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# DeBERTa_Offensive_English_Twitter

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2527
- Macro F1: 0.9072
- Micro F1: 0.9114
- Accuracy: 0.9114

Results on the test set:

Accuracy: 0.9203707604803033 

F1 score: 0.9030805039045081

Precision: 0.8948599346467434

Recall : 0.9127112237048041

Matthews Correlation Coefficient: 0.8073738336608549

Precision of each class: [0.95849624 0.83122363]

Recall of each class: [0.9299679  0.89545455]

F1 score of each class: [0.94401659 0.86214442]


## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Macro F1 | Micro F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:--------:|
| 0.3045        | 1.0   | 4036  | 0.3042          | 0.8903   | 0.8968   | 0.8968   |
| 0.2935        | 2.0   | 8073  | 0.3068          | 0.8877   | 0.8921   | 0.8921   |
| 0.2478        | 3.0   | 12109 | 0.2746          | 0.9031   | 0.9072   | 0.9072   |
| 0.2041        | 4.0   | 16144 | 0.2527          | 0.9072   | 0.9114   | 0.9114   |


### Framework versions

- Transformers 4.27.1
- Pytorch 2.0.1+cu118
- Datasets 2.9.0
- Tokenizers 0.13.3
{"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["christinacdl/Offensive_Twitter_Dataset"], "metrics": ["accuracy", "f1", "precision", "recall"], "pipeline_tag": "text-classification", "model-index": [{"name": "DeBERTa_Offensive_English_Twitter", "results": []}]}
transformers pytorch tensorboard deberta-v2 text-classification generated_from_trainer en dataset:christinacdl/Offensive_Twitter_Dataset license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #en #dataset-christinacdl/Offensive_Twitter_Dataset #license-mit #autotrain_compatible #endpoints_compatible #region-us 

DeBERTa\_Offensive\_English\_Twitter
====================================


This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2527
* Macro F1: 0.9072
* Micro F1: 0.9114
* Accuracy: 0.9114


Results on the test set:


Accuracy: 0.9203707604803033


F1 score: 0.9030805039045081


Precision: 0.8948599346467434


Recall : 0.9127112237048041


Matthews Correlation Coefficient: 0.8073738336608549


Precision of each class: [0.95849624 0.83122363]


Recall of each class: [0.9299679 0.89545455]


F1 score of each class: [0.94401659 0.86214442]


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


More information needed


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


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 10
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.1
* Pytorch 2.0.1+cu118
* Datasets 2.9.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 10
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 2.0.1+cu118
* Datasets 2.9.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #en #dataset-christinacdl/Offensive_Twitter_Dataset #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 10
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 2.0.1+cu118
* Datasets 2.9.0
* Tokenizers 0.13.3