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---
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
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ner_column_TQ
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.
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
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