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# bert-base-sst-2

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: 0.4216
- Accuracy: 0.9300
- F1: 0.9300
- Precision: 0.9302
- Recall: 0.9299

## 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: 0.0001
- train_batch_size: 160
- eval_batch_size: 160
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 640
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2366        | 1.0   | 105  | 0.2193          | 0.9117   | 0.9115 | 0.9139    | 0.9111 |
| 0.1104        | 2.0   | 210  | 0.2174          | 0.9243   | 0.9243 | 0.9243    | 0.9243 |
| 0.0685        | 2.99  | 315  | 0.2441          | 0.9186   | 0.9185 | 0.9186    | 0.9185 |
| 0.0476        | 4.0   | 421  | 0.2524          | 0.9232   | 0.9232 | 0.9233    | 0.9234 |
| 0.0319        | 5.0   | 526  | 0.2832          | 0.9220   | 0.9219 | 0.9226    | 0.9217 |
| 0.0227        | 6.0   | 631  | 0.3093          | 0.9289   | 0.9289 | 0.9289    | 0.9289 |
| 0.0169        | 6.99  | 736  | 0.3755          | 0.9209   | 0.9209 | 0.9208    | 0.9210 |
| 0.0112        | 8.0   | 842  | 0.3793          | 0.9220   | 0.9219 | 0.9234    | 0.9215 |
| 0.0079        | 9.0   | 947  | 0.3980          | 0.9255   | 0.9254 | 0.9255    | 0.9254 |
| 0.007         | 9.98  | 1050 | 0.4216          | 0.9300   | 0.9300 | 0.9302    | 0.9299 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "bert-base-sst-2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "config": "sst2", "split": "validation", "args": "sst2"}, "metrics": [{"type": "accuracy", "value": 0.930045871559633, "name": "Accuracy"}, {"type": "f1", "value": 0.9299971705127952, "name": "F1"}, {"type": "precision", "value": 0.9302394783826914, "name": "Precision"}, {"type": "recall", "value": 0.9298749684263703, "name": "Recall"}]}]}]}
transformers pytorch bert text-classification generated_from_trainer dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

bert-base-sst-2
===============


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: 0.4216
* Accuracy: 0.9300
* F1: 0.9300
* Precision: 0.9302
* Recall: 0.9299


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: 0.0001
* train\_batch\_size: 160
* eval\_batch\_size: 160
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 640
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 160
* eval\_batch\_size: 160
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 640
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #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: 0.0001
* train\_batch\_size: 160
* eval\_batch\_size: 160
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 640
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #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: 0.0001
* train\_batch\_size: 160
* eval\_batch\_size: 160
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 640
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3