
# hateBERT-Hate_Offensive_or_Normal_Speech

This model is a fine-tuned version of [GroNLP/hateBERT](https://huggingface.co/GroNLP/hateBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1655
- Accuracy: 0.9410
- F1
  - Weighted: 0.9395
  - Micro: 0.9410
  - Macro: 0.9351
- Recall
  - Weighted: 0.9410
  - Micro: 0.9410
  - Macro: 0.9273
- Precision
  - Weighted: 0.9447
  - Micro: 0.9410
  - Macro: 0.9510

## 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-%20hateBERT.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/distilbert-base-uncased-Hate_Offensive_or_Normal_Speech
- https://huggingface.co/DunnBC22/fBERT-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.8958        | 1.0   | 39   | 0.6817          | 0.5508   | 0.4792      | 0.5508   | 0.4395   | 0.5508          | 0.5508       | 0.4853       | 0.7547             | 0.5508          | 0.7906          |
| 0.4625        | 2.0   | 78   | 0.2448          | 0.9246   | 0.9230      | 0.9246   | 0.9170   | 0.9246          | 0.9246       | 0.9103       | 0.9263             | 0.9246          | 0.9296          |
| 0.2071        | 3.0   | 117  | 0.1655          | 0.9410   | 0.9395      | 0.9410   | 0.9351   | 0.9410          | 0.9410       | 0.9273       | 0.9447             | 0.9410          | 0.9510          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.12.1
{"language": ["en"], "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "hateBERT-Hate_Offensive_or_Normal_Speech", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer en autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #autotrain_compatible #endpoints_compatible #region-us 

hateBERT-Hate\_Offensive\_or\_Normal\_Speech
============================================


This model is a fine-tuned version of GroNLP/hateBERT on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1655
* Accuracy: 0.9410
* F1
	+ Weighted: 0.9395
	+ Micro: 0.9410
	+ Macro: 0.9351
* Recall
	+ Weighted: 0.9410
	+ Micro: 0.9410
	+ Macro: 0.9273
* Precision
	+ Weighted: 0.9447
	+ Micro: 0.9410
	+ Macro: 0.9510


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 #bert #text-classification #generated_from_trainer #en #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 #bert #text-classification #generated_from_trainer #en #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