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# roberta-base-finetuned-sts

This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1999
- Pearsonr: 0.9560

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Pearsonr |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 329  | 0.2462          | 0.9478   |
| 1.2505        | 2.0   | 658  | 0.1671          | 0.9530   |
| 1.2505        | 3.0   | 987  | 0.1890          | 0.9525   |
| 0.133         | 4.0   | 1316 | 0.2360          | 0.9548   |
| 0.0886        | 5.0   | 1645 | 0.2265          | 0.9528   |
| 0.0886        | 6.0   | 1974 | 0.2097          | 0.9518   |
| 0.0687        | 7.0   | 2303 | 0.2281          | 0.9523   |
| 0.0539        | 8.0   | 2632 | 0.2212          | 0.9542   |
| 0.0539        | 9.0   | 2961 | 0.1843          | 0.9532   |
| 0.045         | 10.0  | 3290 | 0.1999          | 0.9560   |
| 0.0378        | 11.0  | 3619 | 0.2357          | 0.9533   |
| 0.0378        | 12.0  | 3948 | 0.2134          | 0.9541   |
| 0.033         | 13.0  | 4277 | 0.2273          | 0.9540   |
| 0.03          | 14.0  | 4606 | 0.2148          | 0.9533   |
| 0.03          | 15.0  | 4935 | 0.2207          | 0.9534   |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6

{"tags": ["generated_from_trainer"], "datasets": ["klue"], "metrics": ["pearsonr"], "model-index": [{"name": "roberta-base-finetuned-sts", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "klue", "type": "klue", "args": "sts"}, "metrics": [{"type": "pearsonr", "value": 0.956039443806831, "name": "Pearsonr"}]}]}]}
transformers pytorch roberta text-classification generated_from_trainer dataset:klue model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-klue #model-index #autotrain_compatible #endpoints_compatible #region-us 

roberta-base-finetuned-sts
==========================


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


* Loss: 0.1999
* Pearsonr: 0.9560


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: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 15
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 15
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #roberta #text-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: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 15
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #roberta #text-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: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 15
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6