---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: kinit/slovakbert-sentiment-twitter
metrics:
- accuracy
widget:
- text: Pan Vilasek bol velmi mily, snaizl sa spestrit vyucbu zaujimavymi aktivitami.
    a zaroven sa snazil nam vstiepit nieco z nemciny. skripta z ktorych sa ucime by
    vsak mohli byt kusok zlozitejsie, slovna zasoba je vecsinou trivialna...
- text: Predmet na ktorom sa skvelo naucite zaklady html a css a celkovo webdizajnu,
    dobre prednasky a cvicenia kde si to precvicite
- text: Super zostavena prednaska, veci pre mna zaujimave, lebo viem ze sa celkom
    pouzivaju, vyborne vysvetlovane, tempo na mna tak akurat - vela sa stihlo (clovek
    sa nenudil) ale na druhej strane sa aj dalo stihat ak si to clovek aspon raz za
    cas pozrel. Co som pocul tak niektori si stazovali na narocnost vykladu, ale myslim
    ze to je skor tym, ze ked sa na to niekto ani raz nepozrie nemoze cakat ze vsetko
    hned na prve pocutie pochopi. este raz - super
- text: potešili by ma praktické ukážky namiesto viacnásobných odvodení podobných
    vecí
- text: Veľmi zaujímavé, zrozumiteľne podané a niekedy aj vtipné:)
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with kinit/slovakbert-sentiment-twitter
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.6830314585319351
      name: Accuracy
---

# SetFit with kinit/slovakbert-sentiment-twitter

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [kinit/slovakbert-sentiment-twitter](https://huggingface.co/kinit/slovakbert-sentiment-twitter) 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:** [kinit/slovakbert-sentiment-twitter](https://huggingface.co/kinit/slovakbert-sentiment-twitter)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 514 tokens
- **Number of Classes:** 3 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
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### 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>'Predmet bol fajn, zaujimavy, akurat bolo malo prilezitosti ziskat body za prezentacie.'</li><li>'Great.'</li><li>'Zaujímavý, konceptuálne náročný predmet. Objavuje sa tu všeličo z iných predmetov. Najskôr sa preberie záver a zvyšok semestra sa študenti k tomu dopracovávajú. Predmet nie je rozdelený na prednášky a cvičenia. Na hodinách však musia študenti veľa pracovať, často treba niečo odvodiť overiť alebo vypočitať príklad a tiež sa diskutuje. Tento spôsob učenia považujem za účinný - z prednášok som odchádzal s tým, že som sa niečo nové naučil a rozumiem tomu. K dispozícii sú skriptá, ktoré pomôžu vyjasniť prípadné nezrovnalosti a pri učení sa na skúšku.'</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
| -1    | <ul><li>'Bolo by dobré, keby sa zadávali témy prác pred letnými prázdninami. Tento semester bol dosť náročný, takže sme nemali  čas pracovať na bakalárkach.'</li><li>'Na začiatku semestra vynechať trochu s počtu hodín venovaným OS Linuxu a práci s jeho konzolou.'</li><li>'Zaujimavy predmet,dobre prednasany, kvalitne skripta, no chybaju mu cvicenia.'</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
| 1     | <ul><li>'Predmet bol narocny, ale bez dobrovolnych cviceni z algebry by bol podla mna kazdy student este viac zmateny, nepochopil by uplne prebranu temu.'</li><li>'Pravidla na praktickych cviceniach sa stale menili. Najprv boli cvicenia povinne, zrazu boli dobrovolne, niekolkokrat sa menil system odovzdavania uloh, nakoniec ani ten povinny projekt nebol uz povinny, ked som ho zacal kodit. Prakticke cvicenia su strata casu, urcene pre ludi co "kodia" stylom opisem to z tabule. Clovek co vie programovat (co by 4taci mali vediet vsetci) to zvladne v pohode aj bez nich.'</li><li>'Prvy problem nastal takmer okamzite, ked sa na zaciatku menili pravidla. Po dovysvetlovani podmienok sa ustalili a (az na praktocke cvicenia) nemenili. Nastastie ak sa najde nieco, comu nepochopite na prednaske, na cviceniach vam to ochotne vysvetlia. Hlavnym problemom tohto predmetu je extremna casova narocnost vzhladom na pocet kreditov. Z pociatku boli pivinne 2*2h cvicenia tyzdenne + prednaska, pricom cvicenia museli byt so 100% ucastou. Tiez nepotesi ze okrem midtermu a final testu, ide student este na jednu pisomnu a ustnu cast....'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.6830   |

## 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("pEpOo/setfit-model-24-3")
# Run inference
preds = model("Veľmi zaujímavé, zrozumiteľne podané a niekedy aj vtipné:)")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 41.9167 | 128 |

| Label | Training Sample Count |
|:------|:----------------------|
| -1    | 8                     |
| 0     | 8                     |
| 1     | 0                     |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (10, 10)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0333 | 1    | 0.3101        | -               |
| 1.6667 | 50   | 0.0032        | -               |
| 3.3333 | 100  | 0.0012        | -               |
| 5.0    | 150  | 0.0004        | -               |
| 6.6667 | 200  | 0.0002        | -               |
| 8.3333 | 250  | 0.0003        | -               |
| 10.0   | 300  | 0.0002        | -               |

### Framework Versions
- Python: 3.11.0
- SetFit: 1.0.3
- Sentence Transformers: 2.5.1
- Transformers: 4.38.2
- PyTorch: 2.2.1+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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