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temporalInformation_classifier

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
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metadata 
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: >-
      i’ve just been making sure that it is healthier food and not unhealthy
      food.
  - text: >-
      28 male, history of smoking but quit last year, no major health issues,
      history of pretty bad acne on back as a teen - was on acutane as a teen,
      6ft something, healthy average weight.
  - text: >-
      is this expected of a fairly healthy young person just due to getting
      covid?
  - text: >-
      we never said no matter the cost, we always said as long as mom and baby
      are healthy.
  - text: >-
      for how many days in succession, can one healthy individual take a single
      dose of 500mg paracetamol, without causing liver damage?
metrics:
  - accuracy
  - precision
  - recall
  - f1
pipeline_tag: text-classification
library_name: setfit
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: 0.75
            name: Accuracy
          - type: precision
            value: 0.75
            name: Precision
          - type: recall
            value: 0.75
            name: Recall
          - type: f1
            value: 0.75
            name: F1

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
time
  • "i mean it hasn't been that long, my heart is perfectly healthy otherwise."
  • 'otherwise i’m been healthy and all other blood work they did this year was unremarkable. \n'
  • 'but i can not run 3 seconds without breathing for 10 minutes that should say how unhealthy i am.'
no
  • 'one of the ob doctors i work with likes to emphasize these lists are birth "preferences" as the birth plan is ultimately having a healthy baby and mom.'
  • 'some who may seem “soft” to you enjoy the challenge and reward of safely delivering tens of thousands of healthy babies in their career and putting them in their mother’s arms. \n\n'
  • 'so you are right, he is just going to wipe out normal healthy flora , and this includes that wee innocent little lactobacillus the gp wants to put down like old yeller.'

Evaluation

Metrics

Label Accuracy Precision Recall F1
all 0.75 0.75 0.75 0.75

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("setfit_model_id")
# Run inference
preds = model("i’ve just been making sure that it is healthier food and not unhealthy food.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 12 25.325 60
Label Training Sample Count
no 36
time 44

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (10, 10)
  • 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
  • l2_weight: 0.01
  • seed: 3786
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.005 1 0.2939 -
0.25 50 0.2641 -
0.5 100 0.195 -
0.75 150 0.0162 -
1.0 200 0.0007 -
1.25 250 0.0003 -
1.5 300 0.0002 -
1.75 350 0.0001 -
2.0 400 0.0001 -
2.25 450 0.0002 -
2.5 500 0.0013 -
2.75 550 0.0002 -
3.0 600 0.0006 -
3.25 650 0.0015 -
3.5 700 0.0008 -
3.75 750 0.0001 -
4.0 800 0.0001 -
4.25 850 0.0007 -
4.5 900 0.0001 -
4.75 950 0.003 -
5.0 1000 0.0001 -
5.25 1050 0.0018 -
5.5 1100 0.0001 -
5.75 1150 0.0001 -
6.0 1200 0.0014 -
6.25 1250 0.0001 -
6.5 1300 0.0009 -
6.75 1350 0.0001 -
7.0 1400 0.0002 -
7.25 1450 0.0 -
7.5 1500 0.0 -
7.75 1550 0.0002 -
8.0 1600 0.0 -
8.25 1650 0.0006 -
8.5 1700 0.0 -
8.75 1750 0.0 -
9.0 1800 0.0 -
9.25 1850 0.0 -
9.5 1900 0.0 -
9.75 1950 0.0 -
10.0 2000 0.0 -

Framework Versions

  • Python: 3.11.7
  • SetFit: 1.1.1
  • Sentence Transformers: 3.3.1
  • Transformers: 4.44.2
  • PyTorch: 2.5.1
  • Datasets: 2.19.0
  • 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}
}