---
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 situations that require the preparation of a mission order?
- text: How can audio data be used to improve speaker identification using neural
    networks?
- text: How can organizations balance the need for data privacy with the benefits
    of involving interns in data-related projects?
- text: What is the purpose of the message posted by the CR?
- text: What are the consequences of adopting a 'if not broken, don't fix' attitude
    towards data monitoring?
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: 0.3076923076923077
      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                                                                                                                                                                                                                                                                                                                                              |
|:--------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| very_semantic | <ul><li>'What are the key considerations when proposing names for a project or initiative?'</li><li>'What are the key aspects of team life and events in a company?'</li><li>'What is being asked for or sought in this conversation?'</li></ul>                                                                                                      |
| lexical       | <ul><li>'Who is responsible for reviewing and signing documents related to conference submissions?'</li><li>'How do data architecture and management systems enable digital transformation and address its associated challenges?'</li><li>'How do keys or access credentials get shared or transferred among team members in a workplace?'</li></ul> |
| very_lexical  | <ul><li>'What are some of the key challenges associated with handling and storing large amounts of genomic data?'</li><li>"What is the focus of Eurobiomed's partnership with Digital113?"</li><li>'What are the key considerations for generating well-formatted JSON instances that conform to a given schema?'</li></ul>                           |
| semantic      | <ul><li>'How can visualizations be used to enhance documentation and collaboration in software development?'</li><li>'What are the key considerations when choosing a distance metric for a vector database?'</li><li>'How can AI be leveraged to support HR departments in detecting and addressing gender bias?'</li></ul>                          |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.3077   |

## 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   | 7   | 14.1913 | 24  |

| Label         | Training Sample Count |
|:--------------|:----------------------|
| lexical       | 41                    |
| semantic      | 24                    |
| very_lexical  | 17                    |
| very_semantic | 33                    |

### 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 |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0008  | 1        | 0.4237        | -               |
| 0.0417  | 50       | 0.2917        | -               |
| 0.0834  | 100      | 0.1835        | -               |
| 0.1251  | 150      | 0.3215        | -               |
| 0.1668  | 200      | 0.2299        | -               |
| 0.2085  | 250      | 0.2595        | -               |
| 0.2502  | 300      | 0.3193        | -               |
| 0.2919  | 350      | 0.2288        | -               |
| 0.3336  | 400      | 0.2947        | -               |
| 0.3753  | 450      | 0.1171        | -               |
| 0.4170  | 500      | 0.1442        | -               |
| 0.4587  | 550      | 0.1859        | -               |
| 0.5004  | 600      | 0.1959        | -               |
| 0.5421  | 650      | 0.2797        | -               |
| 0.5838  | 700      | 0.2079        | -               |
| 0.6255  | 750      | 0.2706        | -               |
| 0.6672  | 800      | 0.1956        | -               |
| 0.7089  | 850      | 0.0833        | -               |
| 0.7506  | 900      | 0.1421        | -               |
| 0.7923  | 950      | 0.2345        | -               |
| 0.8340  | 1000     | 0.1347        | -               |
| 0.8757  | 1050     | 0.241         | -               |
| 0.9174  | 1100     | 0.133         | -               |
| 0.9591  | 1150     | 0.1041        | -               |
| **1.0** | **1199** | **-**         | **0.3562**      |
| 1.0008  | 1200     | 0.0837        | -               |
| 1.0425  | 1250     | 0.1566        | -               |
| 1.0842  | 1300     | 0.2101        | -               |
| 1.1259  | 1350     | 0.0496        | -               |
| 1.1676  | 1400     | 0.063         | -               |
| 1.2093  | 1450     | 0.149         | -               |
| 1.2510  | 1500     | 0.038         | -               |
| 1.2927  | 1550     | 0.0504        | -               |
| 1.3344  | 1600     | 0.0679        | -               |
| 1.3761  | 1650     | 0.1699        | -               |
| 1.4178  | 1700     | 0.1293        | -               |
| 1.4595  | 1750     | 0.1083        | -               |
| 1.5013  | 1800     | 0.2044        | -               |
| 1.5430  | 1850     | 0.1267        | -               |
| 1.5847  | 1900     | 0.0842        | -               |
| 1.6264  | 1950     | 0.1126        | -               |
| 1.6681  | 2000     | 0.0544        | -               |
| 1.7098  | 2050     | 0.143         | -               |
| 1.7515  | 2100     | 0.08          | -               |
| 1.7932  | 2150     | 0.1103        | -               |
| 1.8349  | 2200     | 0.1768        | -               |
| 1.8766  | 2250     | 0.1639        | -               |
| 1.9183  | 2300     | 0.1637        | -               |
| 1.9600  | 2350     | 0.1637        | -               |
| 2.0     | 2398     | -             | 0.3682          |
| 2.0017  | 2400     | 0.2938        | -               |
| 2.0434  | 2450     | 0.0808        | -               |
| 2.0851  | 2500     | 0.0788        | -               |
| 2.1268  | 2550     | 0.2187        | -               |
| 2.1685  | 2600     | 0.0701        | -               |
| 2.2102  | 2650     | 0.0385        | -               |
| 2.2519  | 2700     | 0.135         | -               |
| 2.2936  | 2750     | 0.2276        | -               |
| 2.3353  | 2800     | 0.2203        | -               |
| 2.3770  | 2850     | 0.0029        | -               |
| 2.4187  | 2900     | 0.1855        | -               |
| 2.4604  | 2950     | 0.1278        | -               |
| 2.5021  | 3000     | 0.0487        | -               |
| 2.5438  | 3050     | 0.0404        | -               |
| 2.5855  | 3100     | 0.1158        | -               |
| 2.6272  | 3150     | 0.1354        | -               |
| 2.6689  | 3200     | 0.1633        | -               |
| 2.7106  | 3250     | 0.1484        | -               |
| 2.7523  | 3300     | 0.1146        | -               |
| 2.7940  | 3350     | 0.1437        | -               |
| 2.8357  | 3400     | 0.0948        | -               |
| 2.8774  | 3450     | 0.0833        | -               |
| 2.9191  | 3500     | 0.0668        | -               |
| 2.9608  | 3550     | 0.1687        | -               |
| 3.0     | 3597     | -             | 0.3651          |

* 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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