---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: mental/mental-bert-base-uncased
metrics:
- accuracy
widget:
- text: I let myself go, I make no effort to eat, sleep or take care of myself.
- text: There's no structure in my life, and that makes me even sicker.
- text: I'm drifting away from my friends, my family, games that I couldn't possibly
know anything about.
- text: My grandmother's homemade pasta recipe is the best, nothing else compares
to it.
- text: It's frustrating to realize I've made yet another impulsive choice that sets
me back instead of moving forward.
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with mental/mental-bert-base-uncased
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.8275862068965517
name: Accuracy
---
# SetFit with mental/mental-bert-base-uncased
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mental/mental-bert-base-uncased](https://huggingface.co/mental/mental-bert-base-uncased) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [mental/mental-bert-base-uncased](https://huggingface.co/mental/mental-bert-base-uncased)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 8 classes
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### 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:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
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
```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}
}
```