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ANLP_kaggle

Text Classification
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sentence-transformers
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generated_from_setfit_trainer
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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:

  1. Fine-tuning a Sentence Transformer with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Sources

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: (10, 10)
  • 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 0.2748 -
0.0727 50 0.2537 -
0.1453 100 0.1734 -
0.2180 150 0.1086 -
0.2907 200 0.062 -
0.3634 250 0.046 -
0.4360 300 0.017 -
0.5087 350 0.0104 -
0.5814 400 0.006 -
0.6541 450 0.0021 -
0.7267 500 0.0052 -
0.7994 550 0.0045 -
0.8721 600 0.0012 -
0.9448 650 0.0007 -
1.0174 700 0.0006 -
1.0901 750 0.0006 -
1.1628 800 0.0006 -
1.2355 850 0.0005 -
1.3081 900 0.0004 -
1.3808 950 0.0003 -
1.4535 1000 0.0004 -
1.5262 1050 0.0004 -
1.5988 1100 0.0004 -
1.6715 1150 0.0003 -
1.7442 1200 0.0002 -
1.8169 1250 0.0002 -
1.8895 1300 0.0005 -
1.9622 1350 0.0004 -
2.0349 1400 0.0002 -
2.1076 1450 0.0004 -
2.1802 1500 0.0002 -
2.2529 1550 0.0002 -
2.3256 1600 0.0004 -
2.3983 1650 0.0002 -
2.4709 1700 0.0002 -
2.5436 1750 0.0002 -
2.6163 1800 0.0002 -
2.6890 1850 0.0002 -
2.7616 1900 0.0003 -
2.8343 1950 0.0001 -
2.9070 2000 0.0002 -
2.9797 2050 0.0002 -
3.0523 2100 0.0003 -
3.125 2150 0.0002 -
3.1977 2200 0.0002 -
3.2703 2250 0.0001 -
3.3430 2300 0.0002 -
3.4157 2350 0.0002 -
3.4884 2400 0.0002 -
3.5610 2450 0.0001 -
3.6337 2500 0.0001 -
3.7064 2550 0.0001 -
3.7791 2600 0.0001 -
3.8517 2650 0.0001 -
3.9244 2700 0.0001 -
3.9971 2750 0.0001 -
4.0698 2800 0.0001 -
4.1424 2850 0.0001 -
4.2151 2900 0.0001 -
4.2878 2950 0.0001 -
4.3605 3000 0.0001 -
4.4331 3050 0.0001 -
4.5058 3100 0.0001 -
4.5785 3150 0.0001 -
4.6512 3200 0.0001 -
4.7238 3250 0.0001 -
4.7965 3300 0.0001 -
4.8692 3350 0.0001 -
4.9419 3400 0.0001 -
5.0145 3450 0.0001 -
5.0872 3500 0.0001 -
5.1599 3550 0.0001 -
5.2326 3600 0.0001 -
5.3052 3650 0.0001 -
5.3779 3700 0.0001 -
5.4506 3750 0.0001 -
5.5233 3800 0.0001 -
5.5959 3850 0.0001 -
5.6686 3900 0.0001 -
5.7413 3950 0.0001 -
5.8140 4000 0.0001 -
5.8866 4050 0.0001 -
5.9593 4100 0.0001 -
6.0320 4150 0.0001 -
6.1047 4200 0.0001 -
6.1773 4250 0.0001 -
6.25 4300 0.0001 -
6.3227 4350 0.0001 -
6.3953 4400 0.0001 -
6.4680 4450 0.0001 -
6.5407 4500 0.0001 -
6.6134 4550 0.0001 -
6.6860 4600 0.0001 -
6.7587 4650 0.0001 -
6.8314 4700 0.0001 -
6.9041 4750 0.0001 -
6.9767 4800 0.0 -
7.0494 4850 0.0001 -
7.1221 4900 0.0001 -
7.1948 4950 0.0001 -
7.2674 5000 0.0001 -
7.3401 5050 0.0001 -
7.4128 5100 0.0001 -
7.4855 5150 0.0001 -
7.5581 5200 0.0001 -
7.6308 5250 0.0001 -
7.7035 5300 0.0001 -
7.7762 5350 0.0001 -
7.8488 5400 0.0001 -
7.9215 5450 0.0001 -
7.9942 5500 0.0 -
8.0669 5550 0.0001 -
8.1395 5600 0.0001 -
8.2122 5650 0.0001 -
8.2849 5700 0.0 -
8.3576 5750 0.0001 -
8.4302 5800 0.0001 -
8.5029 5850 0.0001 -
8.5756 5900 0.0001 -
8.6483 5950 0.0001 -
8.7209 6000 0.0001 -
8.7936 6050 0.0001 -
8.8663 6100 0.0 -
8.9390 6150 0.0 -
9.0116 6200 0.0001 -
9.0843 6250 0.0001 -
9.1570 6300 0.0 -
9.2297 6350 0.0 -
9.3023 6400 0.0 -
9.375 6450 0.0001 -
9.4477 6500 0.0001 -
9.5203 6550 0.0001 -
9.5930 6600 0.0001 -
9.6657 6650 0.0001 -
9.7384 6700 0.0001 -
9.8110 6750 0.0001 -
9.8837 6800 0.0001 -
9.9564 6850 0.0 -

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}
}