

# Model Description

This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) to detect suicidal ideation/behavior in public comments (reddit, forums, twitter, etc.) using the Spanish language.

# How to use

```python
>>> from transformers import pipeline


>>> model_name= 'hackathon-somos-nlp-2023/roberta-base-bne-finetuned-suicide-es'
>>> pipe = pipeline("text-classification", model=model_name)

>>> pipe("Quiero acabar con todo. No merece la pena vivir.")
[{'label': 'Suicide', 'score': 0.9999703168869019}]

>>> pipe("El partido de fútbol fue igualado, disfrutamos mucho jugando juntos.")
[{'label': 'Non-Suicide', 'score': 0.999990701675415}]
```


# Training

## Training data

The dataset consists of comments on Reddit, Twitter, and inputs/outputs of the Alpaca dataset translated to Spanish language and classified as suicidal ideation/behavior and non-suicidal.

The dataset has 10050 rows (777 considered as Suicidal Ideation/Behavior and 9273 considered Non-Suicidal).

More info: https://huggingface.co/datasets/hackathon-somos-nlp-2023/suicide-comments-es

## Training procedure

The training data has been tokenized using the `PlanTL-GOB-ES/roberta-base-bne` tokenizer with a vocabulary size of 50262 tokens and a model maximum length of 512 tokens.

The training lasted a total of 10 minutes using a NVIDIA GPU GeForce RTX 3090 provided by Q Blocks.

```
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  GeForce RTX 3090    Off  | 00000000:68:00.0 Off |                  N/A |
| 31%   50C    P8    25W / 250W |      1MiB / 24265MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
```


# Considerations for Using the Model

The model is designed for use in Spanish language, specifically to detect suicidal ideation/behavior.

## Limitations

It is a research toy project. Don't expect a professional, bug-free model. We have found some false positives and false negatives. If you find a bug, please send us your feedback.

## Bias

No measures have been taken to estimate the bias and toxicity embedded in the model or dataset. However, the model was fine-tuned using a dataset mainly collected on Reddit, Twitter, and ChatGPT. So there is probably an age bias because [the Internet is used more by younger people](https://www.statista.com/statistics/272365/age-distribution-of-internet-users-worldwide).

In addition, this model inherits biases from its original base model. You can review these biases by visiting the following [link](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne#limitations-and-bias).


# Evaluation


## Metric

F1 = 2 * (precision * recall) / (precision + recall)

## 5 K fold

We use [KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html) with `n_splits=5` to evaluate the model.

Results:

```
>>> best_f1_model_by_fold = [0.9163879598662207, 0.9380530973451328, 0.9333333333333333, 0.8943661971830986, 0.9226190476190477]
>>> best_f1_model_by_fold.mean()
0.9209519270693666
```


# Additional Information

## Team

* [dariolopez](https://huggingface.co/dariolopez)
* [diegogd](https://huggingface.co/diegogd)

## Licesing

This work is licensed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)

## Demo (Space)

https://huggingface.co/spaces/hackathon-somos-nlp-2023/suicide-comments-es
{"language": ["es"], "license": "apache-2.0", "datasets": ["hackathon-somos-nlp-2023/suicide-comments-es"], "metrics": ["f1"], "pipeline_tag": "text-classification"}
transformers pytorch roberta text-classification es dataset:hackathon-somos-nlp-2023/suicide-comments-es license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #roberta #text-classification #es #dataset-hackathon-somos-nlp-2023/suicide-comments-es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 



# Model Description

This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne to detect suicidal ideation/behavior in public comments (reddit, forums, twitter, etc.) using the Spanish language.

# How to use




# Training

## Training data

The dataset consists of comments on Reddit, Twitter, and inputs/outputs of the Alpaca dataset translated to Spanish language and classified as suicidal ideation/behavior and non-suicidal.

The dataset has 10050 rows (777 considered as Suicidal Ideation/Behavior and 9273 considered Non-Suicidal).

More info: URL

## Training procedure

The training data has been tokenized using the 'PlanTL-GOB-ES/roberta-base-bne' tokenizer with a vocabulary size of 50262 tokens and a model maximum length of 512 tokens.

The training lasted a total of 10 minutes using a NVIDIA GPU GeForce RTX 3090 provided by Q Blocks.




# Considerations for Using the Model

The model is designed for use in Spanish language, specifically to detect suicidal ideation/behavior.

## Limitations

It is a research toy project. Don't expect a professional, bug-free model. We have found some false positives and false negatives. If you find a bug, please send us your feedback.

## Bias

No measures have been taken to estimate the bias and toxicity embedded in the model or dataset. However, the model was fine-tuned using a dataset mainly collected on Reddit, Twitter, and ChatGPT. So there is probably an age bias because the Internet is used more by younger people.

In addition, this model inherits biases from its original base model. You can review these biases by visiting the following link.


# Evaluation


## Metric

F1 = 2 * (precision * recall) / (precision + recall)

## 5 K fold

We use KFold with 'n_splits=5' to evaluate the model.

Results:




# Additional Information

## Team

* dariolopez
* diegogd

## Licesing

This work is licensed under a Apache License, Version 2.0

## Demo (Space)

URL
# Model Description

This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne to detect suicidal ideation/behavior in public comments (reddit, forums, twitter, etc.) using the Spanish language. # How to use # Training ## Training data

The dataset consists of comments on Reddit, Twitter, and inputs/outputs of the Alpaca dataset translated to Spanish language and classified as suicidal ideation/behavior and non-suicidal.

