---
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) -->
<!-- - **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 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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