metadata

tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: >-
      Although traditional database search methods can effectively identify
      peptide matches, this approach correlates tandem mass spectral data with
      amino acid sequences in a protein database 'however' providing additional
      confirmation and improving identification accuracy.
  - text: >-
      The study involved 30 smallholder farmers from three different regions in
      Africa, each with an average farm size of 1.5 hectares and an annual
      income from farming of approximately $1,500.
  - text: >-
      This study aimed to evaluate the efficacy and safety of interferon α2b
      plus ribavirin for 48 weeks or 24 weeks compared to interferon α2b plus
      placebo for 48 weeks in the treatment of chronic hepatitis C virus
      infection.
  - text: >-
      The study reported that 73% of the psychotherapists endorsed the use of
      cognitive techniques in their treatment of eating disorders, while 61%
      reported using behavioral techniques.
  - text: >-
      Previous research on the psychoanalytic concept of the working alliance
      has established its significance in therapeutic change and identified key
      components such as the bond between therapist and client and the agreement
      on therapeutic goals.
metrics:
  - accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/all-MiniLM-L6-v2
model-index:
  - name: SetFit with sentence-transformers/all-MiniLM-L6-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.9498398588143016
            name: Accuracy

SetFit with sentence-transformers/all-MiniLM-L6-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-MiniLM-L6-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:

Fine-tuning a Sentence Transformer with contrastive learning.
Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Type: SetFit
Sentence Transformer body: sentence-transformers/all-MiniLM-L6-v2
Classification head: a LogisticRegression instance
Maximum Sequence Length: 256 tokens
Number of Classes: 2 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
Misc	'Pravastatin therapy in patients with average cholesterol levels following myocardial infarction has been shown to reduce the risk of coronary events, highlighting the importance of lipid-lowering therapy in internal medicine for cardiovascular disease prevention.' 'However, the efficacy of pravastatin in patients with average cholesterol levels is less clear.' 'This study investigates the impact of Pravastatin on reducing coronary events in internal medicine patients with average cholesterol levels after a myocardial infarction.'
Uncertainty	'Despite the widespread use of pravastatin in post-myocardial infarction patients with average cholesterol levels, the evidence regarding its impact on coronary events remains inconclusive and sometimes contradictory.' 'Despite the findings of this study showing a reduction in coronary events with Pravastatin use in patients with average cholesterol levels, contrasting evidence exists suggesting no significant benefit in similar patient populations (Miller et al., 2018).' 'Despite the proven benefits of dual antiplatelet therapy with aspirin and clopidogrel in the secondary prevention of cardiovascular events, particularly in coronary artery disease, there is a paucity of data specifically addressing its use in stroke or transient ischemic attack (TIA) patients.'

Label

Examples

Misc

'Pravastatin therapy in patients with average cholesterol levels following myocardial infarction has been shown to reduce the risk of coronary events, highlighting the importance of lipid-lowering therapy in internal medicine for cardiovascular disease prevention.'
'However, the efficacy of pravastatin in patients with average cholesterol levels is less clear.'
'This study investigates the impact of Pravastatin on reducing coronary events in internal medicine patients with average cholesterol levels after a myocardial infarction.'

Uncertainty

'Despite the widespread use of pravastatin in post-myocardial infarction patients with average cholesterol levels, the evidence regarding its impact on coronary events remains inconclusive and sometimes contradictory.'
'Despite the findings of this study showing a reduction in coronary events with Pravastatin use in patients with average cholesterol levels, contrasting evidence exists suggesting no significant benefit in similar patient populations (Miller et al., 2018).'
'Despite the proven benefits of dual antiplatelet therapy with aspirin and clopidogrel in the secondary prevention of cardiovascular events, particularly in coronary artery disease, there is a paucity of data specifically addressing its use in stroke or transient ischemic attack (TIA) patients.'

Evaluation

Metrics

Label	Accuracy
all	0.9498

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("Corran/SciGenSetfit24Binary")
# Run inference
preds = model("The study reported that 73% of the psychotherapists endorsed the use of cognitive techniques in their treatment of eating disorders, while 61% reported using behavioral techniques.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	8	29.6038	60

Label	Training Sample Count
Misc	2500
Uncertainty	2500

Training Hyperparameters

batch_size: (300, 300)
num_epochs: (1, 1)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 5
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
l2_weight: 0.01
seed: 42
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0060	1	0.4529	-
0.2994	50	0.3104	-
0.5988	100	0.2514	-
0.8982	150	0.25	-
1.0	167	-	0.2479
0.0060	1	0.2406	-
0.2994	50	0.1576	-
0.5988	100	0.0912	-
0.8982	150	0.0656	-
1.0	167	-	0.0683
0.0060	1	0.0827	-
0.2994	50	0.0581	-
0.5988	100	0.0393	-
0.8982	150	0.0339	-
1.0	167	-	0.0516

Framework Versions

Python: 3.10.12
SetFit: 1.2.0.dev0
Sentence Transformers: 3.1.1
Transformers: 4.42.2
PyTorch: 2.5.1+cu121
Datasets: 3.2.0
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}
}