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---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
- f1
model-index:
- name: roberta-base-suicide-prediction-phr-v2
results:
- task:
type: text-classification
name: Suicidal Tendency Prediction in text
dataset:
type: vibhorag101/phr_suicide_prediction_dataset_clean_light
name: Suicide Prediction Dataset
split: val
metrics:
- type: accuracy
value: 0.9869
- type: f1
value: 0.9875
- type: recall
value: 0.9846
- type: precision
value: 0.9904
datasets:
- vibhorag101/phr_suicide_prediction_dataset_clean_light
language:
- en
library_name: transformers
---
<!-- 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. -->
# vibhorag101/roberta-base-suicide-prediction-phr-v2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on [Suicide Prediction Dataset](https://huggingface.co/datasets/vibhorag101/phr_suicide_prediction_dataset_clean_light), sourced from Reddit.
It achieves the following results on the evaluation set:
- Loss: 0.0553
- Accuracy: 0.9869
- Recall: 0.9846
- Precision: 0.9904
- F1: 0.9875
## Model description
This model is a finetune of roberta-base to detect suicidal tendencies in a given text.
## Training and evaluation data
- The dataset is sourced from Reddit and is available on [Kaggle](https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch).
- The dataset contains text with binary labels for suicide or non-suicide.
- The dataset was cleaned minimally, as BERT depends on contextually sensitive information, which can worsely effect its performance.
- Removed numbers
- Removed URLs, Emojis, and accented characters.
- Remove any extra white spaces and any extra spaces after a single space.
- Removed any consecutive characters repeated more than 3 times.
- The rows with more than 512 BERT Tokens were removed, as they exceeded BERT's max token.
- The cleaned dataset can be found [here](https://huggingface.co/datasets/vibhorag101/phr_suicide_prediction_dataset_clean_light)
- The evaluation set had ~33k samples, while the training set had ~153k samples, i.e., a 70:15:15 (train:test:val) split.
## Training procedure
- The model was trained on an RTXA5000 GPU.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- weight_decay=0.1
- warmup_ratio: 0.06
- num_epochs: 3
- eval_steps: 500
- save_steps: 500
- Early Stopping:
- early_stopping_patience: 5
- early_stopping_threshold: 0.001
- parameter: F1 Score
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1928 | 0.05 | 500 | 0.2289 | 0.9340 | 0.9062 | 0.9660 | 0.9352 |
| 0.0833 | 0.1 | 1000 | 0.1120 | 0.9752 | 0.9637 | 0.9888 | 0.9761 |
| 0.0366 | 0.16 | 1500 | 0.1165 | 0.9753 | 0.9613 | 0.9915 | 0.9762 |
| 0.071 | 0.21 | 2000 | 0.0973 | 0.9709 | 0.9502 | 0.9940 | 0.9716 |
| 0.0465 | 0.26 | 2500 | 0.0680 | 0.9829 | 0.9979 | 0.9703 | 0.9839 |
| 0.0387 | 0.31 | 3000 | 0.1583 | 0.9705 | 0.9490 | 0.9945 | 0.9712 |
| 0.1061 | 0.37 | 3500 | 0.0685 | 0.9848 | 0.9802 | 0.9907 | 0.9854 |
| 0.0593 | 0.42 | 4000 | 0.0550 | 0.9872 | 0.9947 | 0.9813 | 0.9879 |
| 0.0382 | 0.47 | 4500 | 0.0551 | 0.9871 | 0.9912 | 0.9842 | 0.9877 |
| 0.0831 | 0.52 | 5000 | 0.0502 | 0.9840 | 0.9768 | 0.9927 | 0.9847 |
| 0.0376 | 0.58 | 5500 | 0.0654 | 0.9865 | 0.9852 | 0.9889 | 0.9871 |
| 0.0634 | 0.63 | 6000 | 0.0422 | 0.9877 | 0.9897 | 0.9870 | 0.9883 |
| 0.0235 | 0.68 | 6500 | 0.0553 | 0.9869 | 0.9846 | 0.9904 | 0.9875 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0
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