---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/all-mpnet-base-v2
metrics:
- accuracy
widget:
- text: How doCompaniesbalanceIndividualCreativitywithTeamCollaboration to driveInnovationinthe
    WORKPlace?
- text: How do the values of a learning organization impact its ability to innovate
    and respond to constant change?
- text: What is the primary function of the Domain Name System (DNS) layer in the
    Internet Protocol Stack, as defined by ICANN?
- text: What distinguishes a transforming industry from one that merely innovates
    to existing practices?
- text: How can artificial intelligence systems balance individual autonomy with collective
    responsibility in decision-making processes?
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with sentence-transformers/all-mpnet-base-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 1.0
      name: Accuracy
---

# SetFit with sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 384 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                                                                                                                                                                                                                                                                                                                                                                                                                                             |
|:---------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| lexical  | <ul><li>'What is the primary function of the Apache Kafka distributed streaming platform in Big Data processing?'</li><li>"What is the primary difference between Hadoop's FileSystem-based architecture and Apache Cassandra's distributed, masterlessArchitecture in scale-out design?"</li><li>'What is the main difference between optimistic concurrency control and pessimistic concurrency control in database management systems?'</li></ul> |
| semantic | <ul><li>"How does organizational morale impact the competitiveness of a company in today's fast-paced market?"</li><li>'How do organizations balance individual creativity with collective goal achievement in a dynamic environment?'</li><li>'What is a key challenge faced by managers in sustaining a work culture that encourages creativity, innovation, and critical thinking within the technological industry globally?'</li></ul>          |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## 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("yaniseuranova/setfit-paraphrase-mpnet-base-v2-sst2")
# Run inference
preds = model("What distinguishes a transforming industry from one that merely innovates to existing practices?")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 4   | 19.1839 | 42  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| lexical  | 43                    |
| semantic | 44                    |

### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (3, 3)
- 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: True

### Training Results
| Epoch   | Step     | Training Loss | Validation Loss |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0021  | 1        | 0.301         | -               |
| 0.1033  | 50       | 0.1244        | -               |
| 0.2066  | 100      | 0.0021        | -               |
| 0.3099  | 150      | 0.0006        | -               |
| 0.4132  | 200      | 0.0002        | -               |
| 0.5165  | 250      | 0.0002        | -               |
| 0.6198  | 300      | 0.0001        | -               |
| 0.7231  | 350      | 0.0001        | -               |
| 0.8264  | 400      | 0.0001        | -               |
| 0.9298  | 450      | 0.0001        | -               |
| 1.0     | 484      | -             | 0.0001          |
| 1.0331  | 500      | 0.0001        | -               |
| 1.1364  | 550      | 0.0001        | -               |
| 1.2397  | 600      | 0.0001        | -               |
| 1.3430  | 650      | 0.0           | -               |
| 1.4463  | 700      | 0.0001        | -               |
| 1.5496  | 750      | 0.0001        | -               |
| 1.6529  | 800      | 0.0001        | -               |
| 1.7562  | 850      | 0.0001        | -               |
| 1.8595  | 900      | 0.0           | -               |
| 1.9628  | 950      | 0.0           | -               |
| 2.0     | 968      | -             | 0.0001          |
| 2.0661  | 1000     | 0.0001        | -               |
| 2.1694  | 1050     | 0.0001        | -               |
| 2.2727  | 1100     | 0.0           | -               |
| 2.3760  | 1150     | 0.0           | -               |
| 2.4793  | 1200     | 0.0           | -               |
| 2.5826  | 1250     | 0.0           | -               |
| 2.6860  | 1300     | 0.0001        | -               |
| 2.7893  | 1350     | 0.0           | -               |
| 2.8926  | 1400     | 0.0001        | -               |
| 2.9959  | 1450     | 0.0           | -               |
| **3.0** | **1452** | **-**         | **0.0001**      |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.6.1
- Transformers: 4.39.0
- PyTorch: 2.3.1+cu121
- Datasets: 2.18.0
- Tokenizers: 0.15.2

## 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->