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Netta1994
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setfit_undersampling_2k

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
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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
1.0
  • 'Based solely on the given details, it is not feasible to ascertain the physical impacts on the body when an individual is fatigued and subsequently encounters a surge of energy. The provided data primarily concentrates on pH and CO levels in various contexts such as conductivity, soil, water, and culture vessels. Nevertheless, it does not'
  • 'To get a ShoutOut to pop up monthly, you would need to set the frequency options for the ShoutOut to trigger once a month. However, the provided contexts only mention frequency options such as "Once," "Once a Day," and "Always." There is no direct mention of a monthly frequency option in the provided contexts.\n\nGiven this information, the answer to your question based on the provided contexts is: "I'm sorry, I'm not sure how to answer your question. Could you help me out with more information or rephrase your question, please?'
  • "I can see how having the credit card details of a business would provide a deeper understanding of their expenditures. Yet, releasing information such as credit card numbers is strictly against privacy policies and regulations. It's illegal, unethical, and a severe breach of trust to share such confidential details."
0.0
  • 'pRect is an object that contains the x, y, width, and height properties. It is used to determine the index of the object in the nodes array and to insert the object into the nodes object.'
  • 'Yes, you can search an outside knowledge base using the keywords a user searched for in the player menu. WalkMe offers a Search Provider Integration feature that allows you to supplement your WalkMe items with your existing knowledge base or support center resources. Once enabled, a search performed within the WalkMe Widget will yield results from the specified domains, showing your existing content alongside your WalkMe content. The current supported search providers for this integration are Zendesk, Desk, Bing, and Google. If your current search provider is not on the supported list, please reach out to your Account Manager for further assistance. For more information on how to set up the Search Provider Integration, please refer to our Support article. How else can I assist you today?'
  • 'Write a precise answer to "how to export homepage to pdf" only based on "KB12345". Only when absolutely confident that If the information is not present in the "KB12345", respond with Answer Not Found.'

Evaluation

Metrics

Label Accuracy
all 0.9840

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("Netta1994/setfit_undersampling_2k")
# Run inference
preds = model("The author clearly cites it as a Reddit thread.  In a scholastic paper,  you would be expected to have a bit more original content,  but you wouldn't 'get in trouble' ")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 89.6623 412
Label Training Sample Count
0.0 1454
1.0 527

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 20
  • 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
0.0002 1 0.3718 -
0.0101 50 0.2723 -
0.0202 100 0.1298 -
0.0303 150 0.091 -
0.0404 200 0.046 -
0.0505 250 0.0348 -
0.0606 300 0.0208 -
0.0707 350 0.0044 -
0.0808 400 0.0041 -
0.0909 450 0.0046 -
0.1009 500 0.0007 -
0.1110 550 0.0004 -
0.1211 600 0.0601 -
0.1312 650 0.0006 -
0.1413 700 0.0006 -
0.1514 750 0.0661 -
0.1615 800 0.0002 -
0.1716 850 0.0009 -
0.1817 900 0.0002 -
0.1918 950 0.0017 -
0.2019 1000 0.0007 -
0.2120 1050 0.0606 -
0.2221 1100 0.0001 -
0.2322 1150 0.0004 -
0.2423 1200 0.0029 -
0.2524 1250 0.0001 -
0.2625 1300 0.0001 -
0.2726 1350 0.0001 -
0.2827 1400 0.0047 -
0.2928 1450 0.0 -
0.3028 1500 0.0 -
0.3129 1550 0.0 -
0.3230 1600 0.0 -
0.3331 1650 0.0001 -
0.3432 1700 0.0004 -
0.3533 1750 0.0 -
0.3634 1800 0.0 -
0.3735 1850 0.0 -
0.3836 1900 0.0 -
0.3937 1950 0.0 -
0.4038 2000 0.0 -
0.4139 2050 0.0 -
0.4240 2100 0.0 -
0.4341 2150 0.0 -
0.4442 2200 0.0 -
0.4543 2250 0.0001 -
0.4644 2300 0.0 -
0.4745 2350 0.0 -
0.4846 2400 0.0 -
0.4946 2450 0.0 -
0.5047 2500 0.0 -
0.5148 2550 0.0 -
0.5249 2600 0.0 -
0.5350 2650 0.0 -
0.5451 2700 0.0 -
0.5552 2750 0.0001 -
0.5653 2800 0.0 -
0.5754 2850 0.0 -
0.5855 2900 0.0 -
0.5956 2950 0.0 -
0.6057 3000 0.0 -
0.6158 3050 0.0 -
0.6259 3100 0.0002 -
0.6360 3150 0.0 -
0.6461 3200 0.0 -
0.6562 3250 0.0002 -
0.6663 3300 0.0 -
0.6764 3350 0.0 -
0.6865 3400 0.0 -
0.6965 3450 0.0 -
0.7066 3500 0.0 -
0.7167 3550 0.0 -
0.7268 3600 0.0 -
0.7369 3650 0.0 -
0.7470 3700 0.0 -
0.7571 3750 0.0 -
0.7672 3800 0.0 -
0.7773 3850 0.0 -
0.7874 3900 0.0 -
0.7975 3950 0.0 -
0.8076 4000 0.0 -
0.8177 4050 0.0 -
0.8278 4100 0.0 -
0.8379 4150 0.0 -
0.8480 4200 0.0 -
0.8581 4250 0.0 -
0.8682 4300 0.0 -
0.8783 4350 0.0 -
0.8884 4400 0.0 -
0.8984 4450 0.0 -
0.9085 4500 0.0 -
0.9186 4550 0.0 -
0.9287 4600 0.0 -
0.9388 4650 0.0 -
0.9489 4700 0.0 -
0.9590 4750 0.0 -
0.9691 4800 0.0 -
0.9792 4850 0.0 -
0.9893 4900 0.0 -
0.9994 4950 0.0 -

Framework Versions

  • Python: 3.10.14
  • SetFit: 1.0.3
  • Sentence Transformers: 2.7.0
  • Transformers: 4.40.1
  • PyTorch: 2.2.0+cu121
  • Datasets: 2.19.1
  • 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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