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setfit-messages-label-v2

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
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setfit-messages-label-v2 / README.md
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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
datasets:
- konsman/setfit-messages-updated-influence-level
metrics:
- accuracy
widget:
- text: The influence level of Staying hydrated is especially important for older
adults to prevent dehydration.
- text: The influence level of Regularly updating emergency contact information is
important for the elderly.
- text: 'The influence level of Early detection saves lives. Support breast cancer
awareness month. Wear pink, spread awareness. Stand with us this breast cancer
awareness month. '
- text: 'The influence level of Mental Health Day is approaching. Join our online
discussion on well-being. Prioritize mental health. Participate in our online
discussion this Mental Health Day. '
- text: The influence level of Regular kidney function tests are important for those
with high blood pressure.
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/all-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/all-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: konsman/setfit-messages-updated-influence-level
type: konsman/setfit-messages-updated-influence-level
split: test
metrics:
- type: accuracy
value: 0.47368421052631576
name: Accuracy
---
# SetFit with sentence-transformers/all-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [konsman/setfit-messages-updated-influence-level](https://huggingface.co/datasets/konsman/setfit-messages-updated-influence-level) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 4 classes
- **Training Dataset:** [konsman/setfit-messages-updated-influence-level](https://huggingface.co/datasets/konsman/setfit-messages-updated-influence-level)
<!-- - **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 |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | <ul><li>'The influence level of Understanding the effects of aging on the body is key for caregivers.'</li><li>'The influence level of Regular check-ups are key to maintaining good health.'</li><li>'The influence level of Balanced nutrition is key for maintaining health in old age.'</li></ul> |
| 1 | <ul><li>"The influence level of Time for your 3pm medication! Please take as directed. Friendly reminder: It's time for your 3pm medication. Ensure to take it as prescribed."</li><li>'The influence level of Regular bladder function tests are important for elderly individuals.'</li><li>'The influence level of How was your telehealth session? Share your feedback. Help us improve. Provide feedback on your recent telehealth appointment. '</li></ul> |
| 2 | <ul><li>"The influence level of A support group meeting is scheduled for tomorrow at 5pm. It's a great opportunity to share and learn. Connect with others in our support group meeting tomorrow. See you at 5pm!"</li><li>'The influence level of Safety first! Please update your emergency contact details in our system. Ensure swift help when needed. Update your emergency contacts in our app. '</li><li>'The influence level of Regularly discussing health concerns with doctors is important for the elderly.'</li></ul> |
| 3 | <ul><li>'The influence level of Understanding the role of dietary supplements in elderly health is important.'</li><li>'The influence level of Proper medication management is essential for effective treatment.'</li><li>"The influence level of Your child's health is paramount. Reminder for the pediatrician appointment tomorrow. Ensure the best for your child. Don't miss the pediatrician appointment set for tomorrow. "</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.4737 |
## 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("konsman/setfit-messages-label-v2")
# Run inference
preds = model("The influence level of Regularly updating emergency contact information is important for the elderly.")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 12 | 20.8438 | 36 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 8 |
| 1 | 8 |
| 2 | 8 |
| 3 | 8 |
### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (4, 4)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 40
- body_learning_rate: (2.2041595048800003e-05, 2.2041595048800003e-05)
- head_learning_rate: 2.2041595048800003e-05
- 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: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0031 | 1 | 0.1587 | - |
| 0.1562 | 50 | 0.116 | - |
| 0.3125 | 100 | 0.0918 | - |
| 0.4688 | 150 | 0.0042 | - |
| 0.625 | 200 | 0.0005 | - |
| 0.7812 | 250 | 0.0012 | - |
| 0.9375 | 300 | 0.0005 | - |
| 1.0938 | 350 | 0.0005 | - |
| 1.25 | 400 | 0.0003 | - |
| 1.4062 | 450 | 0.0002 | - |
| 1.5625 | 500 | 0.0002 | - |
| 1.7188 | 550 | 0.0001 | - |
| 1.875 | 600 | 0.0001 | - |
| 2.0312 | 650 | 0.0002 | - |
| 2.1875 | 700 | 0.0001 | - |
| 2.3438 | 750 | 0.0001 | - |
| 2.5 | 800 | 0.0001 | - |
| 2.6562 | 850 | 0.0001 | - |
| 2.8125 | 900 | 0.0001 | - |
| 2.9688 | 950 | 0.0001 | - |
| 3.125 | 1000 | 0.0002 | - |
| 3.2812 | 1050 | 0.0001 | - |
| 3.4375 | 1100 | 0.0001 | - |
| 3.5938 | 1150 | 0.0001 | - |
| 3.75 | 1200 | 0.0001 | - |
| 3.9062 | 1250 | 0.0001 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.2
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.0
## 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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