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
voicemail
  • 'Your call has been forwarded to an automated voice messaging system. 6 '
  • 'Please leave your message for 8083526996. '
  • "This is Bart Jumper. I'm sorry I missed your call. Please leave your name and number, and I'll return your call as soon as I "
phone_tree
  • 'Thank you for calling Periton. A next '
  • 'Thank you for calling Signifide. Our main number has changed. The new number is eight six six two '
  • 'Thank you for calling Icahn Health and Fitness. If you know the extension you wish to reach, '

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("nikcheerla/amd-full-phonetree-v1")
# Run inference
preds = model("For calling WL Gore and Associates Incorporated. Please wait ")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 14.7789 214
Label Training Sample Count
phone_tree 4979
voicemail 5519

Training Hyperparameters

  • batch_size: (32, 32)
  • num_epochs: (3, 3)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 20
  • 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: True
  • warmup_proportion: 0.1
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: True

Training Results

Epoch Step Training Loss Validation Loss
0.0001 1 0.2196 -
1.0 13123 0.0001 0.1209
2.0 26246 0.0 0.1101
3.0 39369 0.0446 0.1108
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.1
  • Sentence Transformers: 2.2.2
  • Transformers: 4.35.2
  • PyTorch: 2.0.1+cu118
  • Datasets: 2.16.1
  • Tokenizers: 0.15.0

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}
}
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