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