metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
datasets:
  - ag_news
metrics:
  - accuracy
widget:
  - text: >-
      FSU-Miami Postponed Hurricane Frances forces the postponement of Monday's
      college football season opener between Florida State and Miami.
  - text: >-
      Lenovo to buy IBM PC arm IBM said late Tuesday that it will sell its
      personal computer division, transferring an iconic brand to a Chinese
      rival that also will absorb about 2,000 local workers.
  - text: >-
      NBA Roundup: Sonics fly high again in Philly PHILADELPHIA - Wide open or
      contested, the Seattle SuperSonics hit three-pointers from all over the
      court. Ray Allen scored a season-high 37 points, Rashard Lewis had 21 and
      Vladimir Radmanovic added 20, leading 
  - text: >-
      Democrats Come to Observe Convention (AP) AP - The Democrats have come to
      town to prick rhetorical balloons at the Republican National Convention.
  - text: >-
      US women into final The United States edged past world champions Germany
      in a dramatic 2-1 victory to seal their place in the women #39;s football
      final.
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-MiniLM-L3-v2

SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2

This is a SetFit model trained on the ag_news dataset that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-MiniLM-L3-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-MiniLM-L3-v2
Classification head: a LogisticRegression instance
Maximum Sequence Length: 128 tokens
Training Dataset: ag_news

Model Sources

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

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("vincent1337/test_student_model")
# Run inference
preds = model("FSU-Miami Postponed Hurricane Frances forces the postponement of Monday's college football season opener between Florida State and Miami.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	18	36.04	51

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (1, 16)
max_steps: 50
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
seed: 42
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0196	1	0.8923	-
0.9804	50	0.0968	-
0.0196	1	0.0852	-
0.9804	50	0.0048	-

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 2.7.0
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}
}