metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
base_model: BAAI/bge-small-en-v1.5
metrics:
  - accuracy
widget:
  - text: >-
      Thank you for your outreach. Currