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# bert-base-uncased_stereoset_finetuned

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the stereoset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3464
- Accuracy: 0.7261

## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.21  | 5    | 0.6832          | 0.5565   |
| No log        | 0.42  | 10   | 0.6945          | 0.4741   |
| No log        | 0.62  | 15   | 0.6659          | 0.6224   |
| No log        | 0.83  | 20   | 0.6337          | 0.6758   |
| No log        | 1.04  | 25   | 0.6019          | 0.6695   |
| No log        | 1.25  | 30   | 0.5797          | 0.7096   |
| No log        | 1.46  | 35   | 0.5562          | 0.7166   |
| No log        | 1.67  | 40   | 0.5497          | 0.7363   |
| No log        | 1.88  | 45   | 0.5382          | 0.7418   |
| No log        | 2.08  | 50   | 0.5356          | 0.7418   |
| No log        | 2.29  | 55   | 0.5690          | 0.7316   |
| No log        | 2.5   | 60   | 0.5778          | 0.7418   |
| No log        | 2.71  | 65   | 0.5695          | 0.7386   |
| No log        | 2.92  | 70   | 0.5765          | 0.7386   |
| No log        | 3.12  | 75   | 0.6079          | 0.7363   |
| No log        | 3.33  | 80   | 0.6919          | 0.7370   |
| No log        | 3.54  | 85   | 0.7396          | 0.7339   |
| No log        | 3.75  | 90   | 0.7109          | 0.7339   |
| No log        | 3.96  | 95   | 0.7246          | 0.7308   |
| No log        | 4.17  | 100  | 0.7502          | 0.7292   |
| No log        | 4.38  | 105  | 0.8222          | 0.7331   |
| No log        | 4.58  | 110  | 0.8508          | 0.7268   |
| No log        | 4.79  | 115  | 0.8995          | 0.7378   |
| No log        | 5.0   | 120  | 0.8797          | 0.7323   |
| No log        | 5.21  | 125  | 0.9254          | 0.7370   |
| No log        | 5.42  | 130  | 0.9863          | 0.7292   |
| No log        | 5.62  | 135  | 1.0044          | 0.7198   |
| No log        | 5.83  | 140  | 1.0236          | 0.7339   |
| No log        | 6.04  | 145  | 1.0705          | 0.7355   |
| No log        | 6.25  | 150  | 1.0734          | 0.7323   |
| No log        | 6.46  | 155  | 1.1066          | 0.7300   |
| No log        | 6.67  | 160  | 1.1166          | 0.7292   |
| No log        | 6.88  | 165  | 1.1258          | 0.7370   |
| No log        | 7.08  | 170  | 1.1972          | 0.7300   |
| No log        | 7.29  | 175  | 1.1682          | 0.7268   |
| No log        | 7.5   | 180  | 1.2221          | 0.7166   |
| No log        | 7.71  | 185  | 1.2813          | 0.7151   |
| No log        | 7.92  | 190  | 1.3180          | 0.7214   |
| No log        | 8.12  | 195  | 1.3224          | 0.7166   |
| No log        | 8.33  | 200  | 1.3252          | 0.7135   |
| No log        | 8.54  | 205  | 1.3205          | 0.7221   |
| No log        | 8.75  | 210  | 1.3266          | 0.7245   |
| No log        | 8.96  | 215  | 1.3318          | 0.7206   |
| No log        | 9.17  | 220  | 1.3345          | 0.7237   |
| No log        | 9.38  | 225  | 1.3378          | 0.7245   |
| No log        | 9.58  | 230  | 1.3422          | 0.7261   |
| No log        | 9.79  | 235  | 1.3453          | 0.7261   |
| No log        | 10.0  | 240  | 1.3464          | 0.7261   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["stereoset"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased_stereoset_finetuned", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "stereoset", "type": "stereoset", "config": "intersentence", "split": "validation", "args": "intersentence"}, "metrics": [{"type": "accuracy", "value": 0.7260596546310832, "name": "Accuracy"}]}]}]}
transformers pytorch bert text-classification generated_from_trainer dataset:stereoset license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-stereoset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

bert-base-uncased\_stereoset\_finetuned
=======================================


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


* Loss: 1.3464
* Accuracy: 0.7261


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: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1
* 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: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-stereoset #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: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-stereoset #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: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2