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# bert-base-uncased-8-50-0.01

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9219
- Matthews Correlation: 0.0

## 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: 0.01
- 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: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:-----:|:---------------:|:--------------------:|
| No log        | 1.0   | 400   | 0.9219          | 0.0                  |
| 1.2047        | 2.0   | 800   | 1.8168          | 0.0                  |
| 1.0707        | 3.0   | 1200  | 1.4474          | 0.0                  |
| 1.0538        | 4.0   | 1600  | 1.5223          | 0.0                  |
| 1.316         | 5.0   | 2000  | 0.8467          | 0.0                  |
| 1.316         | 6.0   | 2400  | 1.0906          | 0.0                  |
| 1.2739        | 7.0   | 2800  | 0.6851          | 0.0                  |
| 1.1342        | 8.0   | 3200  | 1.3170          | 0.0                  |
| 1.2572        | 9.0   | 3600  | 0.8870          | 0.0                  |
| 1.0237        | 10.0  | 4000  | 1.3236          | 0.0                  |
| 1.0237        | 11.0  | 4400  | 0.9025          | 0.0                  |
| 0.9597        | 12.0  | 4800  | 0.7757          | 0.0                  |
| 1.0946        | 13.0  | 5200  | 1.2551          | 0.0                  |
| 1.0011        | 14.0  | 5600  | 1.1606          | 0.0                  |
| 1.1111        | 15.0  | 6000  | 0.6040          | 0.0                  |
| 1.1111        | 16.0  | 6400  | 1.4347          | 0.0                  |
| 1.0098        | 17.0  | 6800  | 0.6218          | 0.0                  |
| 1.0829        | 18.0  | 7200  | 0.4979          | 0.0                  |
| 0.9131        | 19.0  | 7600  | 1.3040          | 0.0                  |
| 0.879         | 20.0  | 8000  | 2.0309          | 0.0                  |
| 0.879         | 21.0  | 8400  | 0.5150          | 0.0                  |
| 0.9646        | 22.0  | 8800  | 0.4850          | 0.0                  |
| 0.9625        | 23.0  | 9200  | 0.5076          | 0.0                  |
| 0.9129        | 24.0  | 9600  | 1.1277          | 0.0                  |
| 0.8839        | 25.0  | 10000 | 0.9403          | 0.0                  |
| 0.8839        | 26.0  | 10400 | 1.6226          | 0.0                  |
| 0.9264        | 27.0  | 10800 | 0.6049          | 0.0                  |
| 0.7999        | 28.0  | 11200 | 0.9549          | 0.0                  |
| 0.752         | 29.0  | 11600 | 0.6757          | 0.0                  |
| 0.7675        | 30.0  | 12000 | 0.7320          | 0.0                  |
| 0.7675        | 31.0  | 12400 | 0.8393          | 0.0                  |
| 0.6887        | 32.0  | 12800 | 0.5977          | 0.0                  |
| 0.7563        | 33.0  | 13200 | 0.4815          | 0.0                  |
| 0.7671        | 34.0  | 13600 | 0.5457          | 0.0                  |
| 0.7227        | 35.0  | 14000 | 0.7384          | 0.0                  |
| 0.7227        | 36.0  | 14400 | 0.7749          | 0.0                  |
| 0.7308        | 37.0  | 14800 | 0.4726          | 0.0                  |
| 0.7191        | 38.0  | 15200 | 0.5069          | 0.0                  |
| 0.6846        | 39.0  | 15600 | 0.4762          | 0.0                  |
| 0.6151        | 40.0  | 16000 | 0.4738          | 0.0                  |
| 0.6151        | 41.0  | 16400 | 0.5114          | 0.0                  |
| 0.5982        | 42.0  | 16800 | 0.4866          | 0.0                  |
| 0.6199        | 43.0  | 17200 | 0.4717          | 0.0                  |
| 0.5737        | 44.0  | 17600 | 0.7651          | 0.0                  |
| 0.5703        | 45.0  | 18000 | 0.8008          | 0.0                  |
| 0.5703        | 46.0  | 18400 | 0.5391          | 0.0                  |
| 0.5748        | 47.0  | 18800 | 0.5097          | 0.0                  |
| 0.5297        | 48.0  | 19200 | 0.4731          | 0.0                  |
| 0.4902        | 49.0  | 19600 | 0.4720          | 0.0                  |
| 0.4955        | 50.0  | 20000 | 0.4748          | 0.0                  |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.9.0
- Datasets 1.18.3
- Tokenizers 0.11.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "bert-base-uncased-8-50-0.01", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.0, "name": "Matthews Correlation"}]}]}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

bert-base-uncased-8-50-0.01
===========================


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


* Loss: 0.9219
* Matthews Correlation: 0.0


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: 0.01
* 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: 50


### Training results



### Framework versions


* Transformers 4.16.2
* Pytorch 1.9.0
* Datasets 1.18.3
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.01
* 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: 50 ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.9.0
* Datasets 1.18.3
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #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: 0.01
* 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: 50 ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.9.0
* Datasets 1.18.3
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #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: 0.01
* 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: 50### Training results### Framework versions


* Transformers 4.16.2
* Pytorch 1.9.0
* Datasets 1.18.3
* Tokenizers 0.11.0