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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
NONE
  • 'How do I learn to play the guitar?'
  • "What's the longest river in the world?"
  • 'How do I overcome procrastination?'
KUBIE
  • 'What logs should I check to identify container crashes in the qa-soc-svcs namespace?'
  • 'Can you suggest ways to troubleshoot an image pull error in the "kube-public" namespace?'
  • "I'm encountering errors with a pod in the sandbox-6 namespace. Any suggestions on how to debug it?"
aws_iam
  • 'Show me the IAM role details including attached policies.'
  • 'Show me the IAM roles that have the "admin" prefix.'
  • 'How can I get detailed information about a particular IAM role?'
DOC
  • 'How to access ArgoCD on Production?'
  • 'How to run terraform in CDO?'
  • 'How to push images to dockerhub.cisco.com?'
access_management
  • 'Access to prod-aws infrastructure is required urgently for a deployment.'
  • 'Could you provide me access to the dev-aws resources?'
  • 'I require access to the prod-sagemaker instance for machine learning experiments.'

Evaluation

Metrics

Label Accuracy
all 0.9962

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("setfit_model_id")
# Run inference
preds = model("How can I reduce stress?")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 8.5408 17
Label Training Sample Count
aws_iam 20
access_management 20
DOC 18
KUBIE 20
NONE 20

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (4, 4)
  • 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
0.0021 1 0.2675 -
0.1042 50 0.1143 -
0.2083 100 0.0578 -
0.3125 150 0.0028 -
0.4167 200 0.0032 -
0.5208 250 0.0007 -
0.625 300 0.0006 -
0.7292 350 0.0004 -
0.8333 400 0.0005 -
0.9375 450 0.0006 -
1.0 480 - 0.0027
1.0417 500 0.0004 -
1.1458 550 0.0002 -
1.25 600 0.0003 -
1.3542 650 0.0002 -
1.4583 700 0.0002 -
1.5625 750 0.0002 -
1.6667 800 0.0002 -
1.7708 850 0.0002 -
1.875 900 0.0002 -
1.9792 950 0.0001 -
2.0 960 - 0.0032
2.0833 1000 0.0001 -
2.1875 1050 0.0002 -
2.2917 1100 0.0001 -
2.3958 1150 0.0002 -
2.5 1200 0.0002 -
2.6042 1250 0.0001 -
2.7083 1300 0.0002 -
2.8125 1350 0.0001 -
2.9167 1400 0.0001 -
3.0 1440 - 0.004
3.0208 1450 0.0001 -
3.125 1500 0.0001 -
3.2292 1550 0.0002 -
3.3333 1600 0.0002 -
3.4375 1650 0.0001 -
3.5417 1700 0.0002 -
3.6458 1750 0.0001 -
3.75 1800 0.0001 -
3.8542 1850 0.0001 -
3.9583 1900 0.0002 -
4.0 1920 - 0.0037
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.9.6
  • SetFit: 1.0.3
  • Sentence Transformers: 2.7.0
  • Transformers: 4.40.1
  • PyTorch: 2.1.2
  • Datasets: 2.19.0
  • Tokenizers: 0.19.1

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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