---
base_model: intfloat/multilingual-e5-large-instruct
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: '"Он подарил мне красивое кольцо и прекрасную вечеринку на нашу годовщину."
    Бұл мәтінді қазақ тіліне аударып беріңізші.'
- text: Would you please put that cigarette out? I get sick on it.
- text: Сәлем!
- text: Никусор Эшану
- text: How time flies! We have been lovers for nearly a year. We hit it off instantly.
inference: true
model-index:
- name: SetFit with intfloat/multilingual-e5-large-instruct
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.9955398215928637
      name: Accuracy
---

# SetFit with intfloat/multilingual-e5-large-instruct

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) 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:** [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct)
- **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:** 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                                                                                                                                                                                          |
|:-------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| rag    | <ul><li>'Саксон эпизоды туралы қандай тарихи құжатта мәлімет берілген?'</li><li>'Uttermost өзінің жарыс мансабында қандай маңызды жетістіктерге қол жеткізді?'</li><li>'Ричард Бахтелл'</li></ul> |
| no_rag | <ul><li>'Just a moment, please.'</li><li>'орыс тіліндегі "Я рабочий." сөйлемінің қазақ тіліндегі аудармасы не?'</li><li>'You look tired. Did you sleep well last night?'</li></ul>                |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9955   |

## 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("nlp-team-issai/setfit-me5-large-instruct-v3")
# Run inference
preds = model("Сәлем!")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 10.0022 | 138 |

| Label  | Training Sample Count |
|:-------|:----------------------|
| no_rag | 218                   |
| rag    | 241                   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0003 | 1    | 0.3567        | -               |
| 0.0151 | 50   | 0.2851        | -               |
| 0.0302 | 100  | 0.0943        | -               |
| 0.0452 | 150  | 0.0123        | -               |
| 0.0603 | 200  | 0.0099        | -               |
| 0.0754 | 250  | 0.0056        | -               |
| 0.0905 | 300  | 0.0011        | -               |
| 0.1056 | 350  | 0.0003        | -               |
| 0.1207 | 400  | 0.0002        | -               |
| 0.1357 | 450  | 0.0001        | -               |
| 0.1508 | 500  | 0.0001        | -               |
| 0.1659 | 550  | 0.0001        | -               |
| 0.1810 | 600  | 0.0001        | -               |
| 0.1961 | 650  | 0.0001        | -               |
| 0.2112 | 700  | 0.0001        | -               |
| 0.2262 | 750  | 0.0001        | -               |
| 0.2413 | 800  | 0.0001        | -               |
| 0.2564 | 850  | 0.0001        | -               |
| 0.2715 | 900  | 0.0001        | -               |
| 0.2866 | 950  | 0.0001        | -               |
| 0.3017 | 1000 | 0.0001        | -               |
| 0.3167 | 1050 | 0.0001        | -               |
| 0.3318 | 1100 | 0.0001        | -               |
| 0.3469 | 1150 | 0.0001        | -               |
| 0.3620 | 1200 | 0.0001        | -               |
| 0.3771 | 1250 | 0.0001        | -               |
| 0.3922 | 1300 | 0.0001        | -               |
| 0.4072 | 1350 | 0.0001        | -               |
| 0.4223 | 1400 | 0.0           | -               |
| 0.4374 | 1450 | 0.0           | -               |
| 0.4525 | 1500 | 0.0           | -               |
| 0.4676 | 1550 | 0.0           | -               |
| 0.4827 | 1600 | 0.0           | -               |
| 0.4977 | 1650 | 0.0           | -               |
| 0.5128 | 1700 | 0.0           | -               |
| 0.5279 | 1750 | 0.0           | -               |
| 0.5430 | 1800 | 0.0           | -               |
| 0.5581 | 1850 | 0.0           | -               |
| 0.5732 | 1900 | 0.0           | -               |
| 0.5882 | 1950 | 0.0           | -               |
| 0.6033 | 2000 | 0.0           | -               |
| 0.6184 | 2050 | 0.0           | -               |
| 0.6335 | 2100 | 0.0           | -               |
| 0.6486 | 2150 | 0.0           | -               |
| 0.6637 | 2200 | 0.0           | -               |
| 0.6787 | 2250 | 0.0           | -               |
| 0.6938 | 2300 | 0.0           | -               |
| 0.7089 | 2350 | 0.0           | -               |
| 0.7240 | 2400 | 0.0           | -               |
| 0.7391 | 2450 | 0.0           | -               |
| 0.7541 | 2500 | 0.0           | -               |
| 0.7692 | 2550 | 0.0           | -               |
| 0.7843 | 2600 | 0.0           | -               |
| 0.7994 | 2650 | 0.0           | -               |
| 0.8145 | 2700 | 0.0           | -               |
| 0.8296 | 2750 | 0.0           | -               |
| 0.8446 | 2800 | 0.0           | -               |
| 0.8597 | 2850 | 0.0           | -               |
| 0.8748 | 2900 | 0.0           | -               |
| 0.8899 | 2950 | 0.0           | -               |
| 0.9050 | 3000 | 0.0           | -               |
| 0.9201 | 3050 | 0.0           | -               |
| 0.9351 | 3100 | 0.0           | -               |
| 0.9502 | 3150 | 0.0           | -               |
| 0.9653 | 3200 | 0.0           | -               |
| 0.9804 | 3250 | 0.0           | -               |
| 0.9955 | 3300 | 0.0           | -               |

### Framework Versions
- Python: 3.12.5
- SetFit: 1.1.0
- Sentence Transformers: 3.2.0
- Transformers: 4.45.2
- PyTorch: 2.4.0+cu121
- Datasets: 3.0.1
- Tokenizers: 0.20.0

## 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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