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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Someone comes out of the shack and shoves one of the kids to the ground. He
- text: He approaches the object and reads a plaque on its side. Someone
- text: Later at someone's family farm, someone sees the lights on in the hangar.
Someone
- text: Someone stands looking over some of the old photographs as someone goes through
the mess on the desk. Someone
- text: Snow blows around a city of towering crystalline structures. A warrior
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: 0.11475409836065574
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 |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 6 | <ul><li>'The girl does 2 perfect flips. The girls'</li><li>'Emerging in open water, he does a breaststroke toward the murky. He'</li><li>'A young child is moving back and fourth on a swing while laughing and smiling to the camera. The child'</li></ul> |
| 4 | <ul><li>'He turns away and covers his face with one hand. Someone'</li><li>'With a nod, the man hands it over to the defeated boy. Someone'</li><li>"On the shop floor, his little helper helps himself to an expensive handbag from a display cabinet, then some women's designer shoes, all of which are detailed on a list. He"</li></ul> |
| 1 | <ul><li>'A lot of people are sitting on terraces in a big field and people is walking in the entrance of a big stadium. men'</li><li>'The water gets rough as the past through some rocks. Several people'</li><li>'We see a man dunk the ball twice. We'</li></ul> |
| 5 | <ul><li>'A man wearing a safari hat leads a group of camels with riders in a single file. The camera'</li><li>'Someone stirs the cookie dough in a bowl. The dough'</li><li>'A logo for a sports even is shown. There'</li></ul> |
| 8 | <ul><li>'A girl is shown several times running on a track. She'</li><li>'Someone peers out from the cabin. As she emerges, someone'</li><li>'Someone and someone swap looks. She'</li></ul> |
| 2 | <ul><li>'In slow motion, both the Russians and Americans celebrate. Someone'</li><li>'Through a window, we watch someone raise his teacup to his companions. At home, someone'</li><li>'People stand by the wall, laughing. He'</li></ul> |
| 7 | <ul><li>'Someone turns at the sound of the distant horns. 6000 horsemen, lead by people,'</li><li>"The man plays the video in reverse to look as if he's putting shaving cream on with the razor. The men then"</li><li>'He eyes someone with a furrowed brow, then springs up and hurries after her. Someone and someone'</li></ul> |
| 0 | <ul><li>'She opens a small metal box on a desk and pushes a button inside. Someone'</li><li>'Someone starts to say something then thinks better of it, and remains silent. Someone'</li><li>"Someone changes into a Spanish policeman's outfit and heads down an outside staircase with the packed up rifle. As someone leaves, someone"</li></ul> |
| 3 | <ul><li>'He is shown playing a game with a virtual sumo wrestler. The shorter man'</li><li>'The girls flips, then runs, flips and dismounts. The cloud'</li><li>'The official extends a red flag. As Master someone'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.1148 |
## 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-4")
# Run inference
preds = model("He approaches the object and reads a plaque on its side. Someone")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 6 | 13.25 | 31 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 4 |
| 1 | 4 |
| 2 | 4 |
| 3 | 4 |
| 4 | 4 |
| 5 | 4 |
| 6 | 4 |
| 7 | 4 |
| 8 | 4 |
### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0111 | 1 | 0.2285 | - |
| 0.5556 | 50 | 0.0567 | - |
| 1.1111 | 100 | 0.0083 | - |
| 1.6667 | 150 | 0.0084 | - |
### 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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