Add SetFit model

052e1bf verified 9 months ago

7.57 kB

tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: Ciboire de sacréfice de verrat de colon
  - text: Verrat de cibolac d'estique de cibouleau
  - text: esti
  - text: Câlique de cossin
  - text: estique!
metrics:
  - accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
model-index:
  - name: SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 1
            name: Accuracy

SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-mpnet-base-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-mpnet-base-v2
Classification head: a LogisticRegression instance
Maximum Sequence Length: 128 tokens
Number of Classes: 5 classes

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
sacre composé doux	'Saint-cimonaque de sainte-viarge' 'Cibolac de cibouleau' 'câline de bines!'
sacre ponctuation intense	'sacrament!' 'siboire!' 'câlisse!'
sacre composé intense	'Ciboire de ciarge' "ciboire de viarge de bout d'crisse" "sacrement d'tarbarnak de câlisse"
sacre ponctuation doux	'tabarouette!' 'cibole' 'baptême'
"sacre ponctuation intense"	'sti!'

Evaluation

Metrics

Label	Accuracy
all	1.0

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("POBonin/setfit-quebec-profanity-classifier")
# Run inference
preds = model("esti")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	2.2653	6

Label	Training Sample Count
sacre ponctuation intense	12
sacre ponctuation doux	12
sacre composé intense	12
sacre composé doux	12

Training Hyperparameters

batch_size: (32, 32)
num_epochs: (20, 20)
max_steps: -1
sampling_strategy: oversampling
body_learning_rate: (2e-05, 1e-05)
head_learning_rate: 0.0001
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: True

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0175	1	0.2373	-
0.8772	50	0.2157	0.0794
1.7544	100	0.0818	0.0061
2.6316	150	0.0014	0.0069
3.5088	200	0.0004	0.0086
4.3860	250	0.0003	0.0057
5.2632	300	0.0003	0.0103
6.1404	350	0.0002	0.0092
7.0175	400	0.0002	0.0169

Framework Versions

Python: 3.12.10
SetFit: 1.1.2
Sentence Transformers: 4.1.0
Transformers: 4.51.3
PyTorch: 2.6.0+cu126
Datasets: 2.19.1
Tokenizers: 0.21.1

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}
}