setfit safetensors xlm-roberta sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:BAAI/bge-m3 base_model:finetune:BAAI/bge-m3 model-index region:us
---
base_model: BAAI/bge-m3
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 BAAI/bge-m3
  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 BAAI/bge-m3

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) 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:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 8192 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")
# 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: (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 |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0005  | 1        | 0.257         | -               |
| 0.0250  | 50       | 0.1944        | -               |
| 0.0499  | 100      | 0.2383        | -               |
| 0.0749  | 150      | 0.1279        | -               |
| 0.0999  | 200      | 0.0033        | -               |
| 0.1248  | 250      | 0.0021        | -               |
| 0.1498  | 300      | 0.0012        | -               |
| 0.1747  | 350      | 0.0008        | -               |
| 0.1997  | 400      | 0.0004        | -               |
| 0.2247  | 450      | 0.0006        | -               |
| 0.2496  | 500      | 0.0005        | -               |
| 0.2746  | 550      | 0.0003        | -               |
| 0.2996  | 600      | 0.0003        | -               |
| 0.3245  | 650      | 0.0003        | -               |
| 0.3495  | 700      | 0.0004        | -               |
| 0.3744  | 750      | 0.0005        | -               |
| 0.3994  | 800      | 0.0003        | -               |
| 0.4244  | 850      | 0.0002        | -               |
| 0.4493  | 900      | 0.0002        | -               |
| 0.4743  | 950      | 0.0002        | -               |
| 0.4993  | 1000     | 0.0001        | -               |
| 0.5242  | 1050     | 0.0001        | -               |
| 0.5492  | 1100     | 0.0001        | -               |
| 0.5741  | 1150     | 0.0002        | -               |
| 0.5991  | 1200     | 0.0001        | -               |
| 0.6241  | 1250     | 0.0003        | -               |
| 0.6490  | 1300     | 0.0002        | -               |
| 0.6740  | 1350     | 0.0001        | -               |
| 0.6990  | 1400     | 0.0003        | -               |
| 0.7239  | 1450     | 0.0001        | -               |
| 0.7489  | 1500     | 0.0002        | -               |
| 0.7738  | 1550     | 0.0001        | -               |
| 0.7988  | 1600     | 0.0002        | -               |
| 0.8238  | 1650     | 0.0002        | -               |
| 0.8487  | 1700     | 0.0002        | -               |
| 0.8737  | 1750     | 0.0002        | -               |
| 0.8987  | 1800     | 0.0003        | -               |
| 0.9236  | 1850     | 0.0001        | -               |
| 0.9486  | 1900     | 0.0001        | -               |
| 0.9735  | 1950     | 0.0001        | -               |
| 0.9985  | 2000     | 0.0001        | -               |
| **1.0** | **2003** | **-**         | **0.1735**      |
| 1.0235  | 2050     | 0.0001        | -               |
| 1.0484  | 2100     | 0.0001        | -               |
| 1.0734  | 2150     | 0.0001        | -               |
| 1.0984  | 2200     | 0.0           | -               |
| 1.1233  | 2250     | 0.0001        | -               |
| 1.1483  | 2300     | 0.0001        | -               |
| 1.1732  | 2350     | 0.0001        | -               |
| 1.1982  | 2400     | 0.0002        | -               |
| 1.2232  | 2450     | 0.0001        | -               |
| 1.2481  | 2500     | 0.0           | -               |
| 1.2731  | 2550     | 0.0001        | -               |
| 1.2981  | 2600     | 0.0001        | -               |
| 1.3230  | 2650     | 0.0           | -               |
| 1.3480  | 2700     | 0.0001        | -               |
| 1.3729  | 2750     | 0.0001        | -               |
| 1.3979  | 2800     | 0.0001        | -               |
| 1.4229  | 2850     | 0.0           | -               |
| 1.4478  | 2900     | 0.0001        | -               |
| 1.4728  | 2950     | 0.0001        | -               |
| 1.4978  | 3000     | 0.0001        | -               |
| 1.5227  | 3050     | 0.0001        | -               |
| 1.5477  | 3100     | 0.0           | -               |
| 1.5726  | 3150     | 0.0           | -               |
| 1.5976  | 3200     | 0.0001        | -               |
| 1.6226  | 3250     | 0.0001        | -               |
| 1.6475  | 3300     | 0.0001        | -               |
| 1.6725  | 3350     | 0.0001        | -               |
| 1.6975  | 3400     | 0.0001        | -               |
| 1.7224  | 3450     | 0.0           | -               |
| 1.7474  | 3500     | 0.0002        | -               |
| 1.7723  | 3550     | 0.0001        | -               |
| 1.7973  | 3600     | 0.0           | -               |
| 1.8223  | 3650     | 0.0           | -               |
| 1.8472  | 3700     | 0.0001        | -               |
| 1.8722  | 3750     | 0.0           | -               |
| 1.8972  | 3800     | 0.0001        | -               |
| 1.9221  | 3850     | 0.0           | -               |
| 1.9471  | 3900     | 0.0           | -               |
| 1.9720  | 3950     | 0.0001        | -               |
| 1.9970  | 4000     | 0.0           | -               |
| 2.0     | 4006     | -             | 0.2593          |
| 2.0220  | 4050     | 0.0001        | -               |
| 2.0469  | 4100     | 0.0001        | -               |
| 2.0719  | 4150     | 0.0           | -               |
| 2.0969  | 4200     | 0.0001        | -               |
| 2.1218  | 4250     | 0.0           | -               |
| 2.1468  | 4300     | 0.0001        | -               |
| 2.1717  | 4350     | 0.0001        | -               |
| 2.1967  | 4400     | 0.0001        | -               |
| 2.2217  | 4450     | 0.0001        | -               |
| 2.2466  | 4500     | 0.0001        | -               |
| 2.2716  | 4550     | 0.0           | -               |
| 2.2966  | 4600     | 0.0           | -               |
| 2.3215  | 4650     | 0.0           | -               |
| 2.3465  | 4700     | 0.0001        | -               |
| 2.3714  | 4750     | 0.0001        | -               |
| 2.3964  | 4800     | 0.0002        | -               |
| 2.4214  | 4850     | 0.0001        | -               |
| 2.4463  | 4900     | 0.0001        | -               |
| 2.4713  | 4950     | 0.0           | -               |
| 2.4963  | 5000     | 0.0001        | -               |
| 2.5212  | 5050     | 0.0001        | -               |
| 2.5462  | 5100     | 0.0           | -               |
| 2.5711  | 5150     | 0.0001        | -               |
| 2.5961  | 5200     | 0.0           | -               |
| 2.6211  | 5250     | 0.0           | -               |
| 2.6460  | 5300     | 0.0           | -               |
| 2.6710  | 5350     | 0.0           | -               |
| 2.6960  | 5400     | 0.0           | -               |
| 2.7209  | 5450     | 0.0           | -               |
| 2.7459  | 5500     | 0.0           | -               |
| 2.7708  | 5550     | 0.0           | -               |
| 2.7958  | 5600     | 0.0001        | -               |
| 2.8208  | 5650     | 0.0           | -               |
| 2.8457  | 5700     | 0.0           | -               |
| 2.8707  | 5750     | 0.0           | -               |
| 2.8957  | 5800     | 0.0           | -               |
| 2.9206  | 5850     | 0.0           | -               |
| 2.9456  | 5900     | 0.0001        | -               |
| 2.9705  | 5950     | 0.0           | -               |
| 2.9955  | 6000     | 0.0           | -               |
| 3.0     | 6009     | -             | 0.2738          |

* 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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