metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
base_model: sentence-transformers/all-mpnet-base-v2
widget:
  - text: >-
      How do companies balance individual creativity with team collaboration to
      drive innovation in the work place?
  - 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 RAG query classificator for hybrid search query routing
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 1
            name: Accuracy
language:
  - en

Fast Query Routing for RAG Hybrid Search Using Setfit Tuned Embedding Model.

The goal of this model is to classify users queries in a RAG pipeline between two classes 'semantic' and 'lexical'. This allow an easy query routing in the context of hybrid search and alpha tuning for hybrid search. A query is considered 'semantic' if it doesn't contain any particular jargon, proper noun, technical terms, ect.. on the other hand it is considered lexical if there are precise keywords than can be used to make a lexical search (BM25 for example).

The model is very small and fast, thus enabling a very cost-effective approach for query routing comparing to use large LLMs such as GPT4 for query routing !

The model was trained using the SetFit method that allows Text Classification model finetuning with a reduced number of human annotated training examples. This SetFit model uses sentence-transformers/all-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/all-mpnet-base-v2
Classification head: a LogisticRegression instance
Maximum Sequence Length: 384 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?'

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: (8, 8)
num_epochs: (3, 3)
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
0.0021	1	0.301	-
0.1033	50	0.1244	-
0.2066	100	0.0021	-
0.3099	150	0.0006	-
0.4132	200	0.0002	-
0.5165	250	0.0002	-
0.6198	300	0.0001	-
0.7231	350	0.0001	-
0.8264	400	0.0001	-
0.9298	450	0.0001	-
1.0	484	-	0.0001
1.0331	500	0.0001	-
1.1364	550	0.0001	-
1.2397	600	0.0001	-
1.3430	650	0.0	-
1.4463	700	0.0001	-
1.5496	750	0.0001	-
1.6529	800	0.0001	-
1.7562	850	0.0001	-
1.8595	900	0.0	-
1.9628	950	0.0	-
2.0	968	-	0.0001
2.0661	1000	0.0001	-
2.1694	1050	0.0001	-
2.2727	1100	0.0	-
2.3760	1150	0.0	-
2.4793	1200	0.0	-
2.5826	1250	0.0	-
2.6860	1300	0.0001	-
2.7893	1350	0.0	-
2.8926	1400	0.0001	-
2.9959	1450	0.0	-
3.0	1452	-	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}
}