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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Now that the baffling, elongated, hyperreal coronation has occurred—no, not
that one—and Liz Truss has become Prime Minister, a degree of intervention and
action on energy bills has emerged, ahead of the looming socioeconomic catastrophe
facing the country this winter.
- text: But it needs to go much further.
- text: What could possibly go wrong?
- text: If you are White you might feel bad about hurting others or you might feel
afraid to lose this privilege….Overcoming White privilege is a job that must start
with the White community….
- text: '[JF: Obviously, immigration wasn’t stopped: the current population of the
United States is 329.5 million—it passed 300 million in 2006.'
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.7736625514403292
name: Accuracy
---
# SetFit
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A LinearSVC 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:** [Unknown](https://huggingface.co/unknown) -->
- **Classification head:** a LinearSVC instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **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)
### Model Labels
| Label | Examples |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| SUBJ | <ul><li>'Now suppose that under stress of abnormal public revenue the structure of government is somewhat rationalized and that by such means as economy and efficiency the cost of government by measure is much reduced.'</li><li>'Modern Russia is a propaganda state, but not in the same way as the Soviet Union.'</li><li>'The spender of public money will never want followers.'</li></ul> |
| OBJ | <ul><li>'But a top buying agent tells me that access to 13 can be gained if you know the right people.'</li><li>'“Normally, the majority opinion would speak for itself.” The decision is “really about policy—our state has values of inclusion and diversity.” The ruling is based “on policy, which is the definition of judicial activism.'</li><li>'asked American Federation of Teachers President Randi Weingarten.'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.7737 |
## 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("SOUMYADEEPSAR/SetFit_SubjectivityDetection")
# Run inference
preds = model("What could possibly go wrong?")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 22.085 | 77 |
| Label | Training Sample Count |
|:------|:----------------------|
| OBJ | 100 |
| SUBJ | 100 |
### Training Hyperparameters
- batch_size: (32, 32)
- 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: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0016 | 1 | 0.2686 | - |
| 0.0791 | 50 | 0.2494 | - |
| 0.1582 | 100 | 0.2639 | - |
| 0.2373 | 150 | 0.2258 | - |
| 0.3165 | 200 | 0.0176 | - |
| 0.3956 | 250 | 0.0027 | - |
| 0.4747 | 300 | 0.0017 | - |
| 0.5538 | 350 | 0.0013 | - |
| 0.6329 | 400 | 0.0016 | - |
| 0.7120 | 450 | 0.001 | - |
| 0.7911 | 500 | 0.0009 | - |
| 0.8703 | 550 | 0.001 | - |
| 0.9494 | 600 | 0.001 | - |
| 1.0285 | 650 | 0.0009 | - |
| 1.1076 | 700 | 0.0008 | - |
| 1.1867 | 750 | 0.0008 | - |
| 1.2658 | 800 | 0.0006 | - |
| 1.3449 | 850 | 0.0007 | - |
| 1.4241 | 900 | 0.0006 | - |
| 1.5032 | 950 | 0.0007 | - |
| 1.5823 | 1000 | 0.0006 | - |
| 1.6614 | 1050 | 0.0005 | - |
| 1.7405 | 1100 | 0.0006 | - |
| 1.8196 | 1150 | 0.0007 | - |
| 1.8987 | 1200 | 0.0005 | - |
| 1.9778 | 1250 | 0.0006 | - |
| 2.0570 | 1300 | 0.0005 | - |
| 2.1361 | 1350 | 0.0005 | - |
| 2.2152 | 1400 | 0.0004 | - |
| 2.2943 | 1450 | 0.0005 | - |
| 2.3734 | 1500 | 0.0004 | - |
| 2.4525 | 1550 | 0.0004 | - |
| 2.5316 | 1600 | 0.0004 | - |
| 2.6108 | 1650 | 0.0004 | - |
| 2.6899 | 1700 | 0.0005 | - |
| 2.7690 | 1750 | 0.0005 | - |
| 2.8481 | 1800 | 0.0004 | - |
| 2.9272 | 1850 | 0.0005 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.4.0
- Transformers: 4.37.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.1
- 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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