README.md

metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
base_model: BAAI/bge-m3
metrics:
  - accuracy
widget:
  - text: >-
      How doCompaniesbalanceIndividualCreativitywithTeamCollaboration to
      driveInnovationinthe WORKPlace?
  - text: >-
      How do the values of a learning organization impact its ability to
      innovate and respond to constant change?
  - text: >-
      What is the primary function of the Domain Name System (DNS) layer in the
      Internet Protocol Stack, as defined by ICANN?
  - text: >-
      What distinguishes a transforming industry from one that merely innovates
      to existing practices?
  - text: >-
      How can artificial intelligence systems balance individual autonomy with
      collective responsibility in decision-making processes?
pipeline_tag: text-classification
inference: true
model-index:
  - name: SetFit with BAAI/bge-m3
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 1
            name: Accuracy

SetFit with BAAI/bge-m3

This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-m3 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:

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: BAAI/bge-m3
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 8192 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
lexical	'What is the primary function of the Apache Kafka distributed streaming platform in Big Data processing?' "What is the primary difference between Hadoop's FileSystem-based architecture and Apache Cassandra's distributed, masterlessArchitecture in scale-out design?" 'What is the main difference between optimistic concurrency control and pessimistic concurrency control in database management systems?'
semantic	"How does organizational morale impact the competitiveness of a company in today's fast-paced market?" 'How do organizations balance individual creativity with collective goal achievement in a dynamic environment?' 'What is a key challenge faced by managers in sustaining a work culture that encourages creativity, innovation, and critical thinking within the technological industry globally?'

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("yaniseuranova/setfit-paraphrase-mpnet-base-v2-sst2")
# Run inference
preds = model("What distinguishes a transforming industry from one that merely innovates to existing practices?")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	19.1839	42

Label	Training Sample Count
lexical	43
semantic	44

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
• seed: 42
• eval_max_steps: -1
• load_best_model_at_end: True

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0041	1	0.2391	-
0.2066	50	0.0033	-
0.4132	100	0.0007	-
0.6198	150	0.0007	-
0.8264	200	0.0007	-
1.0	242	-	0.0001
1.0331	250	0.0005	-
1.2397	300	0.0004	-
1.4463	350	0.0004	-
1.6529	400	0.0003	-
1.8595	450	0.0004	-
2.0	484	-	0.0001
2.0661	500	0.0003	-
2.2727	550	0.0003	-
2.4793	600	0.0002	-
2.6860	650	0.0003	-
2.8926	700	0.0002	-
3.0	726	-	0.0001
3.0992	750	0.0003	-
3.3058	800	0.0002	-
3.5124	850	0.0002	-
3.7190	900	0.0002	-
3.9256	950	0.0003	-
4.0	968	-	0.0001

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.12
• SetFit: 1.0.3
• Sentence Transformers: 2.6.1
• Transformers: 4.39.0
• PyTorch: 2.3.1+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}
}