
# Detección de acoso en Twitter Español

This model is a fine-tuned version of [mrm8488/distilroberta-finetuned-tweets-hate-speech](https://huggingface.co/mrm8488/distilroberta-finetuned-tweets-hate-speech) on [hackathon-pln-es/Dataset-Acoso-Twitter-Es](https://huggingface.co/datasets/hackathon-pln-es/Dataset-Acoso-Twitter-Es).

It achieves the following results on the evaluation set:
- Loss: 0.1628
- Accuracy: 0.9167

# UNL: Universidad Nacional de Loja

## Miembros del equipo:
- Anderson Quizhpe <br>
- Luis Negrón <br>
- David Pacheco <br>
- Bryan Requenes <br>
- Paul Pasaca

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6732        | 1.0   | 27   | 0.3797          | 0.875    |
| 0.5537        | 2.0   | 54   | 0.3242          | 0.9167   |
| 0.5218        | 3.0   | 81   | 0.2879          | 0.9167   |
| 0.509         | 4.0   | 108  | 0.2606          | 0.9167   |
| 0.4196        | 5.0   | 135  | 0.1628          | 0.9167   |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6

{"language": "es", "license": "apache-2.0", "tags": ["generated_from_trainer", "es", "text-classification", "acoso", "twitter", "cyberbullying"], "datasets": ["hackathon-pln-es/Dataset-Acoso-Twitter-Es"], "metrics": ["accuracy"], "widget": [{"text": "Que horrible como la far\u00e1ndula chilena siempre se encargaba de dejar mal a las mujeres. Un asco"}, {"text": "Hay que ser bien menestra para amenazar a una mujer con una llave de ruedas. Viendo como se viste no me queda ninguna duda"}, {"text": "m\u00e1s centrados en tener una sociedad reprimida y sumisa que en estudiar y elaborar pol\u00edticas de protecci\u00f3n hacia las personas de mayor riesgo ante el virus."}], "base_model": "mrm8488/distilroberta-finetuned-tweets-hate-speech", "model-index": [{"name": "Detecci\u00f3n de acoso en Twitter", "results": []}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer es acoso twitter cyberbullying dataset:hackathon-pln-es/Dataset-Acoso-Twitter-Es base_model:mrm8488/distilroberta-finetuned-tweets-hate-speech license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #es #acoso #twitter #cyberbullying #dataset-hackathon-pln-es/Dataset-Acoso-Twitter-Es #base_model-mrm8488/distilroberta-finetuned-tweets-hate-speech #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

Detección de acoso en Twitter Español
=====================================


This model is a fine-tuned version of mrm8488/distilroberta-finetuned-tweets-hate-speech on hackathon-pln-es/Dataset-Acoso-Twitter-Es.


It achieves the following results on the evaluation set:


* Loss: 0.1628
* Accuracy: 0.9167


UNL: Universidad Nacional de Loja
=================================


Miembros del equipo:
--------------------


* Anderson Quizhpe
* Luis Negrón
* David Pacheco
* Bryan Requenes
* Paul Pasaca


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


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #es #acoso #twitter #cyberbullying #dataset-hackathon-pln-es/Dataset-Acoso-Twitter-Es #base_model-mrm8488/distilroberta-finetuned-tweets-hate-speech #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #es #acoso #twitter #cyberbullying #dataset-hackathon-pln-es/Dataset-Acoso-Twitter-Es #base_model-mrm8488/distilroberta-finetuned-tweets-hate-speech #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 5### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6