---
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:
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- 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