metadata
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
"The Impact of Assessment for 21 st Century Skills in Higher Education
Institutions: A Narrative Literature Review" by Rany Sam You read the
paper Assessing 21st century skills: Integrating research findings. We
found a related paper on Academia:\r\n\r\nThe Impact of Assessment for 21
st Century Skills in Higher Education Institutions: A Narrative Literature
Review\r\nPaper Thumbnail\t\r\nAuthor Photo Rany Sam\r\n2024, Multitech
Publisher\r\n23 Views \r\nView PDF \u25B8\r\n \t\t\r\nDownload PDF
\u2B07\r\n\r
- text: >-
[Legal Notice] Update to Google Maps Platform terms and products effective
8 July 2025 \r\nHello Google Maps Platform customer,\r\n\r\nWe're writing
to let you know about some important updates to the Google Maps Platform
(GMP) Terms of Service (ToS) and our product offerings for customers with
any GMP project linked to a billing account with an address in the
European Economic Area (EEA customers). These updates will be effective on
8 July 2025.\r\n\r\nThe changes to our terms are a result of a recent proc
- text: >-
Update on our sub-processors list Dear Business Partner,\r\n\r\n
\r\n\r\nTo support our objectives of operational excellence and compliance
with industry best practices, we continuously monitor the best options to
deliver our products and services. \r\n\r\n \r\n\r\nAs of June 9, 2025
(for Enterprise Organizations July 9, 2025), our current list of
sub-processors will be replaced by the updated list available here. No
action is required on your part, and you may continue to use your account
as usual.\r\n\r\n
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/all-MiniLM-L6-v2
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 |
|---|---|
| 👨⚖️ Legal |
|
| 👮🏽♂️ Security |
|
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("\"The Impact of Assessment for 21 st Century Skills in Higher Education Institutions: A Narrative Literature Review\" by Rany Sam You read the paper Assessing 21st century skills: Integrating research findings. We found a related paper on Academia:\r\n\r\nThe Impact of Assessment for 21 st Century Skills in Higher Education Institutions: A Narrative Literature Review\r\nPaper Thumbnail\t\r\nAuthor Photo Rany Sam\r\n2024, Multitech Publisher\r\n23 Views \r\nView PDF \u25B8\r\n \t\t\r\nDownload PDF \u2B07\r\n\r")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 9 | 59.875 | 79 |
| Label | Training Sample Count |
|---|---|
| 👨⚖️ Legal | 6 |
| 👮🏽♂️ Security | 2 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 30
- 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: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0333 | 1 | 0.2806 | - |
| 1.6667 | 50 | 0.038 | - |
Framework Versions
- Python: 3.13.5
- SetFit: 1.1.2
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.7.1
- Datasets: 3.6.0
- 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}
}