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ner_column_TQ

Token Classification
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deberta-v2
Generated from Trainer
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ner_column_TQ / README.md
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metadata
license: mit
base_model: Gladiator/microsoft-deberta-v3-large_ner_conll2003
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: ner_column_TQ
    results: []
language:
  - en
widget:
  - >-
    india 0S0308Z8   trudeau 3000 Ravensburger Hamnoy, Lofoten of gold
    bestseller 620463000001
  - >-
    other china lc waikiki mağazacilik hi̇zmetleri̇ ti̇c aş 630140000000 hilti
    6204699090_BD 55L Toaster Oven with Double Glass
  - >-
    611020000001 italy Apparel  other games 9W1964Z8 debenhams guangzhou hec
    fashion leather co ltd

ner_column_TQ

This model is a fine-tuned version of Gladiator/microsoft-deberta-v3-large_ner_conll2003 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1949
  • Precision: 0.8546
  • Recall: 0.8533
  • F1: 0.8540
  • Accuracy: 0.9154

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 702 0.2342 0.7774 0.7496 0.7632 0.8833
0.369 2.0 1404 0.1708 0.8050 0.8048 0.8049 0.9033
0.1681 3.0 2106 0.1646 0.8007 0.8078 0.8043 0.9054
0.1681 4.0 2808 0.1469 0.8250 0.8335 0.8292 0.9133
0.14 5.0 3510 0.1465 0.8235 0.8345 0.8290 0.9137
0.1279 6.0 4212 0.1517 0.8165 0.8323 0.8244 0.9127
0.1279 7.0 4914 0.1474 0.8224 0.8370 0.8297 0.9138
0.1212 8.0 5616 0.1500 0.8255 0.8409 0.8331 0.9141
0.1165 9.0 6318 0.1545 0.8297 0.8390 0.8343 0.9142
0.1138 10.0 7020 0.1590 0.8342 0.8467 0.8404 0.9150
0.1138 11.0 7722 0.1588 0.8383 0.8474 0.8428 0.9156
0.1099 12.0 8424 0.1547 0.8425 0.8446 0.8435 0.9156
0.1071 13.0 9126 0.1565 0.8475 0.8471 0.8473 0.9164
0.1071 14.0 9828 0.1625 0.8440 0.8489 0.8464 0.9156
0.1031 15.0 10530 0.1680 0.8486 0.8510 0.8498 0.9160
0.0992 16.0 11232 0.1722 0.8529 0.8505 0.8517 0.9156
0.0992 17.0 11934 0.1771 0.8527 0.8529 0.8528 0.9159
0.094 18.0 12636 0.1862 0.8555 0.8531 0.8543 0.9159
0.0892 19.0 13338 0.1884 0.8534 0.8534 0.8534 0.9156
0.086 20.0 14040 0.1949 0.8546 0.8533 0.8540 0.9154

Framework versions

  • Transformers 4.33.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.5
  • Tokenizers 0.13.3