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dim2_Qwen_setfit_model

Text Classification
setfit
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sentence-transformers
qwen3
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dim2_Qwen_setfit_model / README.md
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metadata
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: >-
      For example, there is no better entartainment for group of friends than
      visiting sport games and matches.
  - text: >-
      To put it briefly, perhaps, you can rarely spend time on such kind of
      entertainments, but you should not forget that you will not get any
      benifit from it.
  - text: ' Watching sports helps people to develop their social life.'
  - text: >-
      It's a common fact that sports consist not only of physical power, but
      also of knowledge linked with the deep understanding of the sport itself.
  - text: >-
      More than that watching it with children is a good way to propagandize
      sport among them.
metrics:
  - accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: Qwen/Qwen3-Embedding-0.6B
model-index:
  - name: SetFit with Qwen/Qwen3-Embedding-0.6B
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.7959183673469388
            name: Accuracy

SetFit with Qwen/Qwen3-Embedding-0.6B

This is a SetFit model that can be used for Text Classification. This SetFit model uses Qwen/Qwen3-Embedding-0.6B 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
L
  • 'So it will be possible for you to monitise your expertize on an sport market.'
  • 'Moreover, observing such occasions is also an excellent wat to liven up your holidays and to get new feelings and knowledge about the body.'
  • 'i claim that it brings you, your family and friends closer.'
H
  • "There is an opinion that watching sports is time consuming and is not an efficient way to spend one's free time."
  • 'It develops a logical thinking and concentration.'
  • 'But in my opinion, watching sports competition can be a good and useful enough way of relax for people who enjoy it.'

Evaluation

Metrics

Label Accuracy
all 0.7959

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("Zlovoblachko/dim2_Qwen_setfit_model")
# Run inference
preds = model(" Watching sports helps people to develop their social life.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 4 18.0633 48
Label Training Sample Count
L 150
H 150

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (1, 1)
  • 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
  • l2_weight: 0.01
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0004 1 0.2694 -
0.0177 50 0.2589 -
0.0353 100 0.2489 -
0.0530 150 0.1486 -
0.0706 200 0.0375 -
0.0883 250 0.0014 -
0.1059 300 0.0 -
0.1236 350 0.0 -
0.1412 400 0.0 -
0.1589 450 0.0 -
0.1766 500 0.0 -
0.1942 550 0.0 -
0.2119 600 0.0 -
0.2295 650 0.0 -
0.2472 700 0.0 -
0.2648 750 0.0 -
0.2825 800 0.0 -
0.3001 850 0.0 -
0.3178 900 0.0 -
0.3355 950 0.0 -
0.3531 1000 0.0 -
0.3708 1050 0.0 -
0.3884 1100 0.0 -
0.4061 1150 0.0 -
0.4237 1200 0.0 -
0.4414 1250 0.0 -
0.4590 1300 0.0 -
0.4767 1350 0.0 -
0.4944 1400 0.0 -
0.5120 1450 0.0 -
0.5297 1500 0.0 -
0.5473 1550 0.0 -
0.5650 1600 0.0 -
0.5826 1650 0.0 -
0.6003 1700 0.0 -
0.6179 1750 0.0 -
0.6356 1800 0.0 -
0.6532 1850 0.0 -
0.6709 1900 0.0 -
0.6886 1950 0.0 -
0.7062 2000 0.0 -
0.7239 2050 0.0 -
0.7415 2100 0.0 -
0.7592 2150 0.0 -
0.7768 2200 0.0 -
0.7945 2250 0.0 -
0.8121 2300 0.0 -
0.8298 2350 0.0 -
0.8475 2400 0.0 -
0.8651 2450 0.0 -
0.8828 2500 0.0 -
0.9004 2550 0.0 -
0.9181 2600 0.0 -
0.9357 2650 0.0 -
0.9534 2700 0.0 -
0.9710 2750 0.0 -
0.9887 2800 0.0 -

Framework Versions

  • Python: 3.11.13
  • SetFit: 1.1.3
  • Sentence Transformers: 5.0.0
  • Transformers: 4.55.0
  • PyTorch: 2.6.0+cu124
  • Datasets: 4.0.0
  • Tokenizers: 0.21.4

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}
}