---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/all-MiniLM-L6-v2
metrics:
- accuracy
widget:
- text: What are the benefits of using cloud storage?
- text: 'Which of the following is a Code-Based Test Coverage Metrics(E. F. Miller,
    1977 dissertation)?

    Câu hỏi 1Trả lời


    a.

    C1c: Every condition outcome


    b.

    MMCC: Multiple Module condition coverage


    c.

    Cx - Every "x" statement ("x" can be single, double, triple)


    d.

    C2: C0 coverage + loop coverage'
- text: 'Gọi X là dòng đời (thời gian làm việc tốt) của sản phẩm ổ cứng máy tính (tính
    theo năm). Một ổ cứng loại

    ABC có xác suất làm việc tốt sau 9 năm là 0.1. Giả sử hàm mật độ xác suất của
    X là f(x) = a

    (x+1)b cho x ≥ 0

    với a > 0 và b > 1. Hãy Tính a, b?'
- text: Thủ đô của nước Pháp là gì?
- text: How to prove a problem is NP complete problem
pipeline_tag: text-classification
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: 0.6666666666666666
      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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>'what is microservices'</li><li>'What is the capital of France?'</li><li>'Write a Python function that calculates the factorial of a number.'</li></ul>                                                                                                                                                                                                                                                                                                                             |
| 1     | <ul><li>'Tell me the difference between microservice and service based architecture'</li><li>'What is White-box testing?\nCâu hỏi 7Trả lời\n\na.\nAll of the other answers.\n\nb.\nA testing technique in which internal structure, design and coding of software are tested.\n\nc.\nIts foundation is to execute every part of the code at least once.\n\nd.\nIn this technique, code is visible to testers.'</li><li>'Analyze the time complexity of the merge sort algorithm.'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.6667   |

## 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("chibao24/model_routing_few_shot")
# Run inference
preds = model("Thủ đô của nước Pháp là gì?")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 3   | 20.1613 | 115 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 16                    |
| 1     | 15                    |

### Training Hyperparameters
- batch_size: (4, 4)
- num_epochs: (4, 4)
- 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 |
|:-------:|:-------:|:-------------:|:---------------:|
| 0.0078  | 1       | 0.5129        | -               |
| 0.3906  | 50      | 0.2717        | -               |
| 0.7812  | 100     | 0.0941        | -               |
| **1.0** | **128** | **-**         | **0.1068**      |
| 1.1719  | 150     | 0.0434        | -               |
| 1.5625  | 200     | 0.0075        | -               |
| 1.9531  | 250     | 0.005         | -               |
| 2.0     | 256     | -             | 0.1193          |
| 2.3438  | 300     | 0.0088        | -               |
| 2.7344  | 350     | 0.0027        | -               |
| 3.0     | 384     | -             | 0.1587          |
| 3.125   | 400     | 0.0023        | -               |
| 3.5156  | 450     | 0.0013        | -               |
| 3.9062  | 500     | 0.0011        | -               |
| 4.0     | 512     | -             | 0.1103          |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Datasets: 2.19.0
- Tokenizers: 0.19.1

## 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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