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# klue-roberta-large-klue-ner

This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1279
- Precision: 0.8292
- Recall: 0.8439
- F1: 0.8365
- Accuracy: 0.9664

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1246        | 1.0   | 2626 | 0.1629          | 0.7891    | 0.7725 | 0.7807 | 0.9539   |
| 0.0744        | 2.0   | 5252 | 0.1194          | 0.8124    | 0.8345 | 0.8233 | 0.9642   |
| 0.0401        | 3.0   | 7878 | 0.1279          | 0.8292    | 0.8439 | 0.8365 | 0.9664   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "datasets": ["klue"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "klue-roberta-large-klue-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "klue", "type": "klue", "config": "ner", "split": "validation", "args": "ner"}, "metrics": [{"type": "precision", "value": 0.8292094561996003, "name": "Precision"}, {"type": "recall", "value": 0.8438661710037175, "name": "Recall"}, {"type": "f1", "value": 0.836473614684002, "name": "F1"}, {"type": "accuracy", "value": 0.9663865173522563, "name": "Accuracy"}]}]}]}
transformers pytorch roberta token-classification generated_from_trainer dataset:klue model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-klue #model-index #autotrain_compatible #endpoints_compatible #region-us 

klue-roberta-large-klue-ner
===========================


This model is a fine-tuned version of klue/roberta-large on the klue dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1279
* Precision: 0.8292
* Recall: 0.8439
* F1: 0.8365
* Accuracy: 0.9664


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 4.26.1
* Pytorch 1.12.1
* Datasets 2.12.0
* Tokenizers 0.13.2



### 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 4.26.1
* Pytorch 1.12.1
* Datasets 2.12.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-klue #model-index #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 4.26.1
* Pytorch 1.12.1
* Datasets 2.12.0
* Tokenizers 0.13.2