
# bert-base-uncased-apple_iphone_se_reviews

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased).

It achieves the following results on the evaluation set:
- Loss: 0.0952
- Accuracy: 0.9712
- F1
  - Weighted: 0.9711
  - Micro: 0.9712
  - Macro: 0.9561
- Recall
  - Weighted: 0.9712
  - Micro: 0.9712
  - Macro: 0.9580
- Precision
  - Weighted: 0.9711
  - Micro: 0.9712
  - Macro: 0.9548

## Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Multiclass%20Classification/Apple%20iPhone%20SE%20Reviews/Apple%20IPhone%20Reviews%20-%20MC%20CLF%20-%20Bert-Base.ipynb

* I also completed a version of this project using the MPNet-base transformer.

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

## Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/kmldas/apple-iphone-se-reviews-ratings

_Input Word Length:_

![Length of Input Text (in Words)](https://github.com/DunnBC22/NLP_Projects/raw/main/Multiclass%20Classification/Apple%20iPhone%20SE%20Reviews/Images/Context%20Word%20Length.png)

_Class Distribution:_

![Class Distribution](https://github.com/DunnBC22/NLP_Projects/raw/main/Multiclass%20Classification/Apple%20iPhone%20SE%20Reviews/Images/Class%20Distribution.png)

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted F1 | Micro F1 | Macro F1 | Weighted Recall | Micro Recall | Macro Recall | Weighted Precision | Micro Precision | Macro Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.251         | 1.0   | 122  | 0.1422          | 0.9315   | 0.9238      | 0.9315   | 0.6726   | 0.9315          | 0.9315       | 0.7048       | 0.9222             | 0.9315          | 0.6650          |
| 0.0767        | 2.0   | 244  | 0.0870          | 0.9660   | 0.9653      | 0.9660   | 0.9526   | 0.9660          | 0.9660       | 0.9462       | 0.9665             | 0.9660          | 0.9613          |
| 0.0487        | 3.0   | 366  | 0.0845          | 0.9727   | 0.9727      | 0.9727   | 0.9528   | 0.9727          | 0.9727       | 0.9573       | 0.9730             | 0.9727          | 0.9500          |
| 0.0335        | 4.0   | 488  | 0.0806          | 0.9717   | 0.9715      | 0.9717   | 0.9554   | 0.9717          | 0.9717       | 0.9560       | 0.9717             | 0.9717          | 0.9558          |
| 0.0326        | 5.0   | 610  | 0.0932          | 0.9717   | 0.9717      | 0.9717   | 0.9566   | 0.9717          | 0.9717       | 0.9598       | 0.9718             | 0.9717          | 0.9538          |
| 0.0293        | 6.0   | 732  | 0.0952          | 0.9712   | 0.9711      | 0.9712   | 0.9561   | 0.9712          | 0.9712       | 0.9580       | 0.9711             | 0.9712          | 0.9548          |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "recall", "precision"], "pipeline_tag": "text-classification", "model-index": [{"name": "bert-base-uncased-apple_iphone_se_reviews", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-base-uncased-apple\_iphone\_se\_reviews
============================================


This model is a fine-tuned version of bert-base-uncased.


It achieves the following results on the evaluation set:


* Loss: 0.0952
* Accuracy: 0.9712
* F1
	+ Weighted: 0.9711
	+ Micro: 0.9712
	+ Macro: 0.9561
* Recall
	+ Weighted: 0.9712
	+ Micro: 0.9712
	+ Macro: 0.9580
* Precision
	+ Weighted: 0.9711
	+ Micro: 0.9712
	+ Macro: 0.9548


Model description
-----------------


For more information on how it was created, check out the following link: URL


* I also completed a version of this project using the MPNet-base transformer.


Intended uses & limitations
---------------------------


This model is intended to demonstrate my ability to solve a complex problem using technology.


Training and evaluation data
----------------------------


Dataset Source: URL


*Input Word Length:*


!Length of Input Text (in Words)


*Class Distribution:*


!Class Distribution


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6


### Training results



### Framework versions


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



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6 ### Training results ### Framework versions


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