---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Ruukki Group calculates that it has lost EUR 4mn in the failed project .
- text: The Tecnomen Convergent Charging solution includes functionality for prepaid
    and post-paid billing , charging and rating of voice calls , video calls , raw
    data traffic and any type of content services in both mobile and fixed networks
    .
- text: The combined value of the planned investments is about EUR 30mn .
- text: The Diameter Protocol is developed according to the standards IETF RFC 3588
    and IETF RFC 3539 .
- text: Below are unaudited consolidated results for Aspocomp Group under IFRS reporting
    standards .
pipeline_tag: text-classification
inference: true
base_model: BAAI/bge-small-en-v1.5
model-index:
- name: SetFit with BAAI/bge-small-en-v1.5
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.9426048565121413
      name: Accuracy
---

# SetFit with BAAI/bge-small-en-v1.5

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 3 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
|:---------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| positive | <ul><li>'HELSINKI ( AFX ) - Nokian Tyres reported a fourth quarter pretax profit of 61.5 mln eur , up from 48.6 mln on the back of strong sales .'</li><li>'Equity ratio was 60.9 % compared to 54.2 % In the third quarter of 2007 , net sales of the Frozen Foods Business totaled EUR 11.0 , up by about 5 % from the third quarter of 2006 .'</li><li>"`` After a long , unprofitable period the Food Division posted a profitable result , which speaks of a healthier cost structure and a new approach in business operations , '' Rihko said ."</li></ul> |
| neutral  | <ul><li>'Their names have not yet been released .'</li><li>'The contract includes design , construction , delivery of equipment , installation and commissioning .'</li><li>"Tieto 's service is also used to send , process and receive materials related to absentee voting ."</li></ul>                                                                                                                                                                                                                                                                        |
| negative | <ul><li>'The company confirmed its estimate for lower revenue for the whole 2009 than the year-ago EUR93 .9 m as given in the interim report on 5 August 2009 .'</li><li>'Acando AB ( ACANB SS ) fell 8.9 percent to 13.35 kronor , the lowest close since Dec. 11 .'</li><li>'Okmetic expects its net sales for the first half of 2009 to be less than in 2008 .'</li></ul>                                                                                                                                                                                      |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9426   |

## 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("moshew/bge-small-en-v1.5-SetFit-FSA")
# Run inference
preds = model("The combined value of the planned investments is about EUR 30mn .")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 2   | 22.4020 | 60  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| negative | 266                   |
| neutral  | 1142                  |
| positive | 403                   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- 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: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0004 | 1    | 0.2832        | -               |
| 0.0221 | 50   | 0.209         | -               |
| 0.0442 | 100  | 0.1899        | -               |
| 0.0663 | 150  | 0.1399        | -               |
| 0.0883 | 200  | 0.1274        | -               |
| 0.1104 | 250  | 0.0586        | -               |
| 0.1325 | 300  | 0.0756        | -               |
| 0.1546 | 350  | 0.0777        | -               |
| 0.1767 | 400  | 0.0684        | -               |
| 0.1988 | 450  | 0.0311        | -               |
| 0.2208 | 500  | 0.0102        | -               |
| 0.2429 | 550  | 0.052         | -               |
| 0.2650 | 600  | 0.0149        | -               |
| 0.2871 | 650  | 0.1042        | -               |
| 0.3092 | 700  | 0.061         | -               |
| 0.3313 | 750  | 0.0083        | -               |
| 0.3534 | 800  | 0.0036        | -               |
| 0.3754 | 850  | 0.002         | -               |
| 0.3975 | 900  | 0.0598        | -               |
| 0.4196 | 950  | 0.0036        | -               |
| 0.4417 | 1000 | 0.0027        | -               |
| 0.4638 | 1050 | 0.0617        | -               |
| 0.4859 | 1100 | 0.0015        | -               |
| 0.5080 | 1150 | 0.0022        | -               |
| 0.5300 | 1200 | 0.0016        | -               |
| 0.5521 | 1250 | 0.0009        | -               |
| 0.5742 | 1300 | 0.0013        | -               |
| 0.5963 | 1350 | 0.0009        | -               |
| 0.6184 | 1400 | 0.0015        | -               |
| 0.6405 | 1450 | 0.0018        | -               |
| 0.6625 | 1500 | 0.0015        | -               |
| 0.6846 | 1550 | 0.0018        | -               |
| 0.7067 | 1600 | 0.0016        | -               |
| 0.7288 | 1650 | 0.0022        | -               |
| 0.7509 | 1700 | 0.0013        | -               |
| 0.7730 | 1750 | 0.0108        | -               |
| 0.7951 | 1800 | 0.0016        | -               |
| 0.8171 | 1850 | 0.0021        | -               |
| 0.8392 | 1900 | 0.002         | -               |
| 0.8613 | 1950 | 0.0015        | -               |
| 0.8834 | 2000 | 0.0016        | -               |
| 0.9055 | 2050 | 0.0028        | -               |
| 0.9276 | 2100 | 0.0013        | -               |
| 0.9496 | 2150 | 0.0019        | -               |
| 0.9717 | 2200 | 0.0075        | -               |
| 0.9938 | 2250 | 0.0015        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.5.1
- Transformers: 4.38.1
- PyTorch: 2.1.0+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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