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 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

Model Labels

Label Examples
1
  • 'Eventually little French farmers and their little French farmwives came out of their stone houses and put their hands above their tiny eyes and squinted at us . '
  • 'Mr. Neuberger realized that , although of Italian ancestry , Mr. Mariotta still could qualify as a minority person since he was born in Puerto Rico . '
  • "Biggest trouble was scared family who could n't get a phone line through , and spent a really horrible hour not knowing . "
4
  • 'To ring for even one service at this tower , we have to scrape , says Mr. Hammond , a retired water-authority worker . '</li><li>" It 's my line of work , he said "</li><li>'One writer , signing his letter as Red-blooded , balanced male , remarked on the frequency of women fainting in peals , and suggested that they settle back into their traditional role of making tea at meetings . `` '
5
  • 'Of course On Thursday , Haney mailed the monthly check for separate maintenance to his wife Lolly , and wished the stranger could do something about her '
  • "On the Right , the tone was set by Jacques Chirac , who declared in 1976 that 900,000 unemployed would not become a problem in a country with 2 million of foreign workers , '' and on the Left by Michel Rocard explaining in 1990 that France can not accommodate all the world 's misery . '' "
  • "But the council 's program to attract and train ringers is only partly successful , says Mr. Baldwin . "
6
  • '3 -RRB- Republican congressional representatives , because of their belief in a minimalist state , are less willing to engage in local benefit-seeking than are Democratic members of Congress . '
  • 'As we know , voters tend to favor Republicans more in races for president than in those for Congress . '
  • 'That is the way the system works . '
2
  • '-- Students should move up the educational ladder as their academic potential allows . '
  • 'The next day , Sunday , the hangover reminded Haney where he had been the night before . '
  • '-- In most states , increasing expenditures on education , in our current circumstances , will probably make things worse , not better . '
0
  • 'Then your focus will go to an input text box where you can type your function . '
  • "I might have got hit by that truck if it was n't for you . "
  • "Second , it explains why voters hold Congress in disdain but generally love their own congressional representatives : Any individual legislator 's constituents appreciate the specific benefits that the legislator wins for them but not the overall cost associated with every other legislator doing likewise for his own constituency . "
3
  • "It was the most exercise we 'd had all morning and it was followed by our driving immediately to the nearest watering hole . "
  • 'Alimta is used together with cisplatin ( another anticancer medicine ) when the cancer is unresectable ( cannot be removed by surgery alone ) and malignant ( has spread , or is likely to spread easily , to other parts of the body ) , in patients who have not received chemotherapy ( medicines for cancer ) before advanced or metastatic non-small cell lung cancer that is not affecting the squamous cells . '
  • 'If it is , it will be treated as an operator , if it is not , it will be treated as a user function . '

Evaluation

Metrics

Label Accuracy
all 0.1417

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("HelgeKn/SemEval-multi-class-4")
# Run inference
preds = model("To break the uncomfortable silence , Haney began to talk . ")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 4 27.1786 74
Label Training Sample Count
0 4
1 4
2 4
3 4
4 4
5 4
6 4

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (2, 2)
  • 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
0.0143 1 0.2446 -
0.7143 50 0.0612 -
1.4286 100 0.0078 -

Framework Versions

  • Python: 3.9.13
  • SetFit: 1.0.1
  • Sentence Transformers: 2.2.2
  • Transformers: 4.36.0
  • PyTorch: 2.1.1+cpu
  • Datasets: 2.15.0
  • 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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