---
license: mit
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: 0.2111
- Precision: 0.8593
- Recall: 0.8587
- F1: 0.8590
- Accuracy: 0.9163

## 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   | 0.1938          | 0.7778    | 0.7851 | 0.7815 | 0.8957   |
| 0.3587        | 2.0   | 1404  | 0.1562          | 0.8216    | 0.8219 | 0.8217 | 0.9098   |
| 0.1645        | 3.0   | 2106  | 0.1472          | 0.8161    | 0.8268 | 0.8214 | 0.9114   |
| 0.1645        | 4.0   | 2808  | 0.1528          | 0.8357    | 0.8195 | 0.8275 | 0.9097   |
| 0.1372        | 5.0   | 3510  | 0.1411          | 0.8301    | 0.8349 | 0.8325 | 0.9141   |
| 0.1259        | 6.0   | 4212  | 0.1396          | 0.8341    | 0.8431 | 0.8386 | 0.9149   |
| 0.1259        | 7.0   | 4914  | 0.1470          | 0.8178    | 0.8323 | 0.8250 | 0.9126   |
| 0.1205        | 8.0   | 5616  | 0.1413          | 0.8421    | 0.8480 | 0.8451 | 0.9156   |
| 0.1152        | 9.0   | 6318  | 0.1417          | 0.8342    | 0.8481 | 0.8411 | 0.9158   |
| 0.1126        | 10.0  | 7020  | 0.1475          | 0.8427    | 0.8493 | 0.8460 | 0.9154   |
| 0.1126        | 11.0  | 7722  | 0.1490          | 0.8477    | 0.8510 | 0.8493 | 0.9155   |
| 0.108         | 12.0  | 8424  | 0.1535          | 0.8511    | 0.8540 | 0.8526 | 0.9160   |
| 0.1035        | 13.0  | 9126  | 0.1569          | 0.8515    | 0.8552 | 0.8533 | 0.9160   |
| 0.1035        | 14.0  | 9828  | 0.1677          | 0.8530    | 0.8537 | 0.8534 | 0.9158   |
| 0.097         | 15.0  | 10530 | 0.1721          | 0.8549    | 0.8557 | 0.8553 | 0.9159   |
| 0.0912        | 16.0  | 11232 | 0.1822          | 0.8573    | 0.8574 | 0.8573 | 0.9165   |
| 0.0912        | 17.0  | 11934 | 0.1969          | 0.8577    | 0.8578 | 0.8577 | 0.9158   |
| 0.0854        | 18.0  | 12636 | 0.1969          | 0.8597    | 0.8587 | 0.8592 | 0.9165   |
| 0.08          | 19.0  | 13338 | 0.2035          | 0.8587    | 0.8587 | 0.8587 | 0.9165   |
| 0.0768        | 20.0  | 14040 | 0.2111          | 0.8593    | 0.8587 | 0.8590 | 0.9163   |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.13.1+cu116
- Datasets 2.13.2
- Tokenizers 0.13.3