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# dougtrajano/toxicity-target-type-identification

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: 1.4281
- Accuracy: 0.8002
- F1: 0.7986
- Precision: 0.7990
- Recall: 0.8002

## 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: 3.952388499692274e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1993
- optimizer: Adam with betas=(0.9944095815441554,0.8750000522553327) and epsilon=1.8526084265228802e-07
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 355  | 0.7145          | 0.6903   | 0.7052 | 0.7528    | 0.6903 |
| 0.8011        | 2.0   | 710  | 0.9930          | 0.7928   | 0.7840 | 0.7835    | 0.7928 |
| 0.529         | 3.0   | 1065 | 1.4281          | 0.8002   | 0.7986 | 0.7990    | 0.8002 |
| 0.529         | 4.0   | 1420 | 1.6783          | 0.7727   | 0.7753 | 0.7788    | 0.7727 |
| 0.2706        | 5.0   | 1775 | 2.3904          | 0.7727   | 0.7683 | 0.7660    | 0.7727 |


### 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/toxicity-target-type-identification", "results": []}]}
transformers pytorch 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 #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/toxicity-target-type-identification
===============================================


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: 1.4281
* Accuracy: 0.8002
* F1: 0.7986
* Precision: 0.7990
* Recall: 0.8002


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: 3.952388499692274e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1993
* optimizer: Adam with betas=(0.9944095815441554,0.8750000522553327) and epsilon=1.8526084265228802e-07
* lr\_scheduler\_type: linear
* num\_epochs: 30


### 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: 3.952388499692274e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1993
* optimizer: Adam with betas=(0.9944095815441554,0.8750000522553327) and epsilon=1.8526084265228802e-07
* lr\_scheduler\_type: linear
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.10.2+cu113
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #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: 3.952388499692274e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1993
* optimizer: Adam with betas=(0.9944095815441554,0.8750000522553327) and epsilon=1.8526084265228802e-07
* lr\_scheduler\_type: linear
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.10.2+cu113
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #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: 3.952388499692274e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1993
* optimizer: Adam with betas=(0.9944095815441554,0.8750000522553327) and epsilon=1.8526084265228802e-07
* lr\_scheduler\_type: linear
* num\_epochs: 30### Training results### Framework versions


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