
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# xlm-roberta-base-toxicity (Spanish)

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on 2 datasets, labelled with not_toxic (0) / toxic (1) content from news or tweets.
- a private one, provided by @Newtral, containing both tweets and news.
- one used for data augmentation purposes, containing only news, obtained from [SurgeHQ.ai](https://app.surgehq.ai/datasets/spanish-toxicity)

**This model can not be used for commercial purposes**

## Training and evaluation data

The test dataset was provided by @Newtral and was kept fixed.

It achieves the following results on the evaluation set:
- eval_loss: 0.4852
- eval_f1: 0.8009
- eval_accuracy: 0.901
- eval_runtime: 13.6483
- eval_samples_per_second: 366.347
- eval_steps_per_second: 22.933
- epoch: 5.0
- step: 3595

## Training procedure
- Cleaning
- Data Augmentation
- Optuna for Grid Search
- Shap for interpretability

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 7.889038893287002e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 37
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.2+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
{"language": ["es"], "library_name": "transformers", "tags": ["generated_from_trainer", "optuna", "shap", "toxic", "toxicity", "news", "tweets"], "metrics": ["f1", "accuracy"], "pipeline_tag": "text-classification", "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned", "results": []}]}
transformers pytorch safetensors xlm-roberta text-classification generated_from_trainer optuna shap toxic toxicity news tweets es base_model:xlm-roberta-base autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #generated_from_trainer #optuna #shap #toxic #toxicity #news #tweets #es #base_model-xlm-roberta-base #autotrain_compatible #endpoints_compatible #region-us 




# xlm-roberta-base-toxicity (Spanish)

This model is a fine-tuned version of xlm-roberta-base on 2 datasets, labelled with not_toxic (0) / toxic (1) content from news or tweets.
- a private one, provided by @Newtral, containing both tweets and news.
- one used for data augmentation purposes, containing only news, obtained from URL

This model can not be used for commercial purposes

## Training and evaluation data

The test dataset was provided by @Newtral and was kept fixed.

It achieves the following results on the evaluation set:
- eval_loss: 0.4852
- eval_f1: 0.8009
- eval_accuracy: 0.901
- eval_runtime: 13.6483
- eval_samples_per_second: 366.347
- eval_steps_per_second: 22.933
- epoch: 5.0
- step: 3595

## Training procedure
- Cleaning
- Data Augmentation
- Optuna for Grid Search
- Shap for interpretability

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 7.889038893287002e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 37
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.2+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
# xlm-roberta-base-toxicity (Spanish)

This model is a fine-tuned version of xlm-roberta-base on 2 datasets, labelled with not_toxic (0) / toxic (1) content from news or tweets.
- a private one, provided by @Newtral, containing both tweets and news.
- one used for data augmentation purposes, containing only news, obtained from URL

This model can not be used for commercial purposes ## Training and evaluation data

The test dataset was provided by @Newtral and was kept fixed.

It achieves the following results on the evaluation set:
- eval_loss: 0.4852
- eval_f1: 0.8009
- eval_accuracy: 0.901
- eval_runtime: 13.6483
- eval_samples_per_second: 366.347
- eval_steps_per_second: 22.933
- epoch: 5.0
- step: 3595 ## Training procedure
- Cleaning
- Data Augmentation
- Optuna for Grid Search
- Shap for interpretability ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 7.889038893287002e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 37
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5 ### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.2+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #generated_from_trainer #optuna #shap #toxic #toxicity #news #tweets #es #base_model-xlm-roberta-base #autotrain_compatible #endpoints_compatible #region-us 
 # xlm-roberta-base-toxicity (Spanish)

This model is a fine-tuned version of xlm-roberta-base on 2 datasets, labelled with not_toxic (0) / toxic (1) content from news or tweets.
- a private one, provided by @Newtral, containing both tweets and news.
- one used for data augmentation purposes, containing only news, obtained from URL

This model can not be used for commercial purposes ## Training and evaluation data

The test dataset was provided by @Newtral and was kept fixed.

It achieves the following results on the evaluation set:
- eval_loss: 0.4852
- eval_f1: 0.8009
- eval_accuracy: 0.901
- eval_runtime: 13.6483
- eval_samples_per_second: 366.347
- eval_steps_per_second: 22.933
- epoch: 5.0
- step: 3595 ## Training procedure
- Cleaning
- Data Augmentation
- Optuna for Grid Search
- Shap for interpretability ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 7.889038893287002e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 37
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5 ### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.2+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1