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# bert-large-uncased-Hate_Offensive_or_Normal_Speech

This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0443
- Accuracy: 0.9869
- Weighted f1: 0.9869
- Micro f1: 0.9869
- Macro f1: 0.9863
- Weighted recall: 0.9869
- Micro recall: 0.9869
- Macro recall: 0.9857
- Weighted precision: 0.9869
- Micro precision: 0.9869
- Macro precision: 0.9870

## 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-Large.ipynb

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

- 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
- 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: 5

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.7991        | 1.0   | 39   | 0.4235          | 0.7430   | 0.7100      | 0.7430   | 0.6902   | 0.7430          | 0.7430       | 0.7049       | 0.7782             | 0.7430          | 0.7886          |
| 0.2156        | 2.0   | 78   | 0.1072          | 0.9607   | 0.9605      | 0.9607   | 0.9585   | 0.9607          | 0.9607       | 0.9569       | 0.9607             | 0.9607          | 0.9605          |
| 0.0518        | 3.0   | 117  | 0.0518          | 0.9869   | 0.9869      | 0.9869   | 0.9863   | 0.9869          | 0.9869       | 0.9857       | 0.9869             | 0.9869          | 0.9870          |
| 0.0242        | 4.0   | 156  | 0.0500          | 0.9853   | 0.9852      | 0.9853   | 0.9845   | 0.9853          | 0.9853       | 0.9841       | 0.9853             | 0.9853          | 0.9850          |
| 0.0163        | 5.0   | 195  | 0.0443          | 0.9869   | 0.9869      | 0.9869   | 0.9863   | 0.9869          | 0.9869       | 0.9857       | 0.9869             | 0.9869          | 0.9870          |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.12.1
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-large-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-large-uncased-Hate\_Offensive\_or\_Normal\_Speech
======================================================


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


* Loss: 0.0443
* Accuracy: 0.9869
* Weighted f1: 0.9869
* Micro f1: 0.9869
* Macro f1: 0.9863
* Weighted recall: 0.9869
* Micro recall: 0.9869
* Macro recall: 0.9857
* Weighted precision: 0.9869
* Micro precision: 0.9869
* Macro precision: 0.9870


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: 5


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.8.0
* 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: 5 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.8.0
* 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: 5 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.8.0
* 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: 5### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.12.1