---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-MiniLM-L3-v2
datasets:
- ag_news
metrics:
- accuracy
widget:
- text: Pakistani, US national arrested in New York bomb plot (AFP) AFP - A Pakistani
    national and a US citizen were arrested over an alleged plot to blow up a subway
    station in New York, city police commissioner Raymond Kelly said.
- text: 'Aon  #39;comfortable #39; with past behaviour Aon, the world #39;s second
    largest insurance broker, yesterday denied its brokers had ever steered business
    to favoured insurance companies as a way of generating bigger commissions.'
- text: President Blasts Firing Notre Dame's outgoing president criticized the decision
    to fire Tyrone Willingham after just three seasons, saying he was surprised the
    coach was not given more time to try to succeed.
- text: 'Gold Fields investors snub bid Harmony #39;s bid to create the world #39;s
    biggest gold miner suffered a blow yesterday when the first part of its offer
    for South African rival Gold Fields received a lukewarm reception from shareholders.'
- text: Blood, knives, cage hint at atrocities (Chicago Tribune) Chicago Tribune -
    Acting on information from a man who claimed to have escaped from militant Abu
    Musab al-Zarqawi's network, the U.S. military over the weekend inspected a house
    where intelligence officers believe hostages were detained, tortured and possibly
    killed.
pipeline_tag: text-classification
inference: true
---

# SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2

This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [ag_news](https://huggingface.co/datasets/ag_news) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2) 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:** [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 tokens
- **Number of Classes:** 4 classes
- **Training Dataset:** [ag_news](https://huggingface.co/datasets/ag_news)
<!-- - **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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>'Bangladesh paralysed by strikes Opposition activists have brought many towns and cities in Bangladesh to a halt, the day after 18 people died in explosions at a political rally.'</li><li>'Will Putin #39;s Power Play Make Russia Safer? Outwardly, Russia has not changed since the barrage of terrorist attacks that culminated in the school massacre in Beslan on Sept.'</li><li>'S African TV in beheading blunder Public broadcaster SABC apologises after news bulletin shows footage of American beheaded in Iraq.'</li></ul>                                                                                                                                                                                                                               |
| 1     | <ul><li>'Desiring Stability Redskins coach Joe Gibbs expects few major personnel changes in the offseason and wants to instill a culture of stability in Washington.'</li><li>'Mutombo says he #39;s being traded to Rockets; will back up, mentor &lt;b&gt;...&lt;/b&gt; Dikembe Mutombo, 38, has agreed to a sign-and-trade deal that will send him from the Chicago Bulls to Houston in exchange for Eric Piatkowski, Adrian Griffin and Mike Wilks, the Houston Chronicle reports.'</li><li>'They #146;re in the wrong ATHENS -- Matt Emmons was focusing on staying calm. He should have been focusing on the right target.'</li></ul>                                                                                                                                    |
| 3     | <ul><li>'U2 pitches for Apple New iTunes ads airing during baseball games Tuesday will feature the advertising-shy Irish rockers.'</li><li>'A Cosmic Storm: When Galaxy Clusters Collide Astronomers have found what they are calling the perfect cosmic storm, a galaxy cluster pile-up so powerful its energy output is second only to the Big Bang.'</li><li>'Computer Assoc. Cuts 800 Jobs Worldwide (AP) AP - Computer Associates International Inc. announced a restructuring plan Wednesday that would reduce its work force by 800 people worldwide, saving the business software maker  #36;70 million annually once the plan is fully implemented.'</li></ul>                                                                                                        |
| 2     | <ul><li>'Economy builds steam in KC Fed district The economy continued to strengthen in September and early October in the Great Plains and Rocky Mountain regions covered by the Tenth Federal Reserve District, the Federal Reserve Bank of Kansas City said Wednesday.'</li><li>'RBC Centura CEO steps down RALEIGH, NC - The head of RBC Centura Bank has stepped down, and his successor will run the bank out of Raleigh rather than Rocky Mount, where the bank is based.'</li><li>'Oracle acquisition of PeopleSoft leads flurry of deals NEW YORK (CBS.MW) -- US stocks closed higher Monday, with the Dow Jones Industrial Average ending at its best level in more than nine months amid better-than-expected economic data and merger-related optimism.'</li></ul> |

## 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("ashry/decimal-setfit-minilm-distilled")
# Run inference
preds = model("President Blasts Firing Notre Dame's outgoing president criticized the decision to fire Tyrone Willingham after just three seasons, saying he was surprised the coach was not given more time to try to succeed.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 14  | 38.204 | 143 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 244                   |
| 1     | 243                   |
| 2     | 242                   |
| 3     | 271                   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0008 | 1    | 0.9192        | -               |
| 0.04   | 50   | 0.6426        | -               |
| 0.08   | 100  | 0.0159        | -               |
| 0.12   | 150  | 0.0024        | -               |
| 0.16   | 200  | 0.0013        | -               |
| 0.2    | 250  | 0.0011        | -               |
| 0.24   | 300  | 0.0009        | -               |
| 0.28   | 350  | 0.0006        | -               |
| 0.32   | 400  | 0.0005        | -               |
| 0.36   | 450  | 0.0005        | -               |
| 0.4    | 500  | 0.0003        | -               |
| 0.44   | 550  | 0.0003        | -               |
| 0.48   | 600  | 0.0003        | -               |
| 0.52   | 650  | 0.0004        | -               |
| 0.56   | 700  | 0.0002        | -               |
| 0.6    | 750  | 0.0002        | -               |
| 0.64   | 800  | 0.0002        | -               |
| 0.68   | 850  | 0.0002        | -               |
| 0.72   | 900  | 0.0002        | -               |
| 0.76   | 950  | 0.0002        | -               |
| 0.8    | 1000 | 0.0002        | -               |
| 0.84   | 1050 | 0.0002        | -               |
| 0.88   | 1100 | 0.0001        | -               |
| 0.92   | 1150 | 0.0002        | -               |
| 0.96   | 1200 | 0.0002        | -               |
| 1.0    | 1250 | 0.0002        | -               |

### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.2
- PyTorch: 2.3.0+cu121
- Datasets: 2.18.0
- 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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