setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/paraphrase-mpnet-base-v2 base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2 model-index region:us
---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Blockbuster Cuts Online Price, Challenges Netflix (Reuters) Reuters - Video
    chain Blockbuster Inc on\Friday said it would lower the price of its online DVD
    rentals\to undercut a similar move by Netflix Inc. that sparked a stock\a sell-off
    of both companies' shares.
- text: Goss Gets Senate Panel's OK for CIA Post (AP) AP - A Senate panel on Tuesday
    approved the nomination of Rep. Porter Goss, R-Fla., to head the CIA, overcoming
    Democrats' objections that Goss was too political for the job.
- text: 'Crazy Like a Firefox Today, the Mozilla Foundation #39;s Firefox browser
    officially launched -- welcome, version 1.0. In a way, it #39;s much ado about
    nothing, seeing how it wasn #39;t that long ago that we reported on how Mozilla
    had set '
- text: North Korea eases tough stance against US in nuclear talks North Korea on
    Friday eased its tough stance against the United States, saying it is willing
    to resume stalled six-way talks on its nuclear weapons if Washington is ready
    to consider its demands.
- text: Mauresmo confident of LA victory Amelie Mauresmo insists she can win the Tour
    Championships this week and finish the year as world number one. The Frenchwoman
    could overtake Lindsay Davenport with a win in Los Angeles.
inference: true
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: 0.8726315789473684
      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 [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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **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:** 4 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2     | <ul><li>'Lockheed Martin Defends Polish Investment Lockheed Martin Corp. defended itself Friday against criticism it was moving too slowly in investing \\$6 billion in Poland - a commitment that helped win the US company an order for 48 F-16 fighter jets over its European competitors.'</li><li>'C amp;W to sell Japanese arm to Softbank Cable  amp; Wireless has agreed to sell its Japanese unit to Softbank for 72.4 million pounds. The deal, under which the Japanese Internet communications company will assume debt worth 9.5 million '</li><li>'Martha Stewart Living to replace CEO Martha Stewart Living Omnimedia is expected to name former ABC Entertainment President Susan Lyne as its new chief executive, replacing Sharon Patrick, according to a report on New York Magazines Web site on Thursday.'</li></ul>                                                                                                                               |
| 0     | <ul><li>' #39;Resolution of J amp;K issue will be biggest CBM #39; Islamabad: Emphasising the need for  quot;sincerity quot; and  quot;flexibility quot; to resolve all outstanding issues with India, including the Kashmir problem, Pakistan on Sunday said the recent meeting involving the Prime Ministers of both countries has helped in '</li><li>'Cannabis chemical pregnancy link A cannabis-like chemical may be important for normal pregnancy, researchers believe.'</li><li>'Victims buried alive in Japan quake A series of powerful earthquakes has killed at least 18 people and injured more than 800 people in northern Japan. The first quake struck on Saturday in Niigata prefecture, 200km north of Tokyo, followed by strong aftershocks.'</li></ul>                                                                                                                                                                                              |
| 3     | <ul><li>'Microsoft Gets Good Grades on SP2 Microsoft has begun sending Windows XP Service Pack 2 to home users via of its automatic update system. Despite a few flaws that already have been found in the massive patch, the update will strengthen system security for most Windows XP ...'</li><li>'Aggregator Sites:  One-Stop Shopping? When Che Carsner wanted to find discount airline tickets for his parents from the Miami area to New York, he knew where to look. The Manhattan real estate agent logged onto Kayak.com, a new online travel aggregator, and typed in some dates. Within seconds, dozens of options appeared. Among them: a \\$140 round-trip fare from Fort Lauderdale, Fla., to LaGuardia airport on US Airways, which he booked.'</li><li>'AOL to have desktop searching in new browser America Online will have a desktop search capability in a new browser the company is now beta-testing, an AOL spokeswoman said Friday.'</li></ul> |
| 1     | <ul><li>'Yankees hold off Blue Jays Derek Jeter, Hideki Matsui, and Bernie Williams each homered and the New York Yankees held on for an 8-7 victory over the Blue Jays last night in Toronto. Esteban Loaiza failed to make it out of the fifth inning in another poor start since being acquired from the Chicago White Sox in a July 31 trade for Jose Contreras.'</li><li>'Getting Greene #39;s GOAT a chore worthy of an Olympic medal The tattoo on Maurice Greene #39;s right shoulder succinctly sums up where he feels he ranks in the world of sprinting.  quot;Greatest of all time, quot; he will shout after a victory while thumping it.'</li><li>'Klitschko retains title Vitali Klitschko has knocked out Danny Williams in the eighth round to retain his WBC heavyweight crown and become the premier champion in the division.'</li></ul>                                                                                                             |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8726   |

## 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("vidhi0206/setfit-paraphrase-mpnet-ag_news_v2")
# Run inference
preds = model("Mauresmo confident of LA victory Amelie Mauresmo insists she can win the Tour Championships this week and finish the year as world number one. The Frenchwoman could overtake Lindsay Davenport with a win in Los Angeles.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 15  | 38.1953 | 73  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 64                    |
| 1     | 64                    |
| 2     | 64                    |
| 3     | 64                    |

### Training Hyperparameters
- batch_size: (8, 8)
- 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    | 0.3712        | -               |
| 0.0391 | 50   | 0.2353        | -               |
| 0.0781 | 100  | 0.1091        | -               |
| 0.1172 | 150  | 0.0898        | -               |
| 0.1562 | 200  | 0.0054        | -               |
| 0.1953 | 250  | 0.0103        | -               |
| 0.2344 | 300  | 0.0051        | -               |
| 0.2734 | 350  | 0.0081        | -               |
| 0.3125 | 400  | 0.0007        | -               |
| 0.3516 | 450  | 0.0003        | -               |
| 0.3906 | 500  | 0.0003        | -               |
| 0.4297 | 550  | 0.0005        | -               |
| 0.4688 | 600  | 0.0003        | -               |
| 0.5078 | 650  | 0.0001        | -               |
| 0.5469 | 700  | 0.0002        | -               |
| 0.5859 | 750  | 0.0001        | -               |
| 0.625  | 800  | 0.0001        | -               |
| 0.6641 | 850  | 0.0001        | -               |
| 0.7031 | 900  | 0.0001        | -               |
| 0.7422 | 950  | 0.0001        | -               |
| 0.7812 | 1000 | 0.0002        | -               |
| 0.8203 | 1050 | 0.0002        | -               |
| 0.8594 | 1100 | 0.0001        | -               |
| 0.8984 | 1150 | 0.0002        | -               |
| 0.9375 | 1200 | 0.0001        | -               |
| 0.9766 | 1250 | 0.0001        | -               |

### Framework Versions
- Python: 3.8.10
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.37.2
- PyTorch: 2.2.0+cu121
- Datasets: 2.17.0
- Tokenizers: 0.15.1

## 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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