metadata

license: mit
base_model: roberta-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - recall
  - precision
  - f1
model-index:
  - name: roberta-base-suicide-prediction-phr
    results:
      - task:
          type: text-classification
          name: Suicidal Tendency Prediction in text
        dataset:
          type: vibhorag101/roberta-base-suicide-prediction-phr
          name: Suicide Prediction Dataset
          split: test
        metrics:
          - type: accuracy
            value: 0.9652972367116438
          - type: f1
            value: 0.9651921995935487
          - type: recall
            value: 0.966571403827834
          - type: precision
            value: 0.9638169257340242
datasets:
  - vibhorag101/suicide_prediction_dataset_phr
language:
  - en
library_name: transformers

roberta-base-suicide-prediction-phr

This model is a fine-tuned version of roberta-base on this dataset sourced from Reddit. It achieves the following results on the evaluation/validation set:

Loss: 0.1543
Accuracy: 0.9652972367116438
Recall: 0.966571403827834
Precision: 0.9638169257340242
F1: 0.9651921995935487

It achieves the following result on validation partition of this updated dataset

Loss: 0.08761
Accuracy: 0.97065
Recall: 0.96652
Precision: 0.97732
F1: 0.97189

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.
The dataset contains text with binary labels for suicide or non-suicide.
The dataset was cleaned, and following steps were applied
- Converted to lowercase
- Removed numbers and special characters.
- Removed URLs, Emojis and accented characters.
- Removed any word contractions.
- Remove any extra white spaces and any extra spaces after a single space.
- Removed any consecutive characters repeated more than 3 times.
- Tokenised the text, then lemmatized it and then removed the stopwords (excluding not).
The cleaned dataset can be found here
The evaluation set had ~23000 samples, while the training set had ~186k samples, i.e. a 80:10:10 (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: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Recall	Precision	F1
0.2023	0.09	1000	0.1868	{'accuracy': 0.9415010561710566}	{'recall': 0.9389451805663809}	{'precision': 0.943274752044545}	{'f1': 0.9411049867627274}
0.1792	0.17	2000	0.1465	{'accuracy': 0.9528387291460103}	{'recall': 0.9615484541439335}	{'precision': 0.9446949714966392}	{'f1': 0.9530472103004292}
0.1596	0.26	3000	0.1871	{'accuracy': 0.9523645298961072}	{'recall': 0.9399844115354637}	{'precision': 0.9634297887448962}	{'f1': 0.9515627054749485}
0.1534	0.34	4000	0.1563	{'accuracy': 0.9518041126007674}	{'recall': 0.974971854161254}	{'precision': 0.9314139157772814}	{'f1': 0.9526952695269527}
0.1553	0.43	5000	0.1691	{'accuracy': 0.9513730223735828}	{'recall': 0.93141075604053}	{'precision': 0.9697051663510955}	{'f1': 0.950172276702889}
0.1537	0.52	6000	0.1347	{'accuracy': 0.9568478682588266}	{'recall': 0.9644063393089114}	{'precision': 0.9496844618795839}	{'f1': 0.9569887852876723}
0.1515	0.6	7000	0.1276	{'accuracy': 0.9565461050997974}	{'recall': 0.9426690915389279}	{'precision': 0.9691924138545098}	{'f1': 0.9557467732022126}
0.1453	0.69	8000	0.1351	{'accuracy': 0.960210372030866}	{'recall': 0.9589503767212263}	{'precision': 0.961031070994619}	{'f1': 0.959989596428107}
0.1526	0.78	9000	0.1423	{'accuracy': 0.9610725524852352}	{'recall': 0.9612020438209059}	{'precision': 0.9606196988056085}	{'f1': 0.9609107830829834}
0.1437	0.86	10000	0.1365	{'accuracy': 0.9599948269172738}	{'recall': 0.9625010825322594}	{'precision': 0.9573606684468946}	{'f1': 0.9599239937813093}
