metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
widget:
  - text: >-
      Maintenance to the cambridge.org website is scheduled for 14 March at 12am
      – 8am GMT.
  - text: Quarterly Earnings
  - text: >-
      So set sail for Long John Silver's and discover why wa're America's most
      popular sealood vestments antannro fi 
  - text: |2-

                                                              OPEC oil price annually 1960-2024
                                                          
  - text: 'RUSSELL WILSON OF THE SEATTLE SEAHAWKS — DURING SUPER BOWL XLVIII '
pipeline_tag: text-classification
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: 0.8083333333333333
            name: Accuracy
          - type: precision
            value: 0.7894736842105263
            name: Precision
          - type: recall
            value: 0.8035714285714286
            name: Recall
          - type: f1
            value: 0.7964601769911505
            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:

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
False	'Learn more about this provider' 'Verletzte und Festnahmen' 'Bulgaria'
True	'Free Quotes on Doors ' 'Pakistan Cricket Board, Gaddafi Stadium, Ferozepur Road, Lahore, Pakistan. E-Mail: careers@pcb.com.pk ' "‘here's a new predator in the urban jungle "

Evaluation

Metrics

Label	Accuracy	Precision	Recall	F1
all	0.8083	0.7895	0.8036	0.7965

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("Quarterly Earnings")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	8.2229	242

Label	Training Sample Count
False	236
True	244

Training Hyperparameters

batch_size: (16, 2)
num_epochs: (1, 16)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 20
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
run_name: PG-OCR-test-2
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0008	1	0.3892	-
0.0417	50	0.2262	-
0.0833	100	0.2138	-
0.125	150	0.1058	-
0.1667	200	0.1327	-
0.2083	250	0.098	-
0.25	300	0.0719	-
0.2917	350	0.0634	-
0.3333	400	0.0021	-
0.375	450	0.0084	-
0.4167	500	0.0799	-
0.4583	550	0.0822	-
0.5	600	0.0775	-
0.5417	650	0.0114	-
0.5833	700	0.0013	-
0.625	750	0.0121	-
0.6667	800	0.1034	-
0.7083	850	0.0539	-
0.75	900	0.0076	-
0.7917	950	0.0114	-
0.8333	1000	0.0223	-
0.875	1050	0.0208	-
0.9167	1100	0.0246	-
0.9583	1150	0.0098	-
1.0	1200	0.003	-

Framework Versions

Python: 3.11.0
SetFit: 1.0.3
Sentence Transformers: 2.3.0
Transformers: 4.37.2
PyTorch: 2.2.1+cu121
Datasets: 2.16.1
Tokenizers: 0.15.1

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}
}