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setfit-engagement-model-basic

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
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setfit-engagement-model-basic / README.md
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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      What's your favorite way to learn? Through books, videos, or experiments?
      Experiments. I like seeing science in action.
  - text: >-
      Can you name a living organism's basic needs? Food, water... Can we change
      the subject?
  - text: >-
      What do you find fascinating about the human body? That our brain works
      like a supercomputer.
  - text: >-
      What's something you learned about in technology? We learned about coding.
      I made a simple game.
  - text: Do you know how to code? Nope. Sounds complicated.
pipeline_tag: text-classification
inference: true
base_model: BAAI/bge-small-en-v1.5

SetFit with BAAI/bge-small-en-v1.5

This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-small-en-v1.5 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
negative
  • 'What did you learn in school today? Nothing much, just the usual stuff.'
  • "Do you know the capital of France? Don't know, don't care."
  • "Can you tell me what 2 + 2 equals? Guess it's 4, but why does it matter?"
positive
  • "What's your favorite subject? Science, because I love experiments."
  • 'Can you tell me the planets in order? Sure, Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune. Pluto used to be one, but not anymore.'
  • "Do you enjoy math class? Yeah, it's cool, especially when we do geometry."

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("bew/setfit-engagement-model-basic")
# Run inference
preds = model("Do you know how to code? Nope. Sounds complicated.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 6 15.0470 26
Label Training Sample Count
negative 79
positive 70

Training Hyperparameters

  • batch_size: (32, 32)
  • num_epochs: (10, 10)
  • 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: False

Training Results

Epoch Step Training Loss Validation Loss
0.0028 1 0.2418 -
0.1416 50 0.2311 -
0.2833 100 0.2425 -
0.4249 150 0.0572 -
0.5666 200 0.0049 -
0.7082 250 0.0031 -
0.8499 300 0.0019 -
0.9915 350 0.0018 -
1.1331 400 0.0015 -
1.2748 450 0.001 -
1.4164 500 0.0011 -
1.5581 550 0.0008 -
1.6997 600 0.0008 -
1.8414 650 0.0007 -
1.9830 700 0.0008 -
2.1246 750 0.0007 -
2.2663 800 0.0005 -
2.4079 850 0.0006 -
2.5496 900 0.0005 -
2.6912 950 0.0005 -
2.8329 1000 0.0005 -
2.9745 1050 0.0005 -
3.1161 1100 0.0005 -
3.2578 1150 0.0005 -
3.3994 1200 0.0004 -
3.5411 1250 0.0004 -
3.6827 1300 0.0004 -
3.8244 1350 0.0004 -
3.9660 1400 0.0004 -
4.1076 1450 0.0004 -
4.2493 1500 0.0003 -
4.3909 1550 0.0004 -
4.5326 1600 0.0004 -
4.6742 1650 0.0003 -
4.8159 1700 0.0003 -
4.9575 1750 0.0004 -
5.0992 1800 0.0003 -
5.2408 1850 0.0003 -
5.3824 1900 0.0003 -
5.5241 1950 0.0003 -
5.6657 2000 0.0003 -
5.8074 2050 0.0003 -
5.9490 2100 0.0003 -
6.0907 2150 0.0003 -
6.2323 2200 0.0003 -
6.3739 2250 0.0003 -
6.5156 2300 0.0003 -
6.6572 2350 0.0003 -
6.7989 2400 0.0002 -
6.9405 2450 0.0003 -
7.0822 2500 0.0003 -
7.2238 2550 0.0003 -
7.3654 2600 0.0003 -
7.5071 2650 0.0003 -
7.6487 2700 0.0003 -
7.7904 2750 0.0003 -
7.9320 2800 0.0003 -
8.0737 2850 0.0003 -
8.2153 2900 0.0003 -
8.3569 2950 0.0003 -
8.4986 3000 0.0002 -
8.6402 3050 0.0003 -
8.7819 3100 0.0003 -
8.9235 3150 0.0003 -
9.0652 3200 0.0003 -
9.2068 3250 0.0002 -
9.3484 3300 0.0003 -
9.4901 3350 0.0002 -
9.6317 3400 0.0003 -
9.7734 3450 0.0003 -
9.9150 3500 0.0002 -

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: 0.15.2

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}
}