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# CR_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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2858
- Accuracy: 0.8889
- F1: 0.8883
- Precision: 0.8882
- Recall: 0.8889
- Learning Rate: 0.0000

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Rate   |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| 0.3569        | 1.0   | 302  | 0.3279          | 0.8756   | 0.8761 | 0.8768    | 0.8756 | 0.0000 |
| 0.3371        | 2.0   | 604  | 0.3203          | 0.8590   | 0.8568 | 0.8579    | 0.8590 | 0.0000 |
| 0.322         | 3.0   | 906  | 0.3120          | 0.8690   | 0.8677 | 0.8679    | 0.8690 | 0.0000 |
| 0.3133        | 4.0   | 1208 | 0.3000          | 0.8706   | 0.8694 | 0.8695    | 0.8706 | 0.0000 |
| 0.306         | 5.0   | 1510 | 0.3015          | 0.8789   | 0.8794 | 0.8800    | 0.8789 | 0.0000 |
| 0.2984        | 6.0   | 1812 | 0.2916          | 0.8856   | 0.8851 | 0.8849    | 0.8856 | 0.0000 |
| 0.2893        | 7.0   | 2114 | 0.3101          | 0.8839   | 0.8849 | 0.8875    | 0.8839 | 0.0000 |
| 0.288         | 8.0   | 2416 | 0.2899          | 0.8839   | 0.8833 | 0.8831    | 0.8839 | 0.0000 |
| 0.2862        | 9.0   | 2718 | 0.2944          | 0.8856   | 0.8860 | 0.8866    | 0.8856 | 0.0000 |
| 0.2882        | 10.0  | 3020 | 0.2848          | 0.8872   | 0.8859 | 0.8867    | 0.8872 | 0.0000 |
| 0.2799        | 11.0  | 3322 | 0.2858          | 0.8889   | 0.8883 | 0.8882    | 0.8889 | 0.0000 |


### 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"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "CR_finetune", "results": []}]}
transformers pytorch tensorboard distilbert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

CR\_finetune
============


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


* Loss: 0.2858
* Accuracy: 0.8889
* F1: 0.8883
* Precision: 0.8882
* Recall: 0.8889
* Learning Rate: 0.0000


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: 20


### 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: 20 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #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: 20 ### Training results ### Framework versions


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