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almaghrabima
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ner_column_TQ

Token Classification
Transformers
PyTorch
English
deberta-v2
Generated from Trainer
Inference Endpoints
Model card Files Community
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  1. README.md +30 -36
  2. pytorch_model.bin +1 -1
README.md CHANGED
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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:
@@ -10,13 +11,6 @@ metrics:
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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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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -26,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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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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@@ -61,31 +55,31 @@ The following hyperparameters were used during training:
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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
 
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  ---
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  license: mit
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+ base_model: Gladiator/microsoft-deberta-v3-large_ner_conll2003
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  tags:
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  - generated_from_trainer
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  metrics:
 
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  model-index:
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  - name: ner_column_TQ
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  results: []
 
 
 
 
 
 
 
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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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  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: 0.1949
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+ - Precision: 0.8546
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+ - Recall: 0.8533
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+ - F1: 0.8540
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+ - Accuracy: 0.9154
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  ## Model description
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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 | 0.2342 | 0.7774 | 0.7496 | 0.7632 | 0.8833 |
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+ | 0.369 | 2.0 | 1404 | 0.1708 | 0.8050 | 0.8048 | 0.8049 | 0.9033 |
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+ | 0.1681 | 3.0 | 2106 | 0.1646 | 0.8007 | 0.8078 | 0.8043 | 0.9054 |
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+ | 0.1681 | 4.0 | 2808 | 0.1469 | 0.8250 | 0.8335 | 0.8292 | 0.9133 |
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+ | 0.14 | 5.0 | 3510 | 0.1465 | 0.8235 | 0.8345 | 0.8290 | 0.9137 |
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+ | 0.1279 | 6.0 | 4212 | 0.1517 | 0.8165 | 0.8323 | 0.8244 | 0.9127 |
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+ | 0.1279 | 7.0 | 4914 | 0.1474 | 0.8224 | 0.8370 | 0.8297 | 0.9138 |
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+ | 0.1212 | 8.0 | 5616 | 0.1500 | 0.8255 | 0.8409 | 0.8331 | 0.9141 |
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+ | 0.1165 | 9.0 | 6318 | 0.1545 | 0.8297 | 0.8390 | 0.8343 | 0.9142 |
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+ | 0.1138 | 10.0 | 7020 | 0.1590 | 0.8342 | 0.8467 | 0.8404 | 0.9150 |
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+ | 0.1138 | 11.0 | 7722 | 0.1588 | 0.8383 | 0.8474 | 0.8428 | 0.9156 |
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+ | 0.1099 | 12.0 | 8424 | 0.1547 | 0.8425 | 0.8446 | 0.8435 | 0.9156 |
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+ | 0.1071 | 13.0 | 9126 | 0.1565 | 0.8475 | 0.8471 | 0.8473 | 0.9164 |
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+ | 0.1071 | 14.0 | 9828 | 0.1625 | 0.8440 | 0.8489 | 0.8464 | 0.9156 |
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+ | 0.1031 | 15.0 | 10530 | 0.1680 | 0.8486 | 0.8510 | 0.8498 | 0.9160 |
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+ | 0.0992 | 16.0 | 11232 | 0.1722 | 0.8529 | 0.8505 | 0.8517 | 0.9156 |
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+ | 0.0992 | 17.0 | 11934 | 0.1771 | 0.8527 | 0.8529 | 0.8528 | 0.9159 |
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+ | 0.094 | 18.0 | 12636 | 0.1862 | 0.8555 | 0.8531 | 0.8543 | 0.9159 |
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+ | 0.0892 | 19.0 | 13338 | 0.1884 | 0.8534 | 0.8534 | 0.8534 | 0.9156 |
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+ | 0.086 | 20.0 | 14040 | 0.1949 | 0.8546 | 0.8533 | 0.8540 | 0.9154 |
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  ### Framework versions
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+ - Transformers 4.33.2
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+ - Pytorch 2.0.1+cu117
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+ - Datasets 2.14.5
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+ - Tokenizers 0.13.3
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