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---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
datasets:
- clareandme/multiLabelClassification
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: The AI and user talk about how sleep problems are affecting the user's daily
life. The AI suggests improvements like sticking to a regular sleep schedule,
establishing a bedtime routine, and reducing screen time before bed. The user
acknowledges the challenge of implementing these changes but is willing to give
them a try for better sleep quality.
- text: The AI inquires about the user’s overall well-being and offers to talk about
managing work and study demands. The user reveals they’re feeling swamped by job
and exam pressures but find comfort in having a well-organized schedule.
- text: The AI and user talk about a recent falling out with a close friend who has
been giving them the cold shoulder. The user feels hurt and is uncertain about
the future of their friendship.
- text: The AI and user have a conversation about ways to manage and cope with the
loss of a loved partner.
- text: The AI engages the user in a conversation about their current challenges.
The user discloses that they’re feeling stressed and anxious due to financial
instability and rising debt.
inference: false
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: clareandme/multiLabelClassification
type: clareandme/multiLabelClassification
split: test
metrics:
- type: accuracy
value: 0.2857142857142857
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [clareandme/multiLabelClassification](https://huggingface.co/datasets/clareandme/multiLabelClassification) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier 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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
- **Training Dataset:** [clareandme/multiLabelClassification](https://huggingface.co/datasets/clareandme/multiLabelClassification)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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)
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.2857 |
## 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("clareandme/setfit-ethos-multilabel-v2")
# Run inference
preds = model("The AI and user have a conversation about ways to manage and cope with the loss of a loved partner.")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 10 | 33.475 | 68 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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 |
|:-------:|:-------:|:-------------:|:---------------:|
| 0.0033 | 1 | 0.1896 | - |
| 0.1667 | 50 | 0.2437 | - |
| 0.3333 | 100 | 0.1152 | - |
| 0.5 | 150 | 0.2441 | - |
| 0.6667 | 200 | 0.0491 | - |
| 0.8333 | 250 | 0.0773 | - |
| **1.0** | **300** | **0.0994** | **0.1193** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.3.1+cu121
- Datasets: 2.21.0
- Tokenizers: 0.15.2
## 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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