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# dougtrajano/toxic-comment-classification

This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the OLID-BR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4102
- Accuracy: 0.8547
- F1: 0.8549
- Precision: 0.8669
- Recall: 0.8547

## 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: 3.255788747459486e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1993
- optimizer: Adam with betas=(0.8445637934160373,0.8338816842140165) and epsilon=2.527092625455385e-08
- lr_scheduler_type: linear
- num_epochs: 30
- label_smoothing_factor: 0.07158711257743958

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4465        | 1.0   | 1408 | 0.4102          | 0.8547   | 0.8549 | 0.8669    | 0.8547 |
| 0.3839        | 2.0   | 2816 | 0.4814          | 0.8509   | 0.8497 | 0.8532    | 0.8509 |
| 0.3945        | 3.0   | 4224 | 0.6362          | 0.8002   | 0.7918 | 0.8258    | 0.8002 |
| 0.3643        | 4.0   | 5632 | 0.4961          | 0.8248   | 0.8211 | 0.8349    | 0.8248 |
| 0.3345        | 5.0   | 7040 | 0.5267          | 0.8528   | 0.8532 | 0.8570    | 0.8528 |
| 0.3053        | 6.0   | 8448 | 0.5902          | 0.8002   | 0.7911 | 0.8292    | 0.8002 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.10.2+cu113
- Datasets 2.9.0
- Tokenizers 0.13.2

{"language": ["pt"], "license": "apache-2.0", "tags": ["toxicity", "portuguese", "hate speech", "offensive language", "generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "base_model": "neuralmind/bert-large-portuguese-cased", "model-index": [{"name": "dougtrajano/toxic-comment-classification", "results": []}]}
transformers pytorch safetensors bert text-classification toxicity portuguese hate speech offensive language generated_from_trainer pt base_model:neuralmind/bert-large-portuguese-cased license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #toxicity #portuguese #hate speech #offensive language #generated_from_trainer #pt #base_model-neuralmind/bert-large-portuguese-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

dougtrajano/toxic-comment-classification
========================================


This model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on the OLID-BR dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4102
* Accuracy: 0.8547
* F1: 0.8549
* Precision: 0.8669
* Recall: 0.8547


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: 3.255788747459486e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1993
* optimizer: Adam with betas=(0.8445637934160373,0.8338816842140165) and epsilon=2.527092625455385e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* label\_smoothing\_factor: 0.07158711257743958


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.10.2+cu113
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3.255788747459486e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1993
* optimizer: Adam with betas=(0.8445637934160373,0.8338816842140165) and epsilon=2.527092625455385e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* label\_smoothing\_factor: 0.07158711257743958 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.10.2+cu113
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #safetensors #bert #text-classification #toxicity #portuguese #hate speech #offensive language #generated_from_trainer #pt #base_model-neuralmind/bert-large-portuguese-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3.255788747459486e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1993
* optimizer: Adam with betas=(0.8445637934160373,0.8338816842140165) and epsilon=2.527092625455385e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* label\_smoothing\_factor: 0.07158711257743958 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.10.2+cu113
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #safetensors #bert #text-classification #toxicity #portuguese #hate speech #offensive language #generated_from_trainer #pt #base_model-neuralmind/bert-large-portuguese-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3.255788747459486e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1993
* optimizer: Adam with betas=(0.8445637934160373,0.8338816842140165) and epsilon=2.527092625455385e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* label\_smoothing\_factor: 0.07158711257743958### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.10.2+cu113
* Datasets 2.9.0
* Tokenizers 0.13.2