SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a SetFit model trained on the ayakiri/wolo-app-categories-to-description dataset that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
- Model Type: SetFit
- Sentence Transformer body: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 128 tokens
- Number of Classes: 5 classes
- Training Dataset: ayakiri/wolo-app-categories-to-description
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
Label | Examples |
---|---|
Kultura |
|
Ekologia |
|
Sport |
|
Pomoc |
|
Edukacja |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.9 |
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("ayakiri/wolo-app-categories-setfit-model")
# Run inference
preds = model("Organizacja \"Sport dla Wszystkich\" poszukuje wolontariuszy do programu \"Aktywni Razem\". Inicjatywa ta skierowana jest na promowanie aktywności fizycznej wśród osób z różnymi umiejętnościami. Poszukujemy osób z pasją do sportu, zdolnościami motywacyjnymi oraz chęcią wspierania innych w aktywnym trybie życia. Wolontariusze będą zaangażowani w organizację treningów, wydarzeń sportowych oraz tworzenie przyjaznej atmosfery.")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 19 | 46.8618 | 177 |
Label | Training Sample Count |
---|---|
Edukacja | 29 |
Ekologia | 36 |
Kultura | 25 |
Pomoc | 31 |
Sport | 31 |
Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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 |
---|---|---|---|
0.0013 | 1 | 0.1682 | - |
0.0658 | 50 | 0.0664 | - |
0.1316 | 100 | 0.0306 | - |
0.1974 | 150 | 0.004 | - |
0.2632 | 200 | 0.0169 | - |
0.3289 | 250 | 0.0017 | - |
0.3947 | 300 | 0.0009 | - |
0.4605 | 350 | 0.001 | - |
0.5263 | 400 | 0.0007 | - |
0.5921 | 450 | 0.0004 | - |
0.6579 | 500 | 0.0008 | - |
0.7237 | 550 | 0.0003 | - |
0.7895 | 600 | 0.0002 | - |
0.8553 | 650 | 0.0002 | - |
0.9211 | 700 | 0.0006 | - |
0.9868 | 750 | 0.0007 | - |
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
@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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Dataset used to train ayakiri/wolo-app-categories-setfit-model
Evaluation results
- Accuracy on ayakiri/wolo-app-categories-to-descriptiontest set self-reported0.900