
# distilbert-base-uncased-Hate_Offensive_or_Normal_Speech

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1606
- Accuracy: 0.9607
- Weighted f1: 0.9607
- Micro f1: 0.9607
- Macro f1: 0.9592
- Weighted recall: 0.9607
- Micro recall: 0.9607
- Macro recall: 0.9579
- Weighted precision: 0.9613
- Micro precision: 0.9607
- Macro precision: 0.9613

## 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/Transformer%20Comparison/Hate%20%26%20Offensive%20Speech%20-%20DistilBERT.ipynb

### Associated Models
This project is part of a comparison that included the following models:

- https://huggingface.co/DunnBC22/bert-large-uncased-Hate_Offensive_or_Normal_Speech
- https://huggingface.co/DunnBC22/bert-base-uncased-Hate_Offensive_or_Normal_Speech
- https://huggingface.co/DunnBC22/fBERT-Hate_Offensive_or_Normal_Speech
- https://huggingface.co/DunnBC22/hateBERT-Hate_Offensive_or_Normal_Speech

## Intended uses & limitations

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

The main limitation is the quality of the data source.

## Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/subhajournal/normal-hate-and-offensive-speeches

## 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: 3

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.8735        | 1.0   | 39   | 0.5860          | 0.6951   | 0.5798      | 0.6951   | 0.5406   | 0.6951          | 0.6951       | 0.6483       | 0.4975             | 0.6951          | 0.4638          |
| 0.3954        | 2.0   | 78   | 0.2329          | 0.9410   | 0.9401      | 0.9410   | 0.9376   | 0.9410          | 0.9410       | 0.9349       | 0.9419             | 0.9410          | 0.9431          |
| 0.2048        | 3.0   | 117  | 0.1606          | 0.9607   | 0.9607      | 0.9607   | 0.9592   | 0.9607          | 0.9607       | 0.9579       | 0.9613             | 0.9607          | 0.9613          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.12.1
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-Hate_Offensive_or_Normal_Speech", "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-Hate\_Offensive\_or\_Normal\_Speech
===========================================================


This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1606
* Accuracy: 0.9607
* Weighted f1: 0.9607
* Micro f1: 0.9607
* Macro f1: 0.9592
* Weighted recall: 0.9607
* Micro recall: 0.9607
* Macro recall: 0.9579
* Weighted precision: 0.9613
* Micro precision: 0.9607
* Macro precision: 0.9613


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


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


### Associated Models


This project is part of a comparison that included the following models:


* URL
* URL
* URL
* URL


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


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


The main limitation is the quality of the data source.


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: 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: 3


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.12.1



### Associated Models


This project is part of a comparison that included the following models:


* URL
* URL
* URL
* URL


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


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


The main limitation is the quality of the data source.


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: 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: 3 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.9.0
* 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 
 ### Associated Models


This project is part of a comparison that included the following models:


* URL
* URL
* URL
* URL


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


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


The main limitation is the quality of the data source.


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: 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: 3 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.9.0
* 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 
### Associated Models


This project is part of a comparison that included the following models:


* URL
* URL
* URL
* URL


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


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


The main limitation is the quality of the data source.


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: 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: 3### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.12.1