
# bert-base-uncased-uHack_reviews_multilabel_clf

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: 0.1515
- F1: 0.8737
- Roc Auc: 0.9146
- Accuracy: 0.5967

## Model description

This is a multilabel classification model of whether different aspects of a product are mentioned in reviews.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Multilabel%20Classification/Review%20Sentiments/Sentiment_Analysis%20-%20Using%20BERT%20Instead%20of%20DistilBERT.ipynb

## 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/mohamedziauddin/mh-uhack-sentiments?select=train.csv

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.3409        | 1.0   | 305  | 0.2453          | 0.7717 | 0.8284  | 0.3787   |
| 0.2049        | 2.0   | 610  | 0.1906          | 0.8389 | 0.8796  | 0.5066   |
| 0.1519        | 3.0   | 915  | 0.1707          | 0.8595 | 0.8992  | 0.5443   |
| 0.1176        | 4.0   | 1220 | 0.1575          | 0.8592 | 0.9012  | 0.5459   |
| 0.093         | 5.0   | 1525 | 0.1544          | 0.8647 | 0.9034  | 0.5656   |
| 0.0752        | 6.0   | 1830 | 0.1550          | 0.8630 | 0.9084  | 0.5656   |
| 0.0629        | 7.0   | 2135 | 0.1515          | 0.8737 | 0.9146  | 0.5967   |
| 0.0538        | 8.0   | 2440 | 0.1487          | 0.8717 | 0.9145  | 0.5852   |
| 0.0481        | 9.0   | 2745 | 0.1502          | 0.8714 | 0.9145  | 0.5836   |
| 0.0447        | 10.0  | 3050 | 0.1499          | 0.8715 | 0.9154  | 0.5852   |


### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy", "roc_auc"], "pipeline_tag": "text-classification", "model-index": [{"name": "bert-base-uncased-uHack_reviews_multilabel_clf", "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-uHack\_reviews\_multilabel\_clf
=================================================


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


* Loss: 0.1515
* F1: 0.8737
* Roc Auc: 0.9146
* Accuracy: 0.5967


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


This is a multilabel classification model of whether different aspects of a product are mentioned in reviews.


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


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


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


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* 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 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* 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 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
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: 16
* eval\_batch\_size: 16
* 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 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
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: 16
* eval\_batch\_size: 16
* 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 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2