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: 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.
- text: If we are unable to continue as a going concern, we may have to liquidate
    our assets and may receive less than the value at which those assets are carried
    on our audited financial statements, and it is likely that investors will lose
    all or a part of their investment.
- text: In addition, factors such as failing to meet the expectations of or provide
    quality medical care for our participants, adverse cyber or data security events,
    adverse publicity or litigation involving or surrounding us, one of our centers
    or our management, such as news articles and market rumors with respect to audits,
    litigation and other processes described in these risk factors, have diminished
    and may in the future diminish our reputation or that of our management and have
    harmed and may in the future harm our brand, making it substantially more difficult
    for us to attract new participants.
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: 0.8261964735516373
      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>'Our goal is for every employee to feel a strong sense of belonging and psychological safety.'</li><li>'Revenue from products labeled andor marketed to promote health and nutrition attributes is approximately 10.3 billion.'</li><li>'Assess climate change scenarios of key material risks.'</li></ul>                                                                                                                                                                                                                                                                                                                   |
| 1.0   | <ul><li>'We assessed accounting estimates for bias and evaluated whether the circumstances producing the bias, if any, represent a risk of material misstatement due to fraud.'</li><li>'Board to review and make recommendations to shareholders concerning the composition of the Board of Directors, with particular focus on issues of independence.'</li><li>'Our group audit mainly focused on significant group entities in terms of size and financial interest or where significant risks or complex activities were present, leading to full scope audits performed for including 2 nonconsolidated components.'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8262   |

## 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-governance")
# Run inference
preds = model("The Company will also make every effort to improve the effectiveness of its sustainability reporting.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 24.51  | 74  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 146                   |
| 1.0   | 54                    |

### 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 |
|:-----:|:----:|:-------------:|:---------------:|
| 0.002 | 1    | 0.479         | -               |
| 0.1   | 50   | 0.2577        | -               |
| 0.2   | 100  | 0.0589        | -               |
| 0.3   | 150  | 0.0008        | -               |
| 0.4   | 200  | 0.0004        | -               |
| 0.5   | 250  | 0.0003        | -               |
| 0.6   | 300  | 0.0002        | -               |
| 0.7   | 350  | 0.0002        | -               |
| 0.8   | 400  | 0.0002        | -               |
| 0.9   | 450  | 0.0002        | -               |
| 1.0   | 500  | 0.0002        | -               |

### 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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