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# bert-base-uncased-stsb

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4676
- Pearson: 0.8901
- Spearmanr: 0.8872
- Combined Score: 0.8887

## 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: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
| 2.3939        | 1.0   | 45   | 0.7358          | 0.8686  | 0.8653    | 0.8669         |
| 0.5084        | 2.0   | 90   | 0.4959          | 0.8835  | 0.8799    | 0.8817         |
| 0.3332        | 3.0   | 135  | 0.5002          | 0.8846  | 0.8815    | 0.8830         |
| 0.2202        | 4.0   | 180  | 0.4962          | 0.8854  | 0.8827    | 0.8840         |
| 0.1642        | 5.0   | 225  | 0.4848          | 0.8864  | 0.8839    | 0.8852         |
| 0.1312        | 6.0   | 270  | 0.4987          | 0.8872  | 0.8866    | 0.8869         |
| 0.1057        | 7.0   | 315  | 0.4840          | 0.8895  | 0.8848    | 0.8871         |
| 0.0935        | 8.0   | 360  | 0.4753          | 0.8887  | 0.8840    | 0.8863         |
| 0.0835        | 9.0   | 405  | 0.4676          | 0.8901  | 0.8872    | 0.8887         |
| 0.0749        | 10.0  | 450  | 0.4808          | 0.8901  | 0.8867    | 0.8884         |
| 0.0625        | 11.0  | 495  | 0.4760          | 0.8893  | 0.8857    | 0.8875         |
| 0.0607        | 12.0  | 540  | 0.5113          | 0.8899  | 0.8859    | 0.8879         |
| 0.0564        | 13.0  | 585  | 0.4918          | 0.8900  | 0.8860    | 0.8880         |
| 0.0495        | 14.0  | 630  | 0.4749          | 0.8905  | 0.8868    | 0.8887         |
| 0.0446        | 15.0  | 675  | 0.4889          | 0.8888  | 0.8856    | 0.8872         |
| 0.045         | 16.0  | 720  | 0.4680          | 0.8918  | 0.8889    | 0.8904         |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["spearmanr"], "model-index": [{"name": "bert-base-uncased-stsb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE STSB", "type": "glue", "config": "stsb", "split": "validation", "args": "stsb"}, "metrics": [{"type": "spearmanr", "value": 0.8872483552831365, "name": "Spearmanr"}]}]}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer en dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

bert-base-uncased-stsb
======================


This model is a fine-tuned version of bert-base-uncased on the GLUE STSB dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4676
* Pearson: 0.8901
* Spearmanr: 0.8872
* Combined Score: 0.8887


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: 5e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* seed: 10
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* seed: 10
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* seed: 10
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* seed: 10
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.9.0
* Tokenizers 0.13.2