# Suicidal-ELECTRA
This text classification model predicts whether a sequence of words are suicidal (1) or non-suicidal (0).

## Data
The model was trained on the [Suicide and Depression Dataset](https://www.kaggle.com/nikhileswarkomati/suicide-watch) obtained from Kaggle. The dataset was scraped from Reddit and consists of 232,074 rows equally distributed between 2 classes - suicide and non-suicide.

## Parameters
The model fine-tuning was conducted on 1 epoch, with batch size of 6, and learning rate of 0.00001. Due to limited computing resources and time, we were unable to scale up the number of epochs and batch size.

## Performance
The model has achieved the following results after fine-tuning on the aforementioned dataset:
- Accuracy: 0.9792
- Recall: 0.9788
- Precision: 0.9677
- F1 Score: 0.9732

## How to Use
Load the model via the transformers library:
```
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("gooohjy/suicidal-electra")
model = AutoModel.from_pretrained("gooohjy/suicidal-electra")
```

## Resources
For more resources, including the source code, please refer to the GitHub repository [gohjiayi/suicidal-text-detection](https://github.com/gohjiayi/suicidal-text-detection/).
{}
transformers pytorch electra text-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us 

# Suicidal-ELECTRA
This text classification model predicts whether a sequence of words are suicidal (1) or non-suicidal (0).

## Data
The model was trained on the Suicide and Depression Dataset obtained from Kaggle. The dataset was scraped from Reddit and consists of 232,074 rows equally distributed between 2 classes - suicide and non-suicide.

## Parameters
The model fine-tuning was conducted on 1 epoch, with batch size of 6, and learning rate of 0.00001. Due to limited computing resources and time, we were unable to scale up the number of epochs and batch size.

## Performance
The model has achieved the following results after fine-tuning on the aforementioned dataset:
- Accuracy: 0.9792
- Recall: 0.9788
- Precision: 0.9677
- F1 Score: 0.9732

## How to Use
Load the model via the transformers library:


## Resources
For more resources, including the source code, please refer to the GitHub repository gohjiayi/suicidal-text-detection.
# Suicidal-ELECTRA
This text classification model predicts whether a sequence of words are suicidal (1) or non-suicidal (0). ## Data
The model was trained on the Suicide and Depression Dataset obtained from Kaggle. The dataset was scraped from Reddit and consists of 232,074 rows equally distributed between 2 classes - suicide and non-suicide. ## Parameters
The model fine-tuning was conducted on 1 epoch, with batch size of 6, and learning rate of 0.00001. Due to limited computing resources and time, we were unable to scale up the number of epochs and batch size. ## Performance
The model has achieved the following results after fine-tuning on the aforementioned dataset:
- Accuracy: 0.9792
- Recall: 0.9788
- Precision: 0.9677
- F1 Score: 0.9732 ## How to Use
Load the model via the transformers library: ## Resources
For more resources, including the source code, please refer to the GitHub repository gohjiayi/suicidal-text-detection.
TAGS
#transformers #pytorch #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us 
 # Suicidal-ELECTRA
This text classification model predicts whether a sequence of words are suicidal (1) or non-suicidal (0). ## Data
The model was trained on the Suicide and Depression Dataset obtained from Kaggle. The dataset was scraped from Reddit and consists of 232,074 rows equally distributed between 2 classes - suicide and non-suicide. ## Parameters
The model fine-tuning was conducted on 1 epoch, with batch size of 6, and learning rate of 0.00001. Due to limited computing resources and time, we were unable to scale up the number of epochs and batch size. ## Performance
The model has achieved the following results after fine-tuning on the aforementioned dataset:
- Accuracy: 0.9792
- Recall: 0.9788
- Precision: 0.9677
- F1 Score: 0.9732 ## How to Use
Load the model via the transformers library: ## Resources
For more resources, including the source code, please refer to the GitHub repository gohjiayi/suicidal-text-detection.
TAGS
#transformers #pytorch #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us 
# Suicidal-ELECTRA
This text classification model predicts whether a sequence of words are suicidal (1) or non-suicidal (0).## Data
The model was trained on the Suicide and Depression Dataset obtained from Kaggle. The dataset was scraped from Reddit and consists of 232,074 rows equally distributed between 2 classes - suicide and non-suicide.## Parameters
The model fine-tuning was conducted on 1 epoch, with batch size of 6, and learning rate of 0.00001. Due to limited computing resources and time, we were unable to scale up the number of epochs and batch size.## Performance
The model has achieved the following results after fine-tuning on the aforementioned dataset:
- Accuracy: 0.9792
- Recall: 0.9788
- Precision: 0.9677
- F1 Score: 0.9732## How to Use
Load the model via the transformers library:## Resources
For more resources, including the source code, please refer to the GitHub repository gohjiayi/suicidal-text-detection.