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# SST2_finetune

This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3493
- Accuracy: 0.9037
- F1: 0.9035
- Precision: 0.9050
- Recall: 0.9037
- Learning Rate: 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: 5e-05
- 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: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall | Rate   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| 0.0445        | 1.0   | 8419  | 0.3765          | 0.9025   | 0.9023 | 0.9047    | 0.9025 | 0.0000 |
| 0.0438        | 2.0   | 16838 | 0.3676          | 0.9025   | 0.9024 | 0.9040    | 0.9025 | 0.0000 |
| 0.0419        | 3.0   | 25257 | 0.3654          | 0.9071   | 0.9070 | 0.9077    | 0.9071 | 0.0000 |
| 0.0427        | 4.0   | 33676 | 0.3577          | 0.9025   | 0.9023 | 0.9047    | 0.9025 | 0.0000 |
| 0.0426        | 5.0   | 42095 | 0.3545          | 0.9037   | 0.9035 | 0.9055    | 0.9037 | 0.0000 |
| 0.0423        | 6.0   | 50514 | 0.3488          | 0.9048   | 0.9047 | 0.9061    | 0.9048 | 0.0000 |
| 0.0406        | 7.0   | 58933 | 0.3408          | 0.9060   | 0.9058 | 0.9071    | 0.9060 | 0.0000 |
| 0.0413        | 8.0   | 67352 | 0.3543          | 0.9037   | 0.9035 | 0.9055    | 0.9037 | 0.0000 |
| 0.0407        | 9.0   | 75771 | 0.3445          | 0.9037   | 0.9035 | 0.9050    | 0.9037 | 0.0000 |
| 0.0402        | 10.0  | 84190 | 0.3493          | 0.9037   | 0.9035 | 0.9050    | 0.9037 | 0.0    |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "SST2_finetune", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "config": "sst2", "split": "validation", "args": "sst2"}, "metrics": [{"type": "accuracy", "value": 0.9036697247706422, "name": "Accuracy"}, {"type": "f1", "value": 0.903531061592041, "name": "F1"}, {"type": "precision", "value": 0.9050079487396125, "name": "Precision"}, {"type": "recall", "value": 0.9036697247706422, "name": "Recall"}]}]}]}
transformers pytorch distilbert text-classification generated_from_trainer dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

SST2\_finetune
==============


This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the glue dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3493
* Accuracy: 0.9037
* F1: 0.9035
* Precision: 0.9050
* Recall: 0.9037
* Learning Rate: 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: 5e-05
* 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: 10


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* 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: 10 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #distilbert #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: 5e-05
* 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: 10 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3