setfit safetensors xlm-roberta sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:lighteternal/stsb-xlm-r-greek-transfer base_model:finetune:lighteternal/stsb-xlm-r-greek-transfer model-index region:us
---
base_model: lighteternal/stsb-xlm-r-greek-transfer
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: παστα ατομικη
- text: mikel mini croissant σοκολατα
- text: tasty nat nut παστελι σουσαμι
- text: σκιουφιχτα σαλτσα ντοματας μυζηθρα σαλτσα ντοματας ελιες καππαρη μυζηθρα
- text: κρασι ροζε λιανος
inference: false
model-index:
- name: SetFit with lighteternal/stsb-xlm-r-greek-transfer
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.1588785046728972
      name: Accuracy
---

# SetFit with lighteternal/stsb-xlm-r-greek-transfer

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [lighteternal/stsb-xlm-r-greek-transfer](https://huggingface.co/lighteternal/stsb-xlm-r-greek-transfer) as the Sentence Transformer embedding model. A OneVsRestClassifier 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:** [lighteternal/stsb-xlm-r-greek-transfer](https://huggingface.co/lighteternal/stsb-xlm-r-greek-transfer)
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 400 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **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)

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.1589   |

## 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("st-karlos-efood/setfit-multilabel-one-vs-rest-feb-2024")
# Run inference
preds = model("παστα ατομικη")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 8.6048 | 116 |

