.8574 | 0.8573 | 0.9165 | |
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| 0.0912 | 17.0 | 11934 | 0.1969 | 0.8577 | 0.8578 | 0.8577 | 0.9158 | |
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| 0.0854 | 18.0 | 12636 | 0.1969 | 0.8597 | 0.8587 | 0.8592 | 0.9165 | |
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| 0.08 | 19.0 | 13338 | 0.2035 | 0.8587 | 0.8587 | 0.8587 | 0.9165 | |
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| 0.0768 | 20.0 | 14040 | 0.2111 | 0.8593 | 0.8587 | 0.8590 | 0.9163 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.13.2 |
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- Tokenizers 0.13.3 |
