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bhaskars113
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toyota-paint-attribute-1.1

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
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toyota-paint-attribute-1.1 / README.md
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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
base_model: sentence-transformers/all-mpnet-base-v2
datasets:
  - bhaskars113/toyota-paint-attributes
metrics:
  - accuracy
widget:
  - text: >-
      Hey guys, I'm buying a 2004 Mach 1 Mustang and I'm super excited! It's in
      great condition and has only had one owner. Only thing is the grill
      mustang ornament was stolen years ago he said and he never bothered to
      replace it. After searching online I cannot find anything that's at least
      a reliable source. I am in Canada by the way. If anyone knows how to
      search one down I would be very appreciative! Thanks!
  - text: >-
      Mine is actually gold! I think the official paint name is harvest gold.
      It's nice but I'd rather something like the two-tone paints of the 2nd
      gen. The dull metallic gold reminds me of boring grey old corollas lol
  - text: >-
      Arrgh. Click to expand... Welcome to owning a Jeep/Dodge product. in
      150,000km of ownership of our Jeep, we have replaced everything in the
      suspension 2 times, throttle body, 3 sets of plugs, various electrical
      things, stereo pooped the bed, I could go on and on. The most reliable
      dodge/jeep product I owned was my 2011 Wrangler Once I removed all the
      dumb design features jeep put there, like freaking plastic in the ball
      joints. Move to another brand and be MUCH happier. We have 179k on our
      Ford F150 5.0 and all that's been replaced is one set of plugs and one
      ball joint.
  - text: >-
      The car is from Utah and garage kept, so the paint is still in very good
      condition
  - text: >-
      I've seen wonders done by a good paintless dent repair professional. The
      right person with the right tools could make this look brand new, or at
      least better than slightly mismatched paint.
pipeline_tag: text-classification
inference: false

SetFit with sentence-transformers/all-mpnet-base-v2

This is a SetFit model trained on the bhaskars113/toyota-paint-attributes dataset that can be used for Text Classification. This SetFit model uses sentence-transformers/all-mpnet-base-v2 as the Sentence Transformer embedding model. A OneVsRestClassifier 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

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("bhaskars113/toyota-paint-attribute-1.1")
# Run inference
preds = model("The car is from Utah and garage kept, so the paint is still in very good condition")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 5 33.8098 155

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
0.0004 1 0.1664 -
0.0196 50 0.2377 -
0.0392 100 0.1178 -
0.0588 150 0.0577 -
0.0784 200 0.0163 -
0.0980 250 0.0265 -
0.1176 300 0.0867 -
0.1373 350 0.0181 -
0.1569 400 0.0153 -
0.1765 450 0.0411 -
0.1961 500 0.0308 -
0.2157 550 0.0258 -
0.2353 600 0.0062 -
0.2549 650 0.0036 -
0.2745 700 0.0087 -
0.2941 750 0.0025 -
0.3137 800 0.004 -
0.3333 850 0.0025 -
0.3529 900 0.0044 -
0.3725 950 0.0031 -
0.3922 1000 0.0018 -
0.4118 1050 0.0046 -
0.4314 1100 0.0013 -
0.4510 1150 0.0014 -
0.4706 1200 0.002 -
0.4902 1250 0.0015 -
0.5098 1300 0.0039 -
0.5294 1350 0.0019 -
0.5490 1400 0.0011 -
0.5686 1450 0.0008 -
0.5882 1500 0.0015 -
0.6078 1550 0.0012 -
0.6275 1600 0.0011 -
0.6471 1650 0.0008 -
0.6667 1700 0.0016 -
0.6863 1750 0.0009 -
0.7059 1800 0.0008 -
0.7255 1850 0.0008 -
0.7451 1900 0.0008 -
0.7647 1950 0.0011 -
0.7843 2000 0.0008 -
0.8039 2050 0.001 -
0.8235 2100 0.001 -
0.8431 2150 0.0009 -
0.8627 2200 0.0067 -
0.8824 2250 0.0008 -
0.9020 2300 0.0009 -
0.9216 2350 0.0009 -
0.9412 2400 0.0007 -
0.9608 2450 0.0006 -
0.9804 2500 0.0007 -
1.0 2550 0.0006 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 2.7.0
  • Transformers: 4.40.2
  • PyTorch: 2.2.1+cu121
  • Datasets: 2.19.1
  • Tokenizers: 0.19.1

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}
}