metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-mpnet-base-v2
metrics:
  - accuracy
widget:
  - text: >-
      Anyone 170 or below that takes Wegovy? Well it is, Ozempic and Wegovy are
      actually the same drug. So if you are taking Wegovy, it is affecting your
      insulin and glucose. I was on Trulicity, insurance made me switch to
      wegovy.
  - text: >-
      New Ozempic and Wegovy side effects come to light - After I stopped taking
      it I developed Gallbladder disease and Pancreatitis
  - text: >-
      The beginning of my Semaglutide journey! ???? #semaglutide #ozempic
      #wegovy #weightloss #health #prediabetes #semaglutideweightloss #change
  - text: >-
      I am on victoza. It works well for me. Ozempic made me sick so my doctor
      placed me back on victoza since it was working. I do want to mention that
      the side effects of victoza re the same as Ozempic. That includes thyroid
      issues.
  - text: >-
      What's the cheapest way possible to get semaglutide? I'm currently taking
      2000mg of Metformin with compounded semaglutide with no issues. I have
      PCOS and not Type 2, so I sadly don't qualify for Ozempic through
      insurance.
pipeline_tag: text-classification
inference: true

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:

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 LogisticRegression instance
Maximum Sequence Length: 512 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
1	'I skipped this era, did she ever give a reason why she quit Ozempic? It’s fucking bananas to me she quit after finding something that works for the first time in a decade.' "I was on Ozempic and I lost 40lbs but it stopped there. It cost me $225 Canadian per month. I'm not taking anymore." "Maybe you can try Manjaro or a similar drug. Also, I'm sure they have Ozempic replacements in the works that will be released and you could try them."
0	'I was taking 4 metformin a day with morning blood sugar numbers around 200. I am currently on week 11 of taking Ozempic and only 1 metformin. My sugars are around 100 every morning. I finally feel good.' "Yup, I'm on CRF as well and have probably gained about 50 lbs over time. It sucks. I'm currently taking a smaller dose of mirtazapine and am also on ozempic for weight loss." "July 19 started new lifestyle. Down 30.6 lbs already - Interesting! I started off with Metaformin for a month, but wasn't happy with it, so my doc put me on Ozempic. I just started 0.25. Just for my own understanding, did you doc recommend taking both? If so, did he give any warnings about mixing the two?"

Label

Examples

'I skipped this era, did she ever give a reason why she quit Ozempic? It’s fucking bananas to me she quit after finding something that works for the first time in a decade.'
"I was on Ozempic and I lost 40lbs but it stopped there. It cost me $225 Canadian per month. I'm not taking anymore."
"Maybe you can try Manjaro or a similar drug. Also, I'm sure they have Ozempic replacements in the works that will be released and you could try them."

'I was taking 4 metformin a day with morning blood sugar numbers around 200. I am currently on week 11 of taking Ozempic and only 1 metformin. My sugars are around 100 every morning. I finally feel good.'
"Yup, I'm on CRF as well and have probably gained about 50 lbs over time. It sucks. I'm currently taking a smaller dose of mirtazapine and am also on ozempic for weight loss."
"July 19 started new lifestyle. Down 30.6 lbs already - Interesting! I started off with Metaformin for a month, but wasn't happy with it, so my doc put me on Ozempic. I just started 0.25. Just for my own understanding, did you doc recommend taking both? If so, did he give any warnings about mixing the two?"

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("bhaskars113/ozempic-taking-medications-classifier-1.1")
# Run inference
preds = model("New Ozempic and Wegovy side effects come to light - After I stopped taking it I developed Gallbladder disease and Pancreatitis")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	14	36.4333	94

Label	Training Sample Count
0	15
1	15

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
0.0133	1	0.223	-
0.6667	50	0.0031	-

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 2.7.0
Transformers: 4.40.0
PyTorch: 2.2.1+cu121
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}
}