README.md

metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
base_model: sentence-transformers/all-mpnet-base-v2
metrics:
  - accuracy
widget:
  - text: Warm thanks to the volunteers who dedicated their time this weekend.
  - text: Hats off to the design team.
  - text: >-
      Your assistance with the move was invaluable; I couldn’t have managed
      without you.
  - text: How heartwarming it is to see communities come together in times of need.
  - text: Well done on orchestrating such a seamless event!
pipeline_tag: text-classification
inference: false
model-index:
  - name: SetFit with sentence-transformers/all-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.42857142857142855
            name: Accuracy

SetFit with sentence-transformers/all-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-mpnet-base-v2 as the Sentence Transformer embedding model. A MultiOutputClassifier instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. 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-mpnet-base-v2
• Classification head: a MultiOutputClassifier instance
• Maximum Sequence Length: 384 tokens
• Number of Classes: 3 classes

Model Sources

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

Evaluation

Metrics

Label	Accuracy
all	0.4286

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("Hats off to the design team.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	6	10.8571	16

Training Hyperparameters

• batch_size: (16, 2)
• num_epochs: (10, 10)
• 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
• seed: 42
• eval_max_steps: -1
• load_best_model_at_end: True

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0556	1	0.323	-
1.0	18	-	0.2331
2.0	36	-	0.1994
2.7778	50	0.0779	-
3.0	54	-	0.2288
4.0	72	-	0.2379
5.0	90	-	0.2529
5.5556	100	0.0373	-
6.0	108	-	0.2569
7.0	126	-	0.2665
8.0	144	-	0.2643
8.3333	150	0.0095	-
9.0	162	-	0.2639
10.0	180	-	0.2656

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.12.1
• SetFit: 1.0.3
• Sentence Transformers: 2.7.0
• Transformers: 4.37.2
• PyTorch: 2.2.0
• Datasets: 2.19.1
• 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}
}