The dataset has 10050 rows (777 considered as Suicidal Ideation/Behavior and 9273 considered Non-Suicidal).

More info: URL ## Training procedure

The training data has been tokenized using the 'PlanTL-GOB-ES/roberta-base-bne' tokenizer with a vocabulary size of 50262 tokens and a model maximum length of 512 tokens.

The training lasted a total of 10 minutes using a NVIDIA GPU GeForce RTX 3090 provided by Q Blocks. # Considerations for Using the Model

The model is designed for use in Spanish language, specifically to detect suicidal ideation/behavior. ## Limitations

It is a research toy project. Don't expect a professional, bug-free model. We have found some false positives and false negatives. If you find a bug, please send us your feedback. ## Bias

No measures have been taken to estimate the bias and toxicity embedded in the model or dataset. However, the model was fine-tuned using a dataset mainly collected on Reddit, Twitter, and ChatGPT. So there is probably an age bias because the Internet is used more by younger people.

In addition, this model inherits biases from its original base model. You can review these biases by visiting the following link. # Evaluation ## Metric

F1 = 2 * (precision * recall) / (precision + recall) ## 5 K fold

We use KFold with 'n_splits=5' to evaluate the model.

Results: # Additional Information ## Team

* dariolopez
* diegogd ## Licesing

This work is licensed under a Apache License, Version 2.0 ## Demo (Space)

URL
TAGS
#transformers #pytorch #roberta #text-classification #es #dataset-hackathon-somos-nlp-2023/suicide-comments-es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Description

This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne to detect suicidal ideation/behavior in public comments (reddit, forums, twitter, etc.) using the Spanish language. # How to use # Training ## Training data

The dataset consists of comments on Reddit, Twitter, and inputs/outputs of the Alpaca dataset translated to Spanish language and classified as suicidal ideation/behavior and non-suicidal.

The dataset has 10050 rows (777 considered as Suicidal Ideation/Behavior and 9273 considered Non-Suicidal).

More info: URL ## Training procedure

The training data has been tokenized using the 'PlanTL-GOB-ES/roberta-base-bne' tokenizer with a vocabulary size of 50262 tokens and a model maximum length of 512 tokens.

The training lasted a total of 10 minutes using a NVIDIA GPU GeForce RTX 3090 provided by Q Blocks. # Considerations for Using the Model

The model is designed for use in Spanish language, specifically to detect suicidal ideation/behavior. ## Limitations

It is a research toy project. Don't expect a professional, bug-free model. We have found some false positives and false negatives. If you find a bug, please send us your feedback. ## Bias

No measures have been taken to estimate the bias and toxicity embedded in the model or dataset. However, the model was fine-tuned using a dataset mainly collected on Reddit, Twitter, and ChatGPT. So there is probably an age bias because the Internet is used more by younger people.

In addition, this model inherits biases from its original base model. You can review these biases by visiting the following link. # Evaluation ## Metric

F1 = 2 * (precision * recall) / (precision + recall) ## 5 K fold

We use KFold with 'n_splits=5' to evaluate the model.

Results: # Additional Information ## Team

* dariolopez
* diegogd ## Licesing

This work is licensed under a Apache License, Version 2.0 ## Demo (Space)

URL
TAGS
#transformers #pytorch #roberta #text-classification #es #dataset-hackathon-somos-nlp-2023/suicide-comments-es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Description

This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne to detect suicidal ideation/behavior in public comments (reddit, forums, twitter, etc.) using the Spanish language.# How to use# Training## Training data

The dataset consists of comments on Reddit, Twitter, and inputs/outputs of the Alpaca dataset translated to Spanish language and classified as suicidal ideation/behavior and non-suicidal.

The dataset has 10050 rows (777 considered as Suicidal Ideation/Behavior and 9273 considered Non-Suicidal).

More info: URL## Training procedure

The training data has been tokenized using the 'PlanTL-GOB-ES/roberta-base-bne' tokenizer with a vocabulary size of 50262 tokens and a model maximum length of 512 tokens.

The training lasted a total of 10 minutes using a NVIDIA GPU GeForce RTX 3090 provided by Q Blocks.# Considerations for Using the Model

The model is designed for use in Spanish language, specifically to detect suicidal ideation/behavior.## Limitations

It is a research toy project. Don't expect a professional, bug-free model. We have found some false positives and false negatives. If you find a bug, please send us your feedback.## Bias

No measures have been taken to estimate the bias and toxicity embedded in the model or dataset. However, the model was fine-tuned using a dataset mainly collected on Reddit, Twitter, and ChatGPT. So there is probably an age bias because the Internet is used more by younger people.

In addition, this model inherits biases from its original base model. You can review these biases by visiting the following link.# Evaluation## Metric

F1 = 2 * (precision * recall) / (precision + recall)## 5 K fold

We use KFold with 'n_splits=5' to evaluate the model.

Results:# Additional Information## Team

* dariolopez
* diegogd## Licesing

This work is licensed under a Apache License, Version 2.0## Demo (Space)

URL