---
license: mit
base_model: Gladiator/microsoft-deberta-v3-large_ner_conll2003
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner_tag_model
  results:
  - task:
      name: Token Classification
      type: token-classification
    metrics:
    - name: Precision
      type: precision
      value: 0.8568714588197879
    - name: Recall
      type: recall
      value: 0.8550538245045557
    - name: F1
      type: f1
      value: 0.8559616767268047
    - name: Accuracy
      type: accuracy
      value: 0.9150941588185013
language:
- en
widget:
- text: apparatus for models demonstrational for co ltd education and NON-WOVEN other BAG 902300000000 or unsuitable example intex for designed instruments SS011 uses industries in china 2020 intex purposes exhibitions
- text: 62044200_IN Apparels india 620442000000 zimmermann zimmermann cotton of
- text: nuts or or screws not other Adjusting diesel with and their china screw bolts washers dt 2.24061 whether 731815000000 technic
- text: secret SHOP s canada victoria other 392690_CA ACCESSORIES victoria 392690999999 secret FITTING s
- text: HAC-30 68/550 germany in 730890200003 A.-Channel stores hilti F hilti 431892
---

<!-- 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_tag_model

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: 0.1712
- Precision: 0.8569
- Recall: 0.8551
- F1: 0.8560
- Accuracy: 0.9151

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2322        | 1.0   | 2495  | 0.1925          | 0.7990    | 0.7924 | 0.7957 | 0.8969   |
| 0.1674        | 2.0   | 4990  | 0.1488          | 0.8218    | 0.8316 | 0.8267 | 0.9116   |
| 0.1381        | 3.0   | 7485  | 0.1438          | 0.8204    | 0.8350 | 0.8276 | 0.9130   |
| 0.1284        | 4.0   | 9980  | 0.1381          | 0.8419    | 0.8405 | 0.8412 | 0.9148   |
| 0.1198        | 5.0   | 12475 | 0.1400          | 0.8280    | 0.8410 | 0.8345 | 0.9148   |
| 0.1155        | 6.0   | 14970 | 0.1395          | 0.8379    | 0.8467 | 0.8423 | 0.9154   |
| 0.1125        | 7.0   | 17465 | 0.1496          | 0.8438    | 0.8487 | 0.8462 | 0.9151   |
| 0.1068        | 8.0   | 19960 | 0.1510          | 0.8518    | 0.8529 | 0.8523 | 0.9156   |
| 0.1002        | 9.0   | 22455 | 0.1616          | 0.8536    | 0.8539 | 0.8537 | 0.9150   |
| 0.0964        | 10.0  | 24950 | 0.1712          | 0.8569    | 0.8551 | 0.8560 | 0.9151   |


### Framework versions

- Transformers 4.33.1
- Pytorch 1.13.1+cu116
- Datasets 2.14.5
- Tokenizers 0.13.3