0.1317	0.95	11000	0.1275	{'accuracy': 0.9616760788032935}	{'recall': 0.9653589676972374}	{'precision': 0.9579752492265383}	{'f1': 0.9616529353405513}
0.125	1.03	12000	0.1428	{'accuracy': 0.9608138983489244}	{'recall': 0.9522819780029445}	{'precision': 0.9684692619341201}	{'f1': 0.9603074101567617}
0.1135	1.12	13000	0.1627	{'accuracy': 0.960770789326206}	{'recall': 0.9544470425218672}	{'precision': 0.966330556773345}	{'f1': 0.9603520390379923}
0.1096	1.21	14000	0.1240	{'accuracy': 0.9624520412122257}	{'recall': 0.9566987096215467}	{'precision': 0.9675074443860571}	{'f1': 0.962072719355541}
0.1213	1.29	15000	0.1502	{'accuracy': 0.9616760788032935}	{'recall': 0.9659651857625358}	{'precision': 0.9574248927038627}	{'f1': 0.9616760788032936}
0.1166	1.38	16000	0.1574	{'accuracy': 0.958873992326594}	{'recall': 0.9438815276695246}	{'precision': 0.9726907630522088}	{'f1': 0.9580696202531646}
0.1214	1.47	17000	0.1626	{'accuracy': 0.9562443419407682}	{'recall': 0.9773101238416905}	{'precision': 0.9374480810765908}	{'f1': 0.9569641721433114}
0.1064	1.55	18000	0.1653	{'accuracy': 0.9624089321895073}	{'recall': 0.9622412747899888}	{'precision': 0.9622412747899888}	{'f1': 0.9622412747899888}
0.1046	1.64	19000	0.1608	{'accuracy': 0.9640039660300901}	{'recall': 0.9697756993158396}	{'precision': 0.9584046559397467}	{'f1': 0.9640566484438896}
0.1043	1.72	20000	0.1556	{'accuracy': 0.960770789326206}	{'recall': 0.9493374902572097}	{'precision': 0.9712058119961017}	{'f1': 0.9601471489883507}
0.0995	1.81	21000	0.1646	{'accuracy': 0.9602534810535845}	{'recall': 0.9752316619035247}	{'precision': 0.9465411448264268}	{'f1': 0.9606722402320423}
0.1065	1.9	22000	0.1721	{'accuracy': 0.9627106953485365}	{'recall': 0.9710747380271932}	{'precision': 0.9547854223433242}	{'f1': 0.9628611910179897}
0.1204	1.98	23000	0.1214	{'accuracy': 0.9629693494848471}	{'recall': 0.961028838659392}	{'precision': 0.9644533286980705}	{'f1': 0.9627380384331756}
0.0852	2.07	24000	0.1583	{'accuracy': 0.9643919472345562}	{'recall': 0.9624144799515025}	{'precision': 0.9659278574532811}	{'f1': 0.9641679680721846}
0.0812	2.16	25000	0.1594	{'accuracy': 0.9635728758029055}	{'recall': 0.9572183251060882}	{'precision': 0.9692213258505787}	{'f1': 0.9631824321380331}
0.0803	2.24	26000	0.1629	{'accuracy': 0.9639177479846532}	{'recall': 0.9608556334978783}	{'precision': 0.9664634146341463}	{'f1': 0.963651365787988}
0.0832	2.33	27000	0.1570	{'accuracy': 0.9631417855757209}	{'recall': 0.9658785831817788}	{'precision': 0.9603065266058206}	{'f1': 0.9630844954881052}
0.0887	2.41	28000	0.1551	{'accuracy': 0.9623227141440703}	{'recall': 0.9669178141508616}	{'precision': 0.9577936004117698}	{'f1': 0.9623340803309774}
0.084	2.5	29000	0.1585	{'accuracy': 0.9644350562572747}	{'recall': 0.9613752489824197}	{'precision': 0.96698606271777}	{'f1': 0.9641724931602031}
0.0807	2.59	30000	0.1601	{'accuracy': 0.9639177479846532}	{'recall': 0.9699489044773534}	{'precision': 0.9580838323353293}	{'f1': 0.9639798597065025}
0.079	2.67	31000	0.1645	{'accuracy': 0.9628400224166919}	{'recall': 0.9558326838139777}	{'precision': 0.9690929844586882}	{'f1': 0.9624171607952564}
0.0913	2.76	32000	0.1560	{'accuracy': 0.9642626201664009}	{'recall': 0.964752749631939}	{'precision': 0.9635011243729459}	{'f1': 0.9641265307888701}
0.0927	2.85	33000	0.1491	{'accuracy': 0.9649523645298961}	{'recall': 0.9659651857625358}	{'precision': 0.9637117677553136}	{'f1': 0.9648371610224472}
0.0882	2.93	34000	0.1543	{'accuracy': 0.9652972367116438}	{'recall': 0.966571403827834}	{'precision': 0.9638169257340242}	{'f1': 0.9651921995935487}

Framework versions

Transformers 4.31.0
Pytorch 2.1.0+cu121
Datasets 2.14.5
Tokenizers 0.13.3