metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-mpnet-base-v2
metrics:
  - accuracy
widget:
  - text: Comment rédiger un bon CV?
  - text: What is the address of Microsoft's headquarters?
  - text: Where is the nearest gas station?
  - text: How to create a mobile application?
  - text: Comment calculer le retour sur investissement (ROI)?
pipeline_tag: text-classification
inference: true
model-index:
  - name: SetFit with sentence-transformers/paraphrase-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-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-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-mpnet-base-v2
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 6 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
very_semantic_queries	'Quels sont les principes fondamentaux du corps humain?' "Comment améliorer l'efficacité énergétique dans les bâtiments?" 'Combien de calories dans une pomme?'
very_lexical	"Quelle est la capitale de l'Italie?" "Qui est l'auteur de '1984'?" 'What is the current unemployment rate in France?'
semantic_queries	"Quels sont les avantages de l'apprentissage machine dans le secteur de la santé?" 'Comment puis-je optimiser les performances de mon site web?' 'What are the main challenges in cybersecurity?'
lexical	'Quel est le numéro de téléphone du service client ou du customer suport?' 'How can I reset my user password?' 'What is the zip code for New York?'
lexical_queries	'Comment fonctionne la blockchain?'
lexical_query	'Who won the Nobel Peace Prize in 2021?'

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("yaniseuranova/setfit-paraphrase-mpnet-base-v2-sst2")
# Run inference
preds = model("Comment rédiger un bon CV?")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	7.1667	13

Label	Training Sample Count
very_semantic_queries	16
semantic_queries	18
lexical_queries	1
very_lexical	15

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (4, 4)
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
seed: 42
eval_max_steps: -1
load_best_model_at_end: True

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0059	1	0.4006	-
0.2941	50	0.1896	-
0.5882	100	0.052	-
0.8824	150	0.0042	-
1.0	170	-	0.0023
1.1765	200	0.0011	-
1.4706	250	0.0006	-
1.7647	300	0.0007	-
2.0	340	-	0.0003
2.0588	350	0.0004	-
2.3529	400	0.0004	-
2.6471	450	0.0004	-
2.9412	500	0.0009	-
3.0	510	-	0.0003
3.2353	550	0.0003	-
3.5294	600	0.0004	-
3.8235	650	0.0003	-
4.0	680	-	0.0002

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 2.6.1
Transformers: 4.39.0
PyTorch: 2.3.0+cu121
Datasets: 2.18.0
Tokenizers: 0.15.2

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}
}