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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: avsolatorio/GIST-small-Embedding-v0
metrics:
- accuracy
widget:
- text: News footage from that day shows groups of young men marching through the
capital, chanting “kill, kill, kill a poof”.
- text: They are California, Florida, Illinois, Nebraska, New York, and Wyoming.
- text: Or, are they actively trying to make sure they have a scapegoat for a drug-resistant
form of the monkeypox?
- text: Either way, she said, a public gathering of some kind would go ahead on Saturday.
- text: White House officials have touted their efforts to cut down on the paperwork
in order to get the drug through this so-called “compassionate use” channel.
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with avsolatorio/GIST-small-Embedding-v0
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.9265060240963855
name: Accuracy
---
# SetFit with avsolatorio/GIST-small-Embedding-v0
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [avsolatorio/GIST-small-Embedding-v0](https://huggingface.co/avsolatorio/GIST-small-Embedding-v0) 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:** [avsolatorio/GIST-small-Embedding-v0](https://huggingface.co/avsolatorio/GIST-small-Embedding-v0)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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 |
|:-----------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| objective | <ul><li>'"I have never seen it this bad," said Dan Domenech, executive director of the School Superintendents Association.'</li><li>'There will be an enormous increase of public revenue, as there was after the war from the carry-over of the wartime taxes.'</li><li>'No cases have been spotted so far of a strain that can evade tecovirimat, though the ruling class is warning of a “low barrier to resistance” which poses a risk that a resistant variant could emerge and spread.'</li></ul> |
| subjective | <ul><li>'But what of American individualism?'</li><li>'It’s a kind of brainwashing.'</li><li>'In theory, the problematic behavior parts of the New Mexico ruling could still prevent an illegal alien from being given authorization to practice law, but don’t count on it.'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9265 |
## 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("setfit_model_id")
# Run inference
preds = model("They are California, Florida, Illinois, Nebraska, New York, and Wyoming.")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 22.7637 | 97 |
| Label | Training Sample Count |
|:-----------|:----------------------|
| objective | 256 |
| subjective | 256 |
### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0002 | 1 | 0.2779 | - |
| 0.0122 | 50 | 0.2605 | - |
| 0.0243 | 100 | 0.2721 | - |
| 0.0365 | 150 | 0.2404 | - |
| 0.0486 | 200 | 0.2468 | - |
| 0.0608 | 250 | 0.1941 | - |
| 0.0730 | 300 | 0.0574 | - |
| 0.0851 | 350 | 0.0124 | - |
| 0.0973 | 400 | 0.0019 | - |
| 0.1094 | 450 | 0.0017 | - |
| 0.1216 | 500 | 0.0028 | - |
| 0.1338 | 550 | 0.0011 | - |
| 0.1459 | 600 | 0.0011 | - |
| 0.1581 | 650 | 0.0011 | - |
| 0.1702 | 700 | 0.0316 | - |
| 0.1824 | 750 | 0.0007 | - |
| 0.1946 | 800 | 0.001 | - |
| 0.2067 | 850 | 0.0009 | - |
| 0.2189 | 900 | 0.0008 | - |
| 0.2310 | 950 | 0.0007 | - |
| 0.2432 | 1000 | 0.0006 | - |
| 0.2554 | 1050 | 0.0006 | - |
| 0.2675 | 1100 | 0.0005 | - |
| 0.2797 | 1150 | 0.0005 | - |
| 0.2918 | 1200 | 0.0006 | - |
| 0.3040 | 1250 | 0.0006 | - |
| 0.3161 | 1300 | 0.0005 | - |
| 0.3283 | 1350 | 0.0005 | - |
| 0.3405 | 1400 | 0.001 | - |
| 0.3526 | 1450 | 0.0004 | - |
| 0.3648 | 1500 | 0.0005 | - |
| 0.3769 | 1550 | 0.0005 | - |
| 0.3891 | 1600 | 0.0004 | - |
| 0.4013 | 1650 | 0.0005 | - |
| 0.4134 | 1700 | 0.0004 | - |
| 0.4256 | 1750 | 0.0004 | - |
| 0.4377 | 1800 | 0.0004 | - |
| 0.4499 | 1850 | 0.0004 | - |
| 0.4621 | 1900 | 0.0003 | - |
| 0.4742 | 1950 | 0.0004 | - |
| 0.4864 | 2000 | 0.0004 | - |
| 0.4985 | 2050 | 0.0003 | - |
| 0.5107 | 2100 | 0.0003 | - |
| 0.5229 | 2150 | 0.0004 | - |
| 0.5350 | 2200 | 0.0004 | - |
| 0.5472 | 2250 | 0.0003 | - |
| 0.5593 | 2300 | 0.0003 | - |
| 0.5715 | 2350 | 0.0004 | - |
| 0.5837 | 2400 | 0.0004 | - |
| 0.5958 | 2450 | 0.0004 | - |
| 0.6080 | 2500 | 0.0003 | - |
| 0.6201 | 2550 | 0.0003 | - |
| 0.6323 | 2600 | 0.0003 | - |
| 0.6445 | 2650 | 0.0003 | - |
| 0.6566 | 2700 | 0.0003 | - |
| 0.6688 | 2750 | 0.0003 | - |
| 0.6809 | 2800 | 0.0003 | - |
| 0.6931 | 2850 | 0.0002 | - |
| 0.7053 | 2900 | 0.0003 | - |
| 0.7174 | 2950 | 0.0003 | - |
| 0.7296 | 3000 | 0.0003 | - |
| 0.7417 | 3050 | 0.0002 | - |
| 0.7539 | 3100 | 0.0003 | - |
| 0.7661 | 3150 | 0.0003 | - |
| 0.7782 | 3200 | 0.0003 | - |
| 0.7904 | 3250 | 0.0003 | - |
| 0.8025 | 3300 | 0.0003 | - |
| 0.8147 | 3350 | 0.0003 | - |
| 0.8268 | 3400 | 0.0003 | - |
| 0.8390 | 3450 | 0.0003 | - |
| 0.8512 | 3500 | 0.0003 | - |
| 0.8633 | 3550 | 0.0003 | - |
| 0.8755 | 3600 | 0.0003 | - |
| 0.8876 | 3650 | 0.0002 | - |
| 0.8998 | 3700 | 0.0003 | - |
| 0.9120 | 3750 | 0.0003 | - |
| 0.9241 | 3800 | 0.0002 | - |
| 0.9363 | 3850 | 0.0003 | - |
| 0.9484 | 3900 | 0.0003 | - |
| 0.9606 | 3950 | 0.0003 | - |
| 0.9728 | 4000 | 0.0003 | - |
| 0.9849 | 4050 | 0.0002 | - |
| 0.9971 | 4100 | 0.0003 | - |
### Framework Versions
- Python: 3.11.9
- SetFit: 1.0.3
- Sentence Transformers: 3.0.0
- Transformers: 4.40.2
- PyTorch: 2.1.2
- Datasets: 2.19.1
- Tokenizers: 0.19.1
## 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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