README.md

metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      The Alavas worked themselves to the bone in the last period , and English
      and San Emeterio ( 65-75 ) had already made it clear that they were not
      going to let anyone take away what they had earned during the first thirty
      minutes . 
  - text: 'To break the uncomfortable silence , Haney began to talk . '
  - text: >-
      For the treatment of non-small cell lung cancer , the effects of Alimta
      were compared with those of docetaxel ( another anticancer medicine ) in
      one study involving 571 patients with locally advanced or metastatic
      disease who had received chemotherapy in the past . 
  - text: >-
      As we all know , a few minutes before the end of the game ( that their
      team had already won ) , both players deliberately wasted time which made
      the referee show the second yellow card to both of them . 
  - text: >-
      In contrast , patients whose cancer was affecting squamous cells had
      shorter survival times if they received Alimta . 
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
  - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.1271523178807947
            name: Accuracy

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 Type: SetFit
• Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2
• Classification head: a SetFitHead instance
• Maximum Sequence Length: 512 tokens
• Number of Classes: 7 classes

Model Sources

• Repository: SetFit on GitHub
• Paper: Efficient Few-Shot Learning Without Prompts
• Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label	Examples
4	'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 . ' 'No offense intended , he said gently . ' "It 's my line of work , he said "
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 . '
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 . ' 'The idea would be to administer to patients the growth-controlling proteins made by healthy versions of the damaged genes . ' 'That is the way the system works . '
0	'Prior to 1932 , the pattern was nearly the opposite . ' 'Never in my life have I been so frightened . ' 'Then your focus will go to an input text box where you can type your function . '
1	'Mr. Neuberger realized that , although of Italian ancestry , Mr. Mariotta still could qualify as a minority person since he was born in Puerto Rico . ' 'But Dr. Vogelstein had yet to nail the identity of the gene that , if damaged , flipped a colon cell into full-blown malignancy . ' 'Some found it on the screen of a personal computer . '
5	"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 . " 'The scientists say that since breast cancer often strikes multiple members of certain families , the gene , when inherited in a damaged form , may predispose women to the cancer . '
2	'It explains how the Committee for Medicinal Products for Veterinary Use ( CVMP ) assessed the studies performed , to reach their recommendations on how to use the medicine . ' 'US banks repay state support ' '-- In most states , increasing expenditures on education , in our current circumstances , will probably make things worse , not better . '

Evaluation

Metrics

Label	Accuracy
all	0.1272

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-6")
# 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	25.0952	74

Label	Training Sample Count
0	6
1	6
2	6
3	6
4	6
5	6
6	6

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
0.0095	1	0.3696	-
0.4762	50	0.1725	-
0.9524	100	0.0204	-
1.4286	150	0.0051	-
1.9048	200	0.0037	-

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}
}