metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      She is Female, her heart rate is 63, she walks 4000 steps daily and is
      Underweight. She slept at 2 hrs. Yesterday, she slept from 1 hrs to 7 hrs,
      with a duration of 360 minutes and 5 interruptions. The day before
      yesterday, she slept from 23 hrs to 7 hrs, with a duration of 420 minutes
      and 3 interruptions.
  - text: >-
      She is Female, her heart rate is 70, she walks 8000 steps daily and is
      Normal. She slept at 22 hrs. Yesterday, she slept from 23 hrs to 7 hrs,
      with a duration of 400 minutes and 2 interruptions. The day before
      yesterday, she slept from 22 hrs to 6 hrs, with a duration of 430 minutes
      and 2 interruptions.
  - text: >-
      He is Male, his heart rate is 70, he walks 2400 steps daily, and is
      Underweight. He slept at 0 hrs. Yesterday, he slept from 2hrs to 7 hrs,
      with a duration of 280 minutes and 4 interruptions. The day before
      yesterday, he slept from 2 hrs to 8 hrs, with a duration of 340 minutes
      and 4 interruptions.
  - text: >-
      She is Female, her heart rate is 68, she walks 11,000 steps daily and is
      Normal. She slept at 1 hrs. Yesterday, she slept from 1 hrs to 9 hrs, with
      a duration of 495 minutes and 0 interruptions. The day before yesterday,
      she slept from 1 hrs to 10 hrs, with a duration of 540 minutes and 1
      interruptions.
  - text: >-
      He is Male, his heart rate is 67, he walks 12000 steps daily, and is
      Normal. He slept at 3 hrs. Yesterday, he slept from 4hrs to 11 hrs, with a
      duration of 420 minutes and 3 interruptions. The day before yesterday, he
      slept from 3 hrs to 5 hrs, with a duration of 150 minutes and 0
      interruptions.
pipeline_tag: text-classification
inference: false
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
            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 OneVsRestClassifier 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/paraphrase-mpnet-base-v2
Classification head: a OneVsRestClassifier instance
Maximum Sequence Length: 512 tokens

Model Sources

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

Evaluation

Metrics

Label	Accuracy
all	0.0

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("naushin/setfit-ethos-multilabel-example")
# Run inference
preds = model("He is Male, his heart rate is 67, he walks 12000 steps daily, and is Normal. He slept at 3 hrs. Yesterday, he slept from 4hrs to 11 hrs, with a duration of 420 minutes and 3 interruptions. The day before yesterday, he slept from 3 hrs to 5 hrs, with a duration of 150 minutes and 0 interruptions.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	59	59.5	60

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
0.0667	1	0.421	-

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 2.6.1
Transformers: 4.38.2
PyTorch: 2.2.1+cu121
Datasets: 2.18.0
Tokenizers: 0.15.2

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}
}