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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: 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 -->
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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 |
|:---------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 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?")
```
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## 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}
}
```
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