---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/distiluse-base-multilingual-cased-v2
metrics:
- accuracy
widget:
- text: explain in detail what is FFT and the complexity of it
- text: The p success of karger min cut after k steps
- text: Giải thích sự khác biệt giữa mô hình học có giám sát và không giám sát. Cung
    cấp ví dụ cho từng loại. (ít nhất 150 từ)
- 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: What is software testing?
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with sentence-transformers/distiluse-base-multilingual-cased-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/distiluse-base-multilingual-cased-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-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/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 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 the capital of France?'</li><li>'Nêu ngắn gọn về quá trình quang hợp.'</li><li>'Briefly describe the concept of photosynthesis.'</li></ul>                                                                                                                                                                                                                                                                                                                                                                                              |
| 1     | <ul><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>'For the expression "(a AND (b OR c))", which of the following test-cases is Multiple Condition Coverage (MCC)?'</li><li>'The p success of karger min cut after k steps. Tell me the detail of the equation'</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("chibao24/model_routing_few_shot")
# Run inference
preds = model("What is software testing?")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 3   | 20.2903 | 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       | 0.4319        | -               |
| 0.3906  | 50      | 0.1028        | -               |
| 0.7812  | 100     | 0.0021        | -               |
| **1.0** | **128** | **-**         | **0.2328**      |
| 1.1719  | 150     | 0.0002        | -               |
| 1.5625  | 200     | 0.0           | -               |
| 1.9531  | 250     | 0.0001        | -               |
| 2.0     | 256     | -             | 0.3315          |
| 2.3438  | 300     | 0.0           | -               |
| 2.7344  | 350     | 0.0002        | -               |
| 3.0     | 384     | -             | 0.2364          |
| 3.125   | 400     | 0.0001        | -               |
| 3.5156  | 450     | 0.0           | -               |
| 3.9062  | 500     | 0.0001        | -               |
| 4.0     | 512     | -             | 0.3333          |

* 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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