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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_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 |