# Suicidal
This text categorization model can predict if a word sequence is suicidal (1) or not (0).

## Data
The model was trained on the [Suicide and Depression Dataset](https://www.kaggle.com/) obtained from Kaggle. The dataset was taken from Reddit and contains 232,074 data divided into two categories: suicide and non-suicide.

## Parameters
The model fine-tuning was conducted on 1 epoch, with a batch size of 6, and a 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
Following fine-tuning on the mentioned dataset, the model generated the subsequent results:
- Accuracy: 0.9792
- Recall: 0.9788
- Precision: 0.9677
- F1 Score: 0.9732

## How to Use
Import the model from the transformers library:
```
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Aryan4/suicidal")
model = AutoModel.from_pretrained("Aryan4/suicidal")
```

## Resources
For more resources, including the source code, please refer to the GitHub repository [Aryanstha/suicidal-text-detection](https://github.com/Aryanstha/).

{"license": "afl-3.0"}
transformers pytorch safetensors electra text-classification license:afl-3.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #electra #text-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us 

# Suicidal
This text categorization model can predict if a word sequence is suicidal (1) or not (0).

## Data
The model was trained on the Suicide and Depression Dataset obtained from Kaggle. The dataset was taken from Reddit and contains 232,074 data divided into two categories: suicide and non-suicide.

## Parameters
The model fine-tuning was conducted on 1 epoch, with a batch size of 6, and a 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
Following fine-tuning on the mentioned dataset, the model generated the subsequent results:
- Accuracy: 0.9792
- Recall: 0.9788
- Precision: 0.9677
- F1 Score: 0.9732

## How to Use
Import the model from the transformers library:


## Resources
For more resources, including the source code, please refer to the GitHub repository Aryanstha/suicidal-text-detection.

# Suicidal
This text categorization model can predict if a word sequence is suicidal (1) or not (0). ## Data
The model was trained on the Suicide and Depression Dataset obtained from Kaggle. The dataset was taken from Reddit and contains 232,074 data divided into two categories: suicide and non-suicide. ## Parameters
The model fine-tuning was conducted on 1 epoch, with a batch size of 6, and a 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
Following fine-tuning on the mentioned dataset, the model generated the subsequent results:
- Accuracy: 0.9792
- Recall: 0.9788
- Precision: 0.9677
- F1 Score: 0.9732 ## How to Use
Import the model from the transformers library: ## Resources
For more resources, including the source code, please refer to the GitHub repository Aryanstha/suicidal-text-detection.
TAGS
#transformers #pytorch #safetensors #electra #text-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Suicidal
This text categorization model can predict if a word sequence is suicidal (1) or not (0). ## Data
The model was trained on the Suicide and Depression Dataset obtained from Kaggle. The dataset was taken from Reddit and contains 232,074 data divided into two categories: suicide and non-suicide. ## Parameters
The model fine-tuning was conducted on 1 epoch, with a batch size of 6, and a 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
Following fine-tuning on the mentioned dataset, the model generated the subsequent results:
- Accuracy: 0.9792
- Recall: 0.9788
- Precision: 0.9677
- F1 Score: 0.9732 ## How to Use
Import the model from the transformers library: ## Resources
For more resources, including the source code, please refer to the GitHub repository Aryanstha/suicidal-text-detection.
passage: TAGS
#transformers #pytorch #safetensors #electra #text-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us 
# Suicidal
This text categorization model can predict if a word sequence is suicidal (1) or not (0).## Data
The model was trained on the Suicide and Depression Dataset obtained from Kaggle. The dataset was taken from Reddit and contains 232,074 data divided into two categories: suicide and non-suicide.## Parameters
The model fine-tuning was conducted on 1 epoch, with a batch size of 6, and a 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
Following fine-tuning on the mentioned dataset, the model generated the subsequent results:
- Accuracy: 0.9792
- Recall: 0.9788
- Precision: 0.9677
- F1 Score: 0.9732## How to Use
Import the model from the transformers library:## Resources
For more resources, including the source code, please refer to the GitHub repository Aryanstha/suicidal-text-detection.