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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: She picks up a wine glass and takes a drink. She
- text: Someone smiles as she looks out her window. Their car
- text: Someone turns and her jaw drops at the site of the other woman. Moving in
slow motion, someone
- text: He sneers and winds up with his fist. Someone
- text: He smooths it back with his hand. Finally, appearing confident and relaxed
and with the old familiar glint in his eyes, someone
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.16538461538461538
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model 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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 9 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 |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 8 | <ul><li>'Later she meets someone at the bar. He'</li><li>'He heads to them and sits. The bus'</li><li>'Someone leaps to his feet and punches the agent in the face. Seemingly unaffected, the agent'</li></ul> |
| 2 | <ul><li>'A man sits behind a desk. Two people'</li><li>'A man is seen standing at the bottom of a hole while a man records him. Two men'</li><li>'Someone questions his female colleague who shrugs. Through a window, we'</li></ul> |
| 0 | <ul><li>'A woman bends down and puts something on a scale. She then'</li><li>'He pulls down the blind. He'</li><li>'Someone flings his hands forward. The someone fires, but the water'</li></ul> |
| 6 | <ul><li>'People are sitting down on chairs. They'</li><li>'They look up at stained glass skylights. The Americans'</li><li>'The lady and the man dance around each other in a circle. The people'</li></ul> |
| 1 | <ul><li>'An older gentleman kisses her. As he leads her off, someone'</li><li>'The first girl comes back and does it effortlessly as the second girl still struggles. For the last round, the girl'</li><li>'As she leaves, the bartender smiles. Now the blonde'</li></ul> |
| 3 | <ul><li>'Someone lowers his demoralized gaze. Someone'</li><li>'Someone goes into his bedroom. Someone'</li><li>'As someone leaves, someone spots him on the monitor. Someone'</li></ul> |
| 7 | <ul><li>'Four inches of Plexiglas separate the two and they talk on monitored phones. Someone'</li><li>'The American and Russian commanders each watch them returning. As someone'</li><li>'A group of walkers walk along the sidewalk near the lake. A man'</li></ul> |
| 4 | <ul><li>'The secretary flexes the foot of her crossed - leg as she eyes someone. The woman'</li><li>'A man in a white striped shirt is smiling. A woman'</li><li>'He grabs her hair and pulls her head back. She'</li></ul> |
| 5 | <ul><li>'He heads out of the plaza. Someone'</li><li>"As he starts back, he sees someone's scared look just before he slams the door shut. Someone"</li><li>'He nods at her beaming. Someone'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.1654 |
## 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("HelgeKn/Swag-multi-class-20")
# Run inference
preds = model("He sneers and winds up with his fist. Someone")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 5 | 12.1056 | 33 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 20 |
| 1 | 20 |
| 2 | 20 |
| 3 | 20 |
| 4 | 20 |
| 5 | 20 |
| 6 | 20 |
| 7 | 20 |
| 8 | 20 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0022 | 1 | 0.3747 | - |
| 0.1111 | 50 | 0.2052 | - |
| 0.2222 | 100 | 0.1878 | - |
| 0.3333 | 150 | 0.1126 | - |
| 0.4444 | 200 | 0.1862 | - |
| 0.5556 | 250 | 0.1385 | - |
| 0.6667 | 300 | 0.0154 | - |
| 0.7778 | 350 | 0.0735 | - |
| 0.8889 | 400 | 0.0313 | - |
| 1.0 | 450 | 0.0189 | - |
| 1.1111 | 500 | 0.0138 | - |
| 1.2222 | 550 | 0.0046 | - |
| 1.3333 | 600 | 0.0043 | - |
| 1.4444 | 650 | 0.0021 | - |
| 1.5556 | 700 | 0.0033 | - |
| 1.6667 | 750 | 0.001 | - |
| 1.7778 | 800 | 0.0026 | - |
| 1.8889 | 850 | 0.0022 | - |
| 2.0 | 900 | 0.0014 | - |
### Framework Versions
- Python: 3.9.13
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.36.0
- PyTorch: 2.1.1+cpu
- Datasets: 2.15.0
- 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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