metadata
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
Fido the Stamp is quite an innovative concept, blending pet ownership with
personalized mailing. Many dog owners likely delight in the idea of
featuring their pets' images on parcels. This personalization could make
mailing feel more intimate and fun for both senders and recipients.
However, one might wonder if everyone would want their pet's face
plastered on every package they send.
It’s plausible that some users might find it amusing to enhance the
mundane task of mailing with a dash of pet-inspired personality. Yet,
others might feel that featuring their dog on a stamp diminishes the
seriousness of the parcels they are sending. Fido the Stamp could also
lead
- text: >-
Policy statement revised; IV antibiotic therapy is not medically necessary
for uncomplicated cranial nerve palsy associated with Lyme disease and
antibiotic-refractory Lyme arthritis
7/19/2007: Reviewed and approved by MPAC
7/10/2009: Policy reviewed, no changes
12/15/2009: Coding Section revised with 2010 CPT4 and HCPCS revisions
02/23/2011: Added the following to the policy statement: Determination of
levels of the B lymphocyte chemoattractant CXCL13 for diagnosis or
monitoring treatment is considered investigational. No changes to other
policy statements. Removed deleted HCPCS codes J0530, J0540, and J0550
from the Code Reference section. 02/24/2012: Add the following policy
statement: A single 2- to 4-week course of IV antibiotics may be
considered medically necessary in patients with Lyme carditis, as
evidenced by positive serologic findings (defined above) and associated
with a high degree of atrioventricular block or a PR interval of greater
than 0.3 second. Documentation of Lyme carditis may include PCR-based
direct detection of B burgdorferi in the blood when results of serologic
studies are equivocal.
- text: >-
The subdued July inflation figures indicate that consumer prices are not
increasing rapidly, which usually boosts investor confidence in
fixed-income securities like Treasuries. Likewise, soaring oil prices
suggest that consumers may start cutting back on spending, leading to a
cooling of the overall economy. This cooling effect can trigger a flight
to safety among investors who prefer stable returns in uncertain times,
causing an uptick in Treasury demand. Moreover, summer BBQs would probably
be less popular if gas prices continue to rise, as people will likely
prioritize their spending on essentials. However, a decrease in consumer
spending could ironically lead to increased economic activity as consumers
save money instead of spending it
- text: >-
POLICY HISTORY1/1994: Approved by Medical Policy Advisory Committee (MPAC)
5/1/2002: Type of Service and Place of Service deleted
3/25/2004: Reviewed by MPAC, Policy title “Lyme Disease Treatment” renamed
“Intravenous Antiobiotic Therapy for Lyme Disease”, Description and Policy
sections revised to be consistent with BCBSA policy # 5.01.08, intravenous
antibiotic therapy changed from investigational to medically necessary for
certain indications, investigation definition added, Sources updated,
tables added to Code Reference section
5/5/2004: Code Reference section completed
3/13/2006: Policy reviewed, no changes
9/12/2006: Coding reviewed. ICD9 2006 revisions added to policy
11/13/2006: Code Reference section updated: CPT codes 87475, 87476, and
87477 deleted from policy
4/24/2007: Policy reviewed, policy statement rewritten for clarification
6/21/2007: Policy reviewed, description updated. Policy statement revised;
IV antibiotic therapy is not medically necessary for uncomplicated cranial
nerve palsy associated with Lyme disease and antibiotic-refractory Lyme
arthritis
7/19/2007: Reviewed and approved by MPAC
7/10/2009: Policy reviewed, no changes
12/15/2009: Coding Section revised with 2010 CPT4 and HCPCS revisions
02/23/2011: Added the following to the policy statement: Determination of
levels of the B lymphocyte chemoattractant CXCL13 for diagnosis or
monitoring treatment is considered investigational. No changes to other
policy statements. Removed deleted HCPCS codes J0530, J0540, and J0550
from the Code Reference section.
- text: >-
Given the symptoms described, the most likely karyotype for this
15-year-old boy is 47,XXY, which is characteristic of Klinefelter
syndrome. The combination of decreased facial and pubic hair,
gynecomastia, small testes, long extremities, and tall stature aligns with
this chromosomal pattern. Klinefelter syndrome is caused by the presence
of an extra X chromosome, leading to the 47,XXY karyotype.
metrics:
- accuracy
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: 1
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 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 |
---|---|
positive |
|
negative |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 1.0 |
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("ashercn97/code-y-v1")
# Run inference
preds = model("Given the symptoms described, the most likely karyotype for this 15-year-old boy is 47,XXY, which is characteristic of Klinefelter syndrome. The combination of decreased facial and pubic hair, gynecomastia, small testes, long extremities, and tall stature aligns with this chromosomal pattern. Klinefelter syndrome is caused by the presence of an extra X chromosome, leading to the 47,XXY karyotype.")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 52 | 148.16 | 266 |
Label | Training Sample Count |
---|---|
negative | 9 |
positive | 16 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- max_steps: -1
- sampling_strategy: oversampling
- 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: True
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0435 | 1 | 0.1928 | - |
1.0 | 23 | - | 0.0154 |
2.0 | 46 | - | 0.0023 |
2.1739 | 50 | 0.0214 | - |
3.0 | 69 | - | 0.0018 |
4.0 | 92 | - | 0.0015 |
Framework Versions
- Python: 3.11.10
- SetFit: 1.1.2
- Sentence Transformers: 4.0.2
- Transformers: 4.51.3
- PyTorch: 2.4.1+cu124
- Datasets: 3.5.0
- Tokenizers: 0.21.1
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}
}