harshita23sh/setfit-model-intent-classification-insurance

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:

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/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 'My old health policy from another insurance provider is no longer in effect.'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}
}