
# bert-base-uncased-Hate_Offensive_or_Normal_Speech

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1750
- Accuracy: 0.9213
- Weighted f1: 0.9196
- Micro f1: 0.9213
- Macro f1: 0.9161
- Weighted recall: 0.9213
- Micro recall: 0.9213
- Macro recall: 0.9144
- Weighted precision: 0.9237
- Micro precision: 0.9213
- Macro precision: 0.9241

## 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-%20BERT.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/distilbert-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.8401        | 1.0   | 39   | 0.5510          | 0.6656   | 0.5521      | 0.6656   | 0.5233   | 0.6656          | 0.6656       | 0.6319       | 0.4740             | 0.6656          | 0.4487          |
| 0.3951        | 2.0   | 78   | 0.2724          | 0.8459   | 0.8360      | 0.8459   | 0.8274   | 0.8459          | 0.8459       | 0.8305       | 0.8743             | 0.8459          | 0.8765          |
| 0.21          | 3.0   | 117  | 0.1750          | 0.9213   | 0.9196      | 0.9213   | 0.9161   | 0.9213          | 0.9213       | 0.9144       | 0.9237             | 0.9213          | 0.9241          |


### 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": "bert-base-uncased-Hate_Offensive_or_Normal_Speech", "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-Hate\_Offensive\_or\_Normal\_Speech
=====================================================


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


* Loss: 0.1750
* Accuracy: 0.9213
* Weighted f1: 0.9196
* Micro f1: 0.9213
* Macro f1: 0.9161
* Weighted recall: 0.9213
* Micro recall: 0.9213
* Macro recall: 0.9144
* Weighted precision: 0.9237
* Micro precision: 0.9213
* Macro precision: 0.9241


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 #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 #bert #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