
# distilbert-base-uncased-reviews_multilabel_clf_v2

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased).
It achieves the following results on the evaluation set:
- Loss: 0.1519
- F1: 0.8697
- Roc Auc: 0.9107
- Accuracy: 0.5787

## 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/Sentiments%20-%20Multilabel%20clf.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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.6847        | 1.0   | 305  | 0.2425          | 0.7619 | 0.8209  | 0.3492   |
| 0.296         | 2.0   | 610  | 0.1786          | 0.8447 | 0.8847  | 0.5197   |
| 0.296         | 3.0   | 915  | 0.1634          | 0.8511 | 0.8937  | 0.5361   |
| 0.1476        | 4.0   | 1220 | 0.1544          | 0.8626 | 0.8999  | 0.5623   |
| 0.0986        | 5.0   | 1525 | 0.1490          | 0.8624 | 0.8994  | 0.5639   |
| 0.0986        | 6.0   | 1830 | 0.1521          | 0.8653 | 0.9041  | 0.5787   |
| 0.0686        | 7.0   | 2135 | 0.1511          | 0.8676 | 0.9110  | 0.5656   |
| 0.0686        | 8.0   | 2440 | 0.1501          | 0.8687 | 0.9104  | 0.5869   |
| 0.0525        | 9.0   | 2745 | 0.1519          | 0.8685 | 0.9089  | 0.5754   |
| 0.0432        | 10.0  | 3050 | 0.1519          | 0.8697 | 0.9107  | 0.5787   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.12.1
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy", "roc_auc"], "pipeline_tag": "text-classification", "model-index": [{"name": "distilbert-base-uncased-reviews_multilabel_clf_v2", "results": []}]}
transformers pytorch tensorboard distilbert text-classification generated_from_trainer en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased-reviews\_multilabel\_clf\_v2
====================================================


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


* Loss: 0.1519
* F1: 0.8697
* Roc Auc: 0.9107
* Accuracy: 0.5787


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 4.26.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.12.1



### 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 4.26.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #distilbert #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 4.26.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #distilbert #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 4.26.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.12.1