### Training Hyperparameters
- batch_size: (48, 48)
- num_epochs: (5, 5)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0008 | 1    | 0.2009        | -               |
| 0.0377 | 50   | 0.1674        | -               |
| 0.0754 | 100  | 0.1593        | -               |
| 0.1131 | 150  | 0.1793        | -               |
| 0.1508 | 200  | 0.176         | -               |
| 0.1885 | 250  | 0.1818        | -               |
| 0.2262 | 300  | 0.1209        | -               |
| 0.2640 | 350  | 0.1546        | -               |
| 0.3017 | 400  | 0.0996        | -               |
| 0.3394 | 450  | 0.1108        | -               |
| 0.3771 | 500  | 0.1163        | -               |
| 0.4148 | 550  | 0.1102        | -               |
| 0.4525 | 600  | 0.1477        | -               |
| 0.4902 | 650  | 0.0973        | -               |
| 0.5279 | 700  | 0.1324        | -               |
| 0.5656 | 750  | 0.1792        | -               |
| 0.6033 | 800  | 0.1026        | -               |
| 0.6410 | 850  | 0.1461        | -               |
| 0.6787 | 900  | 0.117         | -               |
| 0.7164 | 950  | 0.0907        | -               |
| 0.7541 | 1000 | 0.0904        | -               |
| 0.7919 | 1050 | 0.1168        | -               |
| 0.8296 | 1100 | 0.0831        | -               |
| 0.8673 | 1150 | 0.0623        | -               |
| 0.9050 | 1200 | 0.0802        | -               |
| 0.9427 | 1250 | 0.0802        | -               |
| 0.9804 | 1300 | 0.1212        | -               |
| 1.0181 | 1350 | 0.0872        | -               |
| 1.0558 | 1400 | 0.1068        | -               |
| 1.0935 | 1450 | 0.0975        | -               |
| 1.1312 | 1500 | 0.096         | -               |
| 1.1689 | 1550 | 0.0649        | -               |
| 1.2066 | 1600 | 0.1004        | -               |
| 1.2443 | 1650 | 0.0818        | -               |
| 1.2821 | 1700 | 0.0714        | -               |
| 1.3198 | 1750 | 0.0875        | -               |
| 1.3575 | 1800 | 0.0893        | -               |
| 1.3952 | 1850 | 0.1132        | -               |
| 1.4329 | 1900 | 0.1127        | -               |
| 1.4706 | 1950 | 0.0707        | -               |
| 1.5083 | 2000 | 0.0819        | -               |
| 1.5460 | 2050 | 0.0954        | -               |
| 1.5837 | 2100 | 0.0948        | -               |
| 1.6214 | 2150 | 0.0953        | -               |
| 1.6591 | 2200 | 0.0813        | -               |
| 1.6968 | 2250 | 0.0974        | -               |
| 1.7345 | 2300 | 0.0785        | -               |
| 1.7722 | 2350 | 0.086         | -               |
| 1.8100 | 2400 | 0.0808        | -               |
| 1.8477 | 2450 | 0.1014        | -               |
| 1.8854 | 2500 | 0.112         | -               |
| 1.9231 | 2550 | 0.0765        | -               |
| 1.9608 | 2600 | 0.0694        | -               |
| 1.9985 | 2650 | 0.0915        | -               |
| 2.0362 | 2700 | 0.087         | -               |
| 2.0739 | 2750 | 0.0831        | -               |
| 2.1116 | 2800 | 0.1223        | -               |
| 2.1493 | 2850 | 0.0897        | -               |
| 2.1870 | 2900 | 0.0937        | -               |
| 2.2247 | 2950 | 0.0862        | -               |
| 2.2624 | 3000 | 0.0977        | -               |
| 2.3002 | 3050 | 0.0563        | -               |
| 2.3379 | 3100 | 0.1197        | -               |
| 2.3756 | 3150 | 0.095         | -               |
| 2.4133 | 3200 | 0.0702        | -               |
| 2.4510 | 3250 | 0.0823        | -               |
| 2.4887 | 3300 | 0.1309        | -               |
| 2.5264 | 3350 | 0.0612        | -               |
| 2.5641 | 3400 | 0.0994        | -               |
| 2.6018 | 3450 | 0.0904        | -               |
| 2.6395 | 3500 | 0.0678        | -               |
| 2.6772 | 3550 | 0.0896        | -               |
| 2.7149 | 3600 | 0.0753        | -               |
| 2.7526 | 3650 | 0.0997        | -               |
| 2.7903 | 3700 | 0.0956        | -               |
| 2.8281 | 3750 | 0.1016        | -               |
| 2.8658 | 3800 | 0.0784        | -               |
| 2.9035 | 3850 | 0.0911        | -               |
| 2.9412 | 3900 | 0.0485        | -               |
| 2.9789 | 3950 | 0.1078        | -               |
| 3.0166 | 4000 | 0.0659        | -               |
| 3.0543 | 4050 | 0.0802        | -               |
| 3.0920 | 4100 | 0.12          | -               |
| 3.1297 | 4150 | 0.0519        | -               |
| 3.1674 | 4200 | 0.047         | -               |
| 3.2051 | 4250 | 0.0906        | -               |
| 3.2428 | 4300 | 0.0999        | -               |
| 3.2805 | 4350 | 0.059         | -               |
| 3.3183 | 4400 | 0.0533        | -               |
| 3.3560 | 4450 | 0.1033        | -               |
| 3.3937 | 4500 | 0.0871        | -               |
| 3.4314 | 4550 | 0.065         | -               |
| 3.4691 | 4600 | 0.1487        | -               |
| 3.5068 | 4650 | 0.0542        | -               |
| 3.5445 | 4700 | 0.0846        | -               |
| 3.5822 | 4750 | 0.0756        | -               |
| 3.6199 | 4800 | 0.0518        | -               |
| 3.6576 | 4850 | 0.1035        | -               |
| 3.6953 | 4900 | 0.1129        | -               |
| 3.7330 | 4950 | 0.1319        | -               |
| 3.7707 | 5000 | 0.0804        | -               |
| 3.8084 | 5050 | 0.108         | -               |
| 3.8462 | 5100 | 0.1246        | -               |
| 3.8839 | 5150 | 0.0923        | -               |
| 3.9216 | 5200 | 0.1048        | -               |
| 3.9593 | 5250 | 0.0951        | -               |
| 3.9970 | 5300 | 0.1015        | -               |
| 4.0347 | 5350 | 0.0888        | -               |
| 4.0724 | 5400 | 0.0917        | -               |
| 4.1101 | 5450 | 0.0823        | -               |
| 4.1478 | 5500 | 0.0882        | -               |
| 4.1855 | 5550 | 0.0807        | -               |
| 4.2232 | 5600 | 0.0997        | -               |
| 4.2609 | 5650 | 0.0782        | -               |
| 4.2986 | 5700 | 0.1165        | -               |
| 4.3363 | 5750 | 0.0837        | -               |
| 4.3741 | 5800 | 0.1098        | -               |
| 4.4118 | 5850 | 0.0564        | -               |
| 4.4495 | 5900 | 0.0715        | -               |
| 4.4872 | 5950 | 0.0858        | -               |
| 4.5249 | 6000 | 0.0889        | -               |
| 4.5626 | 6050 | 0.0719        | -               |
| 4.6003 | 6100 | 0.1076        | -               |
| 4.6380 | 6150 | 0.1044        | -               |
| 4.6757 | 6200 | 0.0914        | -               |
| 4.7134 | 6250 | 0.1078        | -               |
| 4.7511 | 6300 | 0.1137        | -               |
| 4.7888 | 6350 | 0.0666        | -               |
| 4.8265 | 6400 | 0.1009        | -               |
| 4.8643 | 6450 | 0.0537        | -               |
| 4.9020 | 6500 | 0.0576        | -               |
| 4.9397 | 6550 | 0.1366        | -               |
| 4.9774 | 6600 | 0.1009        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.0
- Tokenizers: 0.15.1

## 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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