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: and importance of the climate crisis requires everyone to play their part.
- text: The Group has unused tax losses carried forward of 512m, primarily UK capital
    losses, on which no deferred tax is recognised.
- text: If an acquirer of shares is not prepared to provide this declaration, the
    Board may refuse to register him as a shareholder with the right to vote.
- text: The Company will also make every effort to improve the effectiveness of its
    sustainability reporting.
- text: The Company maintains sufficient liquidity and has a variety of contingent
    liquidity resources to manage liquidity across a range of economic scenarios.
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: 0.7909319899244333
      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:** 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0.0   | <ul><li>'The concentration of our sales in our fourth fiscal quarter increases this impact as the revenue impact of most fourth fiscal quarter subscription sales will not be realized until the following fiscal year.'</li><li>'The deposit insurance fund of the FDIC insures deposit accounts in HomeTrust Bank up to 250,000 per separately insured deposit ownership right or category.'</li><li>'On 19 October 2020 the Company entered into a 25 million revolving credit facility agreement with State Street Bank International GmbH.'</li></ul>                      |
| 1.0   | <ul><li>'The IH Laboratory supports our field organization and offers our customers a wide array of sampling and analysis protocols based on Occupational Safety and Health Administration (OSHA) and National Institute for Occupational Safety and Health methodologies.'</li><li>'We partnered with the Fair Wage Network to increase our understanding of the different definitions of fair wages and to benchmark against their extensive fair and living wage database.'</li><li>'Strategy is to ensure all our employees worldwide earn at least a fair wage.'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.7909   |

## 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("mitra-mir/setfit-model-ESG-social")
# Run inference
preds = model("and importance of the climate crisis requires everyone to play their part.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 4   | 24.5578 | 112 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 147                   |
| 1.0   | 52                    |

### 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0020 | 1    | 0.4072        | -               |
| 0.1004 | 50   | 0.2038        | -               |
| 0.2008 | 100  | 0.0155        | -               |
| 0.3012 | 150  | 0.0003        | -               |
| 0.4016 | 200  | 0.0002        | -               |
| 0.5020 | 250  | 0.0002        | -               |
| 0.6024 | 300  | 0.0002        | -               |
| 0.7028 | 350  | 0.0001        | -               |
| 0.8032 | 400  | 0.0001        | -               |
| 0.9036 | 450  | 0.0001        | -               |

### Framework Versions
- Python: 3.11.6
- SetFit: 1.1.0
- Sentence Transformers: 3.2.1
- Transformers: 4.43.4
- PyTorch: 2.4.1+cu121
- Datasets: 3.0.1
- 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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