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---
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: What are the key components involved in developing a deep learning model for
handwritten digit recognition?
- text: What is the purpose of the message posted by the CR?
- text: How can researchers create and maintain public repositories for reproducible
research?
- text: What are the key components involved in developing a deep learning model for
handwritten digit recognition?
- text: How do you prioritize and delegate tasks to ensure efficient collaboration
and feedback?
inference: true
model-index:
- name: SetFit with sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.5
name: Accuracy
---
# SetFit with sentence-transformers/all-MiniLM-L6-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-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-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-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 256 tokens
- **Number of Classes:** 4 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 |
|:--------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| lexical | <ul><li>'What are the key considerations when choosing an optimization method for a complex problem?'</li><li>'What are the challenges of being a remote mentor or sponsor?'</li><li>'How do researchers typically obtain information on the ranking of machine learning conferences?'</li></ul> |
| semantic | <ul><li>'What are common issues that users may encounter when accessing a platform that uses JumpCloud for authentication?'</li><li>'What are the key components involved in developing a deep learning model for handwritten digit recognition?'</li><li>'How can machine learning and data enrichment be used to improve business outcomes in various industries?'</li></ul> |
| very_semantic | <ul><li>"What are people's opinions on a particular topic?"</li><li>'What are the key considerations when proposing names for a project or initiative?'</li><li>'What are the key considerations for successful collaboration between industry and academia in research and development projects?'</li></ul> |
| very_lexical | <ul><li>'How can one track and store keys in a Flink operator?'</li><li>'What role do companies like Solvay play in addressing key societal challenges through their business strategies and operations?'</li><li>'What is the purpose of the scoring methodology in determining RAI maturity?'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.5 |
## 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-rag-hybrid-search-query-router-test")
# Run inference
preds = model("What is the purpose of the message posted by the CR?")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 8 | 14.4138 | 24 |
| Label | Training Sample Count |
|:--------------|:----------------------|
| lexical | 32 |
| semantic | 21 |
| very_lexical | 10 |
| very_semantic | 24 |
### 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 |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0015 | 1 | 0.268 | - |
| 0.0736 | 50 | 0.2649 | - |
| 0.1473 | 100 | 0.3352 | - |
| 0.2209 | 150 | 0.2516 | - |
| 0.2946 | 200 | 0.2438 | - |
| 0.3682 | 250 | 0.1808 | - |
| 0.4418 | 300 | 0.2365 | - |
| 0.5155 | 350 | 0.1337 | - |
| 0.5891 | 400 | 0.2263 | - |
| 0.6627 | 450 | 0.1936 | - |
| 0.7364 | 500 | 0.0612 | - |
| 0.8100 | 550 | 0.1664 | - |
| 0.8837 | 600 | 0.0987 | - |
| 0.9573 | 650 | 0.0736 | - |
| 1.0 | 679 | - | 0.2288 |
| 1.0309 | 700 | 0.0568 | - |
| 1.1046 | 750 | 0.0765 | - |
| 1.1782 | 800 | 0.1193 | - |
| 1.2518 | 850 | 0.199 | - |
| 1.3255 | 900 | 0.2734 | - |
| 1.3991 | 950 | 0.194 | - |
| 1.4728 | 1000 | 0.1085 | - |
| 1.5464 | 1050 | 0.1496 | - |
| 1.6200 | 1100 | 0.1673 | - |
| 1.6937 | 1150 | 0.2225 | - |
| 1.7673 | 1200 | 0.0503 | - |
| 1.8409 | 1250 | 0.1531 | - |
| 1.9146 | 1300 | 0.2287 | - |
| 1.9882 | 1350 | 0.1187 | - |
| **2.0** | **1358** | **-** | **0.2055** |
| 2.0619 | 1400 | 0.0546 | - |
| 2.1355 | 1450 | 0.2072 | - |
| 2.2091 | 1500 | 0.1208 | - |
| 2.2828 | 1550 | 0.0837 | - |
| 2.3564 | 1600 | 0.0405 | - |
| 2.4300 | 1650 | 0.1334 | - |
| 2.5037 | 1700 | 0.1458 | - |
| 2.5773 | 1750 | 0.2189 | - |
| 2.6510 | 1800 | 0.0561 | - |
| 2.7246 | 1850 | 0.1656 | - |
| 2.7982 | 1900 | 0.1351 | - |
| 2.8719 | 1950 | 0.1826 | - |
| 2.9455 | 2000 | 0.1905 | - |
| 3.0 | 2037 | - | 0.2273 |
* 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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