README.md

---
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: W elementach regału brakuje kilku otworów montażowych, uniemożliwiających
    prawidłowe złożenie.
- text: Półki regału Biblioteka są zbyt wąskie.
- text: Łóżko drewniane posiada wadę konstrukcji.
- text: Noga krzesła drewnianego Country jest złamana.
- text: Konstrukcja łóżka piętrowego jest wadliwa, elementy nie pasują do siebie.
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: 0.8632478632478633
      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:** 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                                                                                                                                                                                                                                                                                                             |
|:---------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| błędny montaż                    | <ul><li>'Pojemnik pod łóżkiem jest źle zamontowany i nie otwiera się.'</li><li>'Nogi komody nowoczesnej są źle przymocowane i komoda jest niestabilna.'</li><li>'Oświetlenie w witrynie z oświetleniem jest źle podłączone i nie działa.'</li></ul>                                                                  |
| wady fabryczne                   | <ul><li>'Front szafki RTV jest uszkodzony, posiada wgniecenie i rysę.'</li><li>'Tapicerka krzesła jest rozdarcia.'</li><li>'Blat biurka narożnego ma pęknięcie, widoczne gołym okiem.'</li></ul>                                                                                                                     |
| niezgodność towaru z zamówieniem | <ul><li>'Materiał szafy Prosty jest inny niż ten, który został zamówiony.'</li><li>'Regał narożny ma inne wymiary niż te, które zostały podane w zamówieniu.'</li><li>'Zamówiony fotel bujany miał zawierać poduszkę, której nie ma w przesyłce. Potwierdzenie zamówienia wskazuje na dołączoną poduszkę.'</li></ul> |
| uszkodzenia                      | <ul><li>'Szyba stolika kawowego Minimal jest pęknięta.'</li><li>'Prowadnice szuflad w szafie wnękowej Max są uszkodzone.'</li><li>'Drzwiczki szafy na buty Shoes są uszkodzone.'</li></ul>                                                                                                                           |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8632   |

## 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("janmariakowalski/LiderzyAI-homestyle-reklamacje")
# Run inference
preds = model("Półki regału Biblioteka są zbyt wąskie.")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 4   | 10.0   | 20  |

| Label                            | Training Sample Count |
|:---------------------------------|:----------------------|
| uszkodzenia                      | 8                     |
| wady fabryczne                   | 8                     |
| niezgodność towaru z zamówieniem | 8                     |
| błędny montaż                    | 8                     |

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (10, 10)
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0417 | 1    | 0.1938        | -               |
| 4.1667 | 100  | 0.0392        | -               |
| 8.3333 | 200  | 0.0011        | -               |

### Framework Versions
- Python: 3.11.0
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.44.2
- PyTorch: 2.4.1
- 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->