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# klue-roberta-large-klue-2016klp-ner

This model is a fine-tuned version of [soddokayo/klue-roberta-large-klue-ner](https://huggingface.co/soddokayo/klue-roberta-large-klue-ner) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0947
- Precision: 0.7693
- Recall: 0.8093
- F1: 0.7888
- Accuracy: 0.9756

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 366  | 0.0970          | 0.7       | 0.784  | 0.7396 | 0.9685   |
| 0.0982        | 2.0   | 732  | 0.0853          | 0.7982    | 0.8173 | 0.8076 | 0.9770   |
| 0.035         | 3.0   | 1098 | 0.0947          | 0.7693    | 0.8093 | 0.7888 | 0.9756   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cpu
- Datasets 2.12.0
- Tokenizers 0.11.0

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "klue-roberta-large-klue-2016klp-ner", "results": []}]}
transformers pytorch roberta token-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

klue-roberta-large-klue-2016klp-ner
===================================


This model is a fine-tuned version of soddokayo/klue-roberta-large-klue-ner on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0947
* Precision: 0.7693
* Recall: 0.8093
* F1: 0.7888
* Accuracy: 0.9756


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.30.2
* Pytorch 2.0.1+cpu
* Datasets 2.12.0
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.30.2
* Pytorch 2.0.1+cpu
* Datasets 2.12.0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.30.2
* Pytorch 2.0.1+cpu
* Datasets 2.12.0
* Tokenizers 0.11.0