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---
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: What is the primary difference between homomorphic encryption and multi-party
computation in the context of secure multi-party computation protocols?
- text: How do organizations balance the need for innovation with the potential risks
and unintended consequences of emerging technologies?
- text: How doCompaniesbalanceIndividualCreativitywithTeamCollaboration to driveInnovationinthe
WORKPlace?
- text: How do companies balance the need for innovation with the risk of disrupting
their existing business models?
- text: What is the primary application of Natural Language Processing (NLP) in Google's
BERT language model, and how does it utilize masked language modeling to improve
contextual understanding?
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) -->
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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 |
|:---------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| semantic | <ul><li>'How do artificial intelligence systems navigate the trade-off between simplicity and accuracy when modeling complex real-world phenomena?'</li><li>'How do complex systems, consisting of many interconnected components, give rise to emergent properties that cannot be predicted from the characteristics of their individual parts?'</li><li>'How do complex systems, such as those found in nature and human societies, exhibit emergent properties that arise from the interactions of individual components?'</li></ul> |
| lexical | <ul><li>'What is the primary difference between a generative adversarial network (GAN) and a variational autoencoder (VAE) in deep learning?'</li><li>'What is the primary difference between a Decision Tree and a Random Forest in Machine Learning, and how do they alleviate overfitting?'</li><li>'What is the primary difference between a Bayesian neural network and a traditional feedforward neural network in the context of machine learning?'</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("How doCompaniesbalanceIndividualCreativitywithTeamCollaboration to driveInnovationinthe WORKPlace?")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 5 | 18.8511 | 32 |
| Label | Training Sample Count |
|:---------|:----------------------|
| lexical | 23 |
| semantic | 24 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- 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 |
|:-------:|:-------:|:-------------:|:---------------:|
| 0.0139 | 1 | 0.2662 | - |
| 0.6944 | 50 | 0.0007 | - |
| 1.0 | 72 | - | 0.0003 |
| 1.3889 | 100 | 0.0004 | - |
| 2.0 | 144 | - | 0.0001 |
| 2.0833 | 150 | 0.0003 | - |
| 2.7778 | 200 | 0.0002 | - |
| 3.0 | 216 | - | 0.0001 |
| 3.4722 | 250 | 0.0002 | - |
| **4.0** | **288** | **-** | **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: 2.3.0+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}
}
```
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