---
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: How does technology impact our daily lives and what benefits can it bring
    to various activities?
- text: How do organizations effectively deploy and manage machine learning algorithms
    to drive business value?
- text: What are the key considerations for organizing and managing computer lab resources
    and tracking their status?
- text: How can batch processing improve the efficiency of data lake operations?
- text: What is the purpose of setting up a CUPS on a server?
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: 0.8947368421052632
      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:** 2 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>"How does Happeo's search AI work to provide answers to user queries?"</li><li>'What are the primary areas of focus in the domain of Data Science and Analysis?'</li><li>'How can one organize a running event in Belgium?'</li></ul> |
| semantic | <ul><li>'What changes can be made to a channel header?'</li><li>'How can hardware capabilities impact the accuracy of motion and object detections?'</li><li>'Who is responsible for managing guarantees and prolongations?'</li></ul>        |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8947   |

## 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 setting up a CUPS on a server?")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 4   | 13.7407 | 28  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| lexical  | 44                    |
| semantic | 118                   |

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- 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 |
|:-------:|:-------:|:-------------:|:---------------:|
| 0.0020  | 1       | 0.4064        | -               |
| 0.0998  | 50      | 0.2177        | -               |
| 0.1996  | 100     | 0.0437        | -               |
| 0.2994  | 150     | 0.0057        | -               |
| 0.3992  | 200     | 0.0034        | -               |
| 0.4990  | 250     | 0.0009        | -               |
| 0.5988  | 300     | 0.0009        | -               |
| 0.6986  | 350     | 0.0007        | -               |
| 0.7984  | 400     | 0.0007        | -               |
| 0.8982  | 450     | 0.0009        | -               |
| 0.9980  | 500     | 0.0005        | -               |
| **1.0** | **501** | **-**         | **0.1811**      |

* 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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