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richie-ghost
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setfit-mental-bert-base-uncased-Suicidal-Topic-Check

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
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SetFit with mental/mental-bert-base-uncased

This is a SetFit model that can be used for Text Classification. This SetFit model uses mental/mental-bert-base-uncased as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

  1. Fine-tuning a Sentence Transformer with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Sources

Model Labels

Label Examples
Presence of a loved one
  • "Living alone feels like a double-edged sword, on one hand, I have my privacy, but on the other hand, there's no one around to support me when I'm feeling like shit."
  • 'I have no one to talk to or no one who understands me.'
  • 'I wish I had a shoulder to lean on when everything is falling apart.'
Previous attempt
  • 'Shit got real after my last attempt, i woke up surrounded by a bunch of medical equipment, wondering what the fuck happened.'
  • "Looking back at my attempts, I can't deny there's a part of me that's glad they didn't work, but there's also this nagging voice wondering if I missed my chance."
  • "In the past, I tried to end it all, but I'm in the same situation now."
Ability to take care of oneself
  • "I've let go of everything that used to matter to me, my hobbies, my friends, everything seems uninteresting."
  • "It's hard to maintain a routine or assume my responsibilities, I've completely dropped out."
  • "I'm so disoriented, I forget important things and it's making my situation worse."
Ability to hope for change
  • "I'm just so tired of feeling stuck, every day feels the same, and I can't see any way out, I need help finding hope for a change in my life.,"
  • 'I feel so isolated in all of this, like nobody would ever understand what I’m going through.'
  • "I don't know what to do anymore, it feels like my reason to live has been completely drained away, i'm desperately searching for any glimmer of hope to hold onto.,"
Other
  • "My garden has been thriving this summer, it's so rewarding to see everything in bloom."
  • "I've been trying to be more sustainable, I even started composting my food scraps."
  • "I've been learning to play the piano lately, it's so fulfilling to see my progress."
Suicidal planning
  • "I don't want to be here anymore, I don't want to endure this, I'm seriously thinking about killing myself."
  • "I've had thoughts of ending my life."
  • "Sometimes, I imagine myself in this life, and I don't see the point of it all."
Ability to control oneself
  • "I wish I could put my brain on pause, just so I don't act on impulse all the time."
  • 'I try to focus, but my thoughts are too fast and I act before considering the consequences.'
  • "I can't stop myself from falling apart, no matter how hard I try."
Consumption
  • "Drinking has become my go-to escape from this overwhelming situation, It's like I can't deal with it sober anymore."
  • "The more I struggle with this distressing situation, the more I find solace in drinking, It's becoming a dangerous habit that's tough to break."
  • 'Lately, when shit hits the fan, I find myself reaching for the bottle'

Evaluation

Metrics

Label Accuracy
all 0.8276

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("richie-ghost/setfit-mental-bert-base-uncased-Suicidal-Topic-Check")
# Run inference
preds = model("There's no structure in my life, and that makes me even sicker.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 7 18.3582 40
Label Training Sample Count
Suicidal planning 9
Previous attempt 11
Presence of a loved one 8
Other 9
Consumption 6
Ability to take care of oneself 8
Ability to hope for change 7
Ability to control oneself 9

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (10, 10)
  • max_steps: -1
  • sampling_strategy: oversampling
  • body_learning_rate: (2e-05, 1e-05)
  • head_learning_rate: 0.01
  • loss: CosineSimilarityLoss
  • distance_metric: cosine_distance
  • margin: 0.25
  • end_to_end: False
  • use_amp: False
  • warmup_proportion: 0.1
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: True

