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# kcbert-base-finetuned-nsmc

This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) on the nsmc dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4197
- Accuracy: 0.9020
- F1: 0.9033
- Recall: 0.9095
- Precision: 0.8972

## 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: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.3028        | 0.32  | 3000  | 0.2994          | 0.8769   | 0.8732 | 0.8422 | 0.9066    |
| 0.2833        | 0.64  | 6000  | 0.2766          | 0.8880   | 0.8844 | 0.8512 | 0.9203    |
| 0.2719        | 0.96  | 9000  | 0.2527          | 0.8980   | 0.8981 | 0.8933 | 0.9030    |
| 0.1938        | 1.28  | 12000 | 0.2934          | 0.8969   | 0.8965 | 0.8869 | 0.9062    |
| 0.1907        | 1.6   | 15000 | 0.3141          | 0.8992   | 0.8999 | 0.9003 | 0.8996    |
| 0.1824        | 1.92  | 18000 | 0.3537          | 0.8986   | 0.8964 | 0.8711 | 0.9232    |
| 0.1261        | 2.24  | 21000 | 0.4197          | 0.9020   | 0.9033 | 0.9095 | 0.8972    |
| 0.1237        | 2.56  | 24000 | 0.4170          | 0.8995   | 0.9017 | 0.9156 | 0.8882    |
| 0.1182        | 2.88  | 27000 | 0.4165          | 0.9020   | 0.9036 | 0.9130 | 0.8945    |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.14.0
- Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["nsmc"], "metrics": ["accuracy", "f1", "recall", "precision"], "model-index": [{"name": "kcbert-base-finetuned-nsmc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "nsmc", "type": "nsmc", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.90198, "name": "Accuracy"}, {"type": "f1", "value": 0.9033161705233671, "name": "F1"}, {"type": "recall", "value": 0.9095062169785088, "name": "Recall"}, {"type": "precision", "value": 0.8972098126812446, "name": "Precision"}]}]}]}
transformers pytorch bert text-classification generated_from_trainer dataset:nsmc model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-nsmc #model-index #autotrain_compatible #endpoints_compatible #region-us 

kcbert-base-finetuned-nsmc
==========================


This model is a fine-tuned version of beomi/kcbert-base on the nsmc dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4197
* Accuracy: 0.9020
* F1: 0.9033
* Recall: 0.9095
* Precision: 0.8972


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: 3
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.1
* Datasets 1.14.0
* Tokenizers 0.10.3



### 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: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.1
* Datasets 1.14.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-nsmc #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.1
* Datasets 1.14.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-nsmc #model-index #autotrain_compatible #endpoints_compatible #region-us 
### 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: 3
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.1
* Datasets 1.14.0
* Tokenizers 0.10.3