---
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 difference between ACID compliance and BASE topology in distributed\
\ database systems?\n While designing a high throughput RDBMS like\
\ Amazon Aurora, how would you choose between consistency model that is highly\
\ optimistic and one that highly paranoid in conflictedupdate scenarios"
- text: What is the primary function of the Apache Hive metastore in a Hadoop ecosystem,
and how does it differ from a traditional relational database management system?
- text: What is the primary purpose of employing Entity-Controlled Vocabulary (ESS)
in open data publishing, according to industry experts at Microsoft?
- text: How do organizations prioritize innovation to strive in a rapidly changing
industry landscape driven by technological and societal shifts?
- text: How do societal norms influence the emergence of new business models in unstable
economies?
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
### 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 |
- 'How do artificial intelligence systems navigate the trade-off between simplicity and accuracy when modeling complex real-world phenomena?'
- '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?'
- 'How do complex systems, such as those found in nature and human societies, exhibit emergent properties that arise from the interactions of individual components?'
|
| lexical | - 'What is the primary difference between a generative adversarial network (GAN) and a variational autoencoder (VAE) in deep learning?'
- 'What is the primary difference between a Decision Tree and a Random Forest in Machine Learning, and how do they alleviate overfitting?'
- 'What is the primary difference between a Bayesian neural network and a traditional feedforward neural network in the context of machine learning?'
|
## 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 do societal norms influence the emergence of new business models in unstable economies?")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 4 | 18.6566 | 56 |
| Label | Training Sample Count |
|:---------|:----------------------|
| lexical | 47 |
| semantic | 52 |
### 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 |
|:-------:|:-------:|:-------------:|:---------------:|
| 0.0032 | 1 | 0.3177 | - |
| 0.1592 | 50 | 0.0905 | - |
| 0.3185 | 100 | 0.0013 | - |
| 0.4777 | 150 | 0.0011 | - |
| 0.6369 | 200 | 0.0002 | - |
| 0.7962 | 250 | 0.0003 | - |
| 0.9554 | 300 | 0.0001 | - |
| 1.0 | 314 | - | 0.0001 |
| 1.1146 | 350 | 0.0001 | - |
| 1.2739 | 400 | 0.0001 | - |
| 1.4331 | 450 | 0.0001 | - |
| 1.5924 | 500 | 0.0001 | - |
| 1.7516 | 550 | 0.0001 | - |
| 1.9108 | 600 | 0.0001 | - |
| 2.0 | 628 | - | 0.0 |
| 2.0701 | 650 | 0.0001 | - |
| 2.2293 | 700 | 0.0 | - |
| 2.3885 | 750 | 0.0001 | - |
| 2.5478 | 800 | 0.0 | - |
| 2.7070 | 850 | 0.0001 | - |
| 2.8662 | 900 | 0.0001 | - |
| **3.0** | **942** | **-** | **0.0** |
| 3.0255 | 950 | 0.0001 | - |
| 3.1847 | 1000 | 0.0 | - |
| 3.3439 | 1050 | 0.0001 | - |
| 3.5032 | 1100 | 0.0001 | - |
| 3.6624 | 1150 | 0.0 | - |
| 3.8217 | 1200 | 0.0001 | - |
| 3.9809 | 1250 | 0.0001 | - |
| 4.0 | 1256 | - | 0.0 |
* 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}
}
```