---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Solicite um relatório financeiro trimestral via ERP conectado.
- text: If you save $200 monthly, how much money will you have saved after 18 months?
- text: Get the stock price history of Tesla for the last month.
- text: Given a historical archive of economic indicators, build a forecasting model
    that predicts recessions, incorporating leading, lagging, and coincident indicators
    with explainable outputs.
- text: Narrate the experience of a character born without the ability to dream.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: ibm-granite/granite-embedding-107m-multilingual
model-index:
- name: SetFit with ibm-granite/granite-embedding-107m-multilingual
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.9966555183946488
      name: Accuracy
---

# SetFit with ibm-granite/granite-embedding-107m-multilingual

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [ibm-granite/granite-embedding-107m-multilingual](https://huggingface.co/ibm-granite/granite-embedding-107m-multilingual) 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:** [ibm-granite/granite-embedding-107m-multilingual](https://huggingface.co/ibm-granite/granite-embedding-107m-multilingual)
- **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:** 8 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                                                                                                                                                                                                                                                                                                                                                                                                        |
|:------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| summarization     | <ul><li>'Resuma um texto acadêmico sobre psicologia do comportamento.'</li><li>'Summarize the timeline and outcomes of a historical event based on multiple eyewitness accounts.'</li><li>'Extract and summarize the key lessons learned from multiple post-project reviews.'</li></ul>                                                                                                                         |
| general_knowledge | <ul><li>'Qual é a importância da agricultura para a economia brasileira?'</li><li>'Quais são os principais países membros da Organização dos Países Exportadores de Petróleo (OPEP)?'</li><li>'What is the mechanism by which vaccines provide immunity?'</li></ul>                                                                                                                                             |
| roleplay          | <ul><li>'Personifique um chef pâtissier criando uma sobremesa para um júri exigente.'</li><li>'You are a software tester devising scenarios to uncover bugs in a complex system.'</li><li>'Simule uma reunião de conselho editorial decidindo o rumo de uma grande publicação.'</li></ul>                                                                                                                       |
| creativity        | <ul><li>'Write a thriller in which the protagonist communicates only through artwork.'</li><li>'Imagine um poema narrativo sobre a relação entre o sertão e a poesia de uma geração esquecida.'</li><li>'Write a story from the perspective of a shadow that gains independence.'</li></ul>                                                                                                                     |
| complex_reasoning | <ul><li>'Analise as implicações do uso de drones autônomos para entregas em áreas urbanas densas.'</li><li>'Proponha um sistema para avaliação automatizada e justa de currículos em processos seletivos corporativos.'</li><li>'Proponha um modelo para prever o crescimento urbano sustentável considerando variáveis ambientais e sociais.'</li></ul>                                                        |
| coding            | <ul><li>'Implemente uma função para decompor números inteiros em fatores primos eficientemente para valores grandes.'</li><li>'Create an integration that consumes streaming data from an external message broker and processes events in real-time with backpressure management.'</li><li>'Escreva um algoritmo para encontrar os pontos de articulação (cut vertices) em um grafo não direcionado.'</li></ul> |
| basic_reasoning   | <ul><li>'Se um carro consome 12 litros de gasolina para 100 km, quantos litros usará para 150 km?'</li><li>'If a ladder leans against a wall forming a 60-degree angle and the ladder length is 10 feet, how high does it reach on the wall?'</li><li>'Quantos centímetros tem 1 metro?'</li></ul>                                                                                                              |
| tool              | <ul><li>'Fetch comprehensive user reviews and ratings for a mobile app across platforms.'</li><li>'Analyze sentiment of a tweet and classify it as positive, neutral, or negative.'</li><li>'Retrieve country-wise COVID-19 vaccination rates from an authoritative source.'</li></ul>                                                                                                                          |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9967   |

## 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("cnmoro/prompt-router")
# Run inference
preds = model("Get the stock price history of Tesla for the last month.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 5   | 13.6792 | 38  |

| Label             | Training Sample Count |
|:------------------|:----------------------|
| summarization     | 160                   |
| tool              | 144                   |
| general_knowledge | 154                   |
| roleplay          | 145                   |
| complex_reasoning | 130                   |
| creativity        | 164                   |
| coding            | 152                   |
| basic_reasoning   | 148                   |

### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 16)
- max_steps: 2400
- 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
- evaluation_strategy: steps
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0004 | 1    | 0.1954        | -               |
| 0.0208 | 50   | 0.2125        | -               |
| 0.0417 | 100  | 0.2131        | -               |
| 0.0625 | 150  | 0.2072        | -               |
| 0.0833 | 200  | 0.2029        | 0.1902          |
| 0.1042 | 250  | 0.1925        | -               |
| 0.125  | 300  | 0.1764        | -               |
| 0.1458 | 350  | 0.1512        | -               |
| 0.1667 | 400  | 0.1229        | 0.1072          |
| 0.1875 | 450  | 0.1015        | -               |
| 0.2083 | 500  | 0.0862        | -               |
| 0.2292 | 550  | 0.065         | -               |
| 0.25   | 600  | 0.0505        | 0.0504          |
| 0.2708 | 650  | 0.0532        | -               |
| 0.2917 | 700  | 0.0427        | -               |
| 0.3125 | 750  | 0.0378        | -               |
| 0.3333 | 800  | 0.0357        | 0.0322          |
| 0.3542 | 850  | 0.0286        | -               |
| 0.375  | 900  | 0.0381        | -               |
| 0.3958 | 950  | 0.0333        | -               |
| 0.4167 | 1000 | 0.0307        | 0.0235          |
| 0.4375 | 1050 | 0.0245        | -               |
| 0.4583 | 1100 | 0.0245        | -               |
| 0.4792 | 1150 | 0.0217        | -               |
| 0.5    | 1200 | 0.0193        | 0.0168          |
| 0.5208 | 1250 | 0.0167        | -               |
| 0.5417 | 1300 | 0.0158        | -               |
| 0.5625 | 1350 | 0.02          | -               |
| 0.5833 | 1400 | 0.0167        | 0.0120          |
| 0.6042 | 1450 | 0.0176        | -               |
| 0.625  | 1500 | 0.0159        | -               |
| 0.6458 | 1550 | 0.0141        | -               |
| 0.6667 | 1600 | 0.0131        | 0.0094          |
| 0.6875 | 1650 | 0.0097        | -               |
| 0.7083 | 1700 | 0.0109        | -               |
| 0.7292 | 1750 | 0.0126        | -               |
| 0.75   | 1800 | 0.0115        | 0.0079          |
| 0.7708 | 1850 | 0.0122        | -               |
| 0.7917 | 1900 | 0.0104        | -               |
| 0.8125 | 1950 | 0.0111        | -               |
| 0.8333 | 2000 | 0.011         | 0.0071          |
| 0.8542 | 2050 | 0.0095        | -               |
| 0.875  | 2100 | 0.009         | -               |
| 0.8958 | 2150 | 0.0107        | -               |
| 0.9167 | 2200 | 0.0099        | 0.0067          |
| 0.9375 | 2250 | 0.0084        | -               |
| 0.9583 | 2300 | 0.0086        | -               |
| 0.9792 | 2350 | 0.0089        | -               |
| 1.0    | 2400 | 0.0098        | 0.0066          |

### Framework Versions
- Python: 3.11.11
- SetFit: 1.2.0.dev0
- Sentence Transformers: 4.0.2
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Datasets: 3.5.0
- Tokenizers: 0.21.1

## 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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