metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      The development of smart cities is leveraging technology to improve urban
      living conditions.
  - text: Climate change is causing a significant rise in sea levels.
  - text: >-
      Fans are speculating about the plot of the upcoming season of Stranger
      Things.
  - text: >-
      Fashion branding and marketing campaigns shape consumer perceptions and
      influence purchasing decisions.
  - text: >-
      Volunteering abroad provides a unique opportunity to experience different
      cultures while giving back to society.
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2

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: 12 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
Politics	'The mayor announced a new initiative to improve public transportation.' 'The senator is facing criticism for her stance on the recent bill.' 'The upcoming election has sparked intense debates among the candidates.'
Health	'Regular exercise and a balanced diet are key to maintaining good health.' 'The World Health Organization has issued new guidelines on COVID-19.' 'A new study reveals the benefits of meditation for mental health.'
Finance	'The stock market saw a significant drop following the announcement.' 'Investing in real estate can be a profitable venture if done correctly.' "The company's profits have doubled since the launch of their new product."
Travel	'Visiting the Grand Canyon is a breathtaking experience.' 'The tourism industry has been severely impacted by the pandemic.' 'Backpacking through Europe is a popular choice for young travelers.'
Food	'The new restaurant in town offers a fusion of Italian and Japanese cuisine.' 'Drinking eight glasses of water a day is essential for staying hydrated.' 'Cooking classes are a fun way to learn new recipes and techniques.'
Education	'The school district is implementing a new curriculum for the upcoming year.' 'Online learning has become increasingly popular during the pandemic.' 'The university is offering scholarships for students in financial need.'
Environment	'Climate change is causing a significant rise in sea levels.' 'Recycling and composting are effective ways to reduce waste.' 'The Amazon rainforest is home to millions of unique species.'
Fashion	'The new fashion trend is all about sustainability and eco-friendly materials.' 'The annual Met Gala is a major event in the fashion world.' 'Vintage clothing has made a comeback in recent years.'
Science	"NASA's Mars Rover has made significant discoveries about the red planet." 'The Nobel Prize in Physics was awarded for breakthroughs in black hole research.' 'Genetic engineering is opening up new possibilities in medical treatment.'
Sports	'The NBA Finals are set to begin next week with the top two teams in the league.' 'Serena Williams continues to dominate the tennis world with her powerful serve.' 'The World Cup is the most prestigious tournament in international soccer.'
Technology	'Artificial intelligence is changing the way we live and work.' 'The latest iPhone has a number of exciting new features.' 'Cybersecurity is becoming increasingly important as more and more data moves online.'
Entertainment	'The new Marvel movie is breaking box office records.' 'The Grammy Awards are a celebration of the best music of the year.' 'The latest season of Game of Thrones had fans on the edge of their seats.'

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("EmeraldMP/ANLP_kaggle")
# Run inference
preds = model("Climate change is causing a significant rise in sea levels.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	7	12.8073	24

Label	Training Sample Count
Education	23
Entertainment	23
Environment	23
Fashion	23
Finance	23
Food	23
Health	23
Politics	22
Science	23
Sports	23
Technology	23
Travel	23

Training Hyperparameters

batch_size: (16, 16)
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
0.0015	1	0.2839	-
0.0727	50	0.1245	-
0.1453	100	0.1334	-
0.2180	150	0.0142	-
0.2907	200	0.0046	-
0.3634	250	0.0024	-
0.4360	300	0.0019	-
0.5087	350	0.0011	-
0.5814	400	0.0013	-
0.6541	450	0.0007	-
0.7267	500	0.0011	-
0.7994	550	0.001	-
0.8721	600	0.001	-
0.9448	650	0.0004	-

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 2.7.0
Transformers: 4.38.2
PyTorch: 2.2.1+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}
}