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word-classification

Text Classification
setfit
ONNX
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
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word-classification / README.md
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metadata
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: Enable audio recorder app
  - text: Open video camera mode
  - text: Show recent chats
  - text: Switch to instant camera usage mode
  - text: Could you switch to video camera mode?
inference: true
model-index:
  - name: SetFit with sentence-transformers/all-MiniLM-L6-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 1
            name: Accuracy

SetFit with sentence-transformers/all-MiniLM-L6-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-MiniLM-L6-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
microphone
  • 'Launch microphone app'
  • 'Launch recording app'
  • 'Access mic app'
history
  • 'View chat logs'
  • 'Display conversation details'
  • 'Show history'
camera
  • 'Switch to webcam mode please'
  • 'Could you switch to video camera mode?'
  • 'Open the photo webcam'

Evaluation

Metrics

Label Accuracy
all 1.0

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("porxelek/word-classification")
# Run inference
preds = model("Show recent chats")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 2 4.1364 10
Label Training Sample Count
camera 250
history 150
microphone 150

Training Hyperparameters

  • batch_size: (64, 64)
  • 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
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: True

Training Results

Epoch Step Training Loss Validation Loss
0.0003 1 0.1209 -
0.0164 50 0.1449 -
0.0328 100 0.046 -
0.0492 150 0.0099 -
0.0656 200 0.0049 -
0.0820 250 0.0036 -
0.0985 300 0.0022 -
0.1149 350 0.0015 -
0.1313 400 0.0011 -
0.1477 450 0.001 -
0.1641 500 0.0009 -
0.1805 550 0.0009 -
0.1969 600 0.0009 -
0.2133 650 0.0008 -
0.2297 700 0.0007 -
0.2461 750 0.0006 -
0.2626 800 0.0006 -
0.2790 850 0.0006 -
0.2954 900 0.0006 -
0.3118 950 0.0005 -
0.3282 1000 0.0004 -
0.3446 1050 0.0005 -
0.3610 1100 0.0005 -
0.3774 1150 0.0004 -
0.3938 1200 0.0004 -
0.4102 1250 0.0004 -
0.4266 1300 0.0005 -
0.4431 1350 0.0004 -
0.4595 1400 0.0003 -
0.4759 1450 0.0003 -
0.4923 1500 0.0003 -
0.5087 1550 0.0003 -
0.5251 1600 0.0003 -
0.5415 1650 0.0003 -
0.5579 1700 0.0003 -
0.5743 1750 0.0003 -
0.5907 1800 0.0003 -
0.6072 1850 0.0002 -
0.6236 1900 0.0003 -
0.6400 1950 0.0002 -
0.6564 2000 0.0002 -
0.6728 2050 0.0002 -
0.6892 2100 0.0003 -
0.7056 2150 0.0002 -
0.7220 2200 0.0002 -
0.7384 2250 0.0002 -
0.7548 2300 0.0002 -
0.7713 2350 0.0002 -
0.7877 2400 0.0002 -
0.8041 2450 0.0002 -
0.8205 2500 0.0002 -
0.8369 2550 0.0002 -
0.8533 2600 0.0002 -
0.8697 2650 0.0002 -
0.8861 2700 0.0002 -
0.9025 2750 0.0002 -
0.9189 2800 0.0002 -
0.9353 2850 0.0002 -
0.9518 2900 0.0002 -
0.9682 2950 0.0002 -
0.9846 3000 0.0002 -
1.0 3047 - 0.0
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 3.0.1
  • Transformers: 4.39.0
  • PyTorch: 2.3.1+cu121
  • Datasets: 2.20.0
  • 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}
}