Training Results

Epoch Step Training Loss Validation Loss
0.0041 1 0.3127 -
0.2041 50 0.1378 -
0.4082 100 0.0519 -
0.6122 150 0.0043 -
0.8163 200 0.0014 -
1.0 245 - 0.0717
1.0204 250 0.0008 -
1.2245 300 0.0006 -
1.4286 350 0.0006 -
1.6327 400 0.0003 -
1.8367 450 0.0005 -
2.0 490 - 0.0693
2.0408 500 0.0005 -
2.2449 550 0.0006 -
2.4490 600 0.0005 -
2.6531 650 0.0003 -
2.8571 700 0.0003 -
3.0 735 - 0.0698
0.0041 1 0.0003 -
0.2041 50 0.0006 -
0.4082 100 0.0004 -
0.6122 150 0.001 -
0.8163 200 0.0002 -
1.0 245 - 0.0633
1.0204 250 0.0002 -
1.2245 300 0.0 -
1.4286 350 0.0001 -
1.6327 400 0.0001 -
1.8367 450 0.0001 -
2.0 490 - 0.0598
2.0408 500 0.0001 -
2.2449 550 0.0001 -
2.4490 600 0.0001 -
2.6531 650 0.0001 -
2.8571 700 0.0001 -
3.0 735 - 0.0585
3.0612 750 0.0001 -
3.2653 800 0.0001 -
3.4694 850 0.0001 -
3.6735 900 0.0001 -
3.8776 950 0.0 -
4.0 980 - 0.0582
4.0816 1000 0.0001 -
4.2857 1050 0.0 -
4.4898 1100 0.0 -
4.6939 1150 0.0 -
4.8980 1200 0.0 -
5.0 1225 - 0.0583
5.1020 1250 0.0 -
5.3061 1300 0.0 -
5.5102 1350 0.0 -
5.7143 1400 0.0 -
5.9184 1450 0.0 -
6.0 1470 - 0.0561
0.0041 1 0.0 -
0.2041 50 0.0 -
0.4082 100 0.0001 -
0.6122 150 0.0002 -
0.8163 200 0.0002 -
1.0 245 - 0.0699
1.0204 250 0.0001 -
1.2245 300 0.0001 -
1.4286 350 0.0 -
1.6327 400 0.0 -
1.8367 450 0.0 -
2.0 490 - 0.0653
2.0408 500 0.0001 -
2.2449 550 0.0 -
2.4490 600 0.0 -
2.6531 650 0.0001 -
2.8571 700 0.0001 -
3.0 735 - 0.0651
3.0612 750 0.0 -
3.2653 800 0.0 -
3.4694 850 0.0 -
3.6735 900 0.0 -
3.8776 950 0.0001 -
4.0 980 - 0.0634
4.0816 1000 0.0 -
4.2857 1050 0.0 -
4.4898 1100 0.0 -
4.6939 1150 0.0 -
4.8980 1200 0.0 -
5.0 1225 - 0.0654
5.1020 1250 0.0 -
5.3061 1300 0.0 -
5.5102 1350 0.0 -
5.7143 1400 0.0 -
5.9184 1450 0.0 -
6.0 1470 - 0.0627
6.1224 1500 0.0 -
6.3265 1550 0.0 -
6.5306 1600 0.0 -
6.7347 1650 0.0 -
6.9388 1700 0.0 -
7.0 1715 - 0.0648
7.1429 1750 0.0 -
7.3469 1800 0.0 -
7.5510 1850 0.0 -
7.7551 1900 0.0 -
7.9592 1950 0.0 -
8.0 1960 - 0.0636
8.1633 2000 0.0 -
8.3673 2050 0.0 -
8.5714 2100 0.0 -
8.7755 2150 0.0 -
8.9796 2200 0.0 -
9.0 2205 - 0.0648
9.1837 2250 0.0 -
9.3878 2300 0.0 -
9.5918 2350 0.0 -
9.7959 2400 0.0 -
10.0 2450 0.0 0.0643
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 2.7.0
  • Transformers: 4.40.0
  • PyTorch: 2.2.1+cu121
  • Datasets: 2.19.0
  • Tokenizers: 0.19.1

Citation

BibTeX 

@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}
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