metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-mpnet-base-v2
metrics:
  - accuracy
widget:
  - text: I need to repair an issue I found in my policy.
  - text: I'm wondering about the developments in my policy alteration.
  - text: Hi, I've noticed my policy is incomplete and needs additional details.
  - text: What's the latest on the status of my claim?
  - text: I have my own health insurance policy.
pipeline_tag: text-classification
inference: true
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: 0.9159663865546218
            name: Accuracy

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: 5 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
2	'I require assistance in altering certain elements of my policy.' "Hey there, I've spotted a gap in my policy information." 'I need to rectify something within my policy documentation.'
4	"I am covered by health insurance through my employer's sponsorship." 'Is it permissible to transfer my health plan to ACKO?' 'My old health policy from another insurance provider is no longer in effect.'
3	'Can you reveal all policies under my profile?' 'I want to be informed about the status of all my insurance arrangements.' "Is it possible for you to display my family's health insurance policies?"
1	'How is my vehicle claim proceeding?' "I'm curious about the status of my car insurance claim." 'Am I required to provide additional evidence for my claims?'
0	'I need help selecting an appropriate health insurance plan for my family.' "I'm looking for a health policy that will cover me along with my two kids." "I'm in urgent need of a health insurance plan for my family's wellbeing."

Evaluation

Metrics

Label	Accuracy
all	0.9160

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("harshita23sh/setfit-model-intent-classification-insurance")
# Run inference
preds = model("I have my own health insurance policy.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	5	10.6	15

Label	Training Sample Count
0	5
1	8
2	12
3	4
4	11

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (1, 1)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 20
body_learning_rate: (2e-05, 2e-05)
head_learning_rate: 2e-05
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
0.01	1	0.1388	-
0.5	50	0.0087	-
1.0	100	0.0029	-

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 2.7.0
Transformers: 4.40.0
PyTorch: 2.2.1+cu121
Datasets: 2.19.0
Tokenizers: 0.19.1

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}
}