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---
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 am going through a divorce. He is extremely angry. He refuses to physically
assist me with our teenager daughter. I have no extended family support. Often
times, I feel overwhelmed, tired, and joyless. I feel out of control, sad and
depressed on a daily basis. I am just going through the motions of life every
day. I am in my mid-50s. I have almost 29 years on my job. How can I handle this?
- text: Every winter I find myself getting sad because of the weather. How can I fight
this?
- text: Adjusting to life after significant life changes
- text: "I have so many issues to address. I have a history of sexual abuse, I’m a\
\ breast cancer survivor and I am a lifetime insomniac. I have a long history\
\ of depression and I’m beginning to have anxiety. I have low self esteem but\
\ I’ve been happily married for almost 35 years.\n I’ve never had counseling\
\ about any of this. Do I have too many issues to address in counseling?"
- text: Planning a DIY home renovation project.
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: 0.9882352941176471
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:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
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### 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 |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| True | <ul><li>'I have so many issues to address. I have a history of sexual abuse, I’m a breast cancer survivor and I am a lifetime insomniac. I have a long history of depression and I’m beginning to have anxiety. I have low self esteem but I’ve been happily married for almost 35 years.\n I’ve never had counseling about any of this. Do I have too many issues to address in counseling?'</li><li>'I have so many issues to address. I have a history of sexual abuse, I’m a breast cancer survivor and I am a lifetime insomniac. I have a long history of depression and I’m beginning to have anxiety. I have low self esteem but I’ve been happily married for almost 35 years.\n I’ve never had counseling about any of this. Do I have too many issues to address in counseling?'</li><li>'Experiencing extreme mood swings not related to external circumstances.'</li></ul> |
| False | <ul><li>'Guide to learning a new language'</li><li>'Learning about the historical significance of the Silk Road.'</li><li>'Exploring historical landmarks in Europe'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9882 |
## 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-MH-Topic-Check")
# Run inference
preds = model("Planning a DIY home renovation project.")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 4 | 33.7092 | 111 |
| Label | Training Sample Count |
|:------|:----------------------|
| True | 138 |
| False | 58 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- 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 |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0007 | 1 | 0.2132 | - |
| 0.0354 | 50 | 0.1508 | - |
| 0.0708 | 100 | 0.0193 | - |
| 0.1062 | 150 | 0.0075 | - |
| 0.1415 | 200 | 0.0025 | - |
| 0.1769 | 250 | 0.0009 | - |
| 0.2123 | 300 | 0.0003 | - |
| 0.2477 | 350 | 0.0005 | - |
| 0.2831 | 400 | 0.0004 | - |
| 0.3185 | 450 | 0.0004 | - |
| 0.3539 | 500 | 0.0002 | - |
| 0.3892 | 550 | 0.0004 | - |
| 0.4246 | 600 | 0.0001 | - |
| 0.4600 | 650 | 0.0003 | - |
| 0.4954 | 700 | 0.0001 | - |
| 0.5308 | 750 | 0.0001 | - |
| 0.5662 | 800 | 0.0001 | - |
| 0.6016 | 850 | 0.0002 | - |
| 0.6369 | 900 | 0.0001 | - |
| 0.6723 | 950 | 0.0001 | - |
| 0.7077 | 1000 | 0.0001 | - |
| 0.7431 | 1050 | 0.0 | - |
| 0.7785 | 1100 | 0.0001 | - |
| 0.8139 | 1150 | 0.0001 | - |
| 0.8493 | 1200 | 0.0001 | - |
| 0.8846 | 1250 | 0.0001 | - |
| 0.9200 | 1300 | 0.0001 | - |
| 0.9554 | 1350 | 0.0001 | - |
| 0.9908 | 1400 | 0.0001 | - |
| **1.0** | **1413** | **-** | **0.017** |
| 1.0262 | 1450 | 0.0001 | - |
| 1.0616 | 1500 | 0.0001 | - |
| 1.0970 | 1550 | 0.0 | - |
| 1.1323 | 1600 | 0.0001 | - |
| 1.1677 | 1650 | 0.0001 | - |
| 1.2031 | 1700 | 0.0001 | - |
| 1.2385 | 1750 | 0.0 | - |
| 1.2739 | 1800 | 0.0001 | - |
| 1.3093 | 1850 | 0.0 | - |
| 1.3447 | 1900 | 0.0 | - |
| 1.3800 | 1950 | 0.0 | - |
| 1.4154 | 2000 | 0.0 | - |
| 1.4508 | 2050 | 0.0 | - |
| 1.4862 | 2100 | 0.0 | - |
| 1.5216 | 2150 | 0.0 | - |
| 1.5570 | 2200 | 0.0 | - |
| 1.5924 | 2250 | 0.0 | - |
| 1.6277 | 2300 | 0.0 | - |
| 1.6631 | 2350 | 0.0 | - |
| 1.6985 | 2400 | 0.0 | - |
| 1.7339 | 2450 | 0.0 | - |
| 1.7693 | 2500 | 0.0 | - |
| 1.8047 | 2550 | 0.0 | - |
| 1.8401 | 2600 | 0.0 | - |
| 1.8754 | 2650 | 0.0 | - |
| 1.9108 | 2700 | 0.0001 | - |
| 1.9462 | 2750 | 0.0 | - |
| 1.9816 | 2800 | 0.0 | - |
| 2.0 | 2826 | - | 0.018 |
| 2.0170 | 2850 | 0.0 | - |
| 2.0524 | 2900 | 0.0 | - |
| 2.0878 | 2950 | 0.0 | - |
| 2.1231 | 3000 | 0.0 | - |
| 2.1585 | 3050 | 0.0 | - |
| 2.1939 | 3100 | 0.0 | - |
| 2.2293 | 3150 | 0.0 | - |
| 2.2647 | 3200 | 0.0 | - |
| 2.3001 | 3250 | 0.0 | - |
| 2.3355 | 3300 | 0.0 | - |
| 2.3708 | 3350 | 0.0 | - |
| 2.4062 | 3400 | 0.0 | - |
| 2.4416 | 3450 | 0.0 | - |
| 2.4770 | 3500 | 0.0 | - |
| 2.5124 | 3550 | 0.0 | - |
| 2.5478 | 3600 | 0.0 | - |
| 2.5832 | 3650 | 0.0 | - |
| 2.6185 | 3700 | 0.0 | - |
| 2.6539 | 3750 | 0.0 | - |
| 2.6893 | 3800 | 0.0 | - |
| 2.7247 | 3850 | 0.0 | - |
| 2.7601 | 3900 | 0.0 | - |
| 2.7955 | 3950 | 0.0 | - |
| 2.8309 | 4000 | 0.0 | - |
| 2.8662 | 4050 | 0.0001 | - |
| 2.9016 | 4100 | 0.0 | - |
| 2.9370 | 4150 | 0.0 | - |
| 2.9724 | 4200 | 0.0001 | - |
| 3.0 | 4239 | - | 0.0182 |
* 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}
}
```
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