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

This is a SetFit model trained on the Kevinger/hub-report-dataset dataset that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-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

Evaluation

Metrics

Label Accuracy
all 0.6529

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("Kevinger/setfit-hub-multilabel-example")
# Run inference
preds = model("LEVERETT — Dakin Humane Society announced Wednesday that it has sold its former animal shelter at 63 Montague Road in Leverett to Better Together Dog Rescue.

The news release didn’t include a sales price for the 3,480-square-foot building on 5 acres of land.

But records at the Franklin County Registry of Deeds show the sale was for $575,000.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 53 386.3906 2161

Training Hyperparameters

  • batch_size: (8, 8)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 75
  • 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
0.0008 1 0.1304 -
0.0417 50 0.1596 -
0.0833 100 0.132 -
0.125 150 0.0064 -
0.1667 200 0.0017 -
0.2083 250 0.0004 -
0.25 300 0.0001 -
0.2917 350 0.0002 -
0.3333 400 0.0003 -
0.375 450 0.0002 -
0.4167 500 0.0001 -
0.4583 550 0.0002 -
0.5 600 0.0002 -
0.5417 650 0.0002 -
0.5833 700 0.0001 -
0.625 750 0.0001 -
0.6667 800 0.0001 -
0.7083 850 0.0001 -
0.75 900 0.0 -
0.7917 950 0.0001 -
0.8333 1000 0.0001 -
0.875 1050 0.0001 -
0.9167 1100 0.0001 -
0.9583 1150 0.0 -
1.0 1200 0.0001 -

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}
}
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Model size
109M params
Tensor type
F32
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Inference Examples
Inference API (serverless) has been turned off for this model.

Finetuned from

Dataset used to train Kevinger/setfit-hub-multilabel-example

Evaluation results