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

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-mpnet-base-v2 as the Sentence Transformer embedding model. A SetFitHead 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("leavoigt/vulnerability_multilabel_updated")
# Run inference
preds = model("Workers in the formal sector. Formal sector workers also face economic risks. A number of them experience income instability due to contractualization, retrenchment, and firm closures. In 2014, contractual workers accounted for 22 percent of the total 4.5 million workers employed in establishments with 20 or more employees.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 21 72.6472 238

Training Hyperparameters

  • batch_size: (16, 2)
  • num_epochs: (1, 0)
  • max_steps: -1
  • sampling_strategy: undersampling
  • 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: 0.01
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0006 1 0.1906 -
0.0316 50 0.1275 0.1394
0.0631 100 0.0851 0.1247
0.0947 150 0.0959 0.1269
0.1263 200 0.1109 0.1179
0.1578 250 0.0923 0.1354
0.1894 300 0.063 0.1292
0.2210 350 0.0555 0.1326
0.2525 400 0.0362 0.1127
0.2841 450 0.0582 0.132
0.3157 500 0.0952 0.1339
0.3472 550 0.0793 0.1171
0.3788 600 0.059 0.1187
0.4104 650 0.0373 0.1131
0.4419 700 0.0593 0.1144
0.4735 750 0.0405 0.1174
0.5051 800 0.0284 0.1196
0.5366 850 0.0329 0.1116
0.5682 900 0.0895 0.1193
0.5997 950 0.0576 0.1159
0.6313 1000 0.0385 0.1203
0.6629 1050 0.0842 0.1195
0.6944 1100 0.0274 0.113
0.7260 1150 0.0226 0.1137
0.7576 1200 0.0276 0.1204
0.7891 1250 0.0355 0.1163
0.8207 1300 0.077 0.1161
0.8523 1350 0.0735 0.1135
0.8838 1400 0.0357 0.1175
0.9154 1450 0.0313 0.1207
0.9470 1500 0.0241 0.1159
0.9785 1550 0.0339 0.1161

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 2.3.1
  • Transformers: 4.38.1
  • PyTorch: 2.1.0+cu121
  • Datasets: 2.3.0
  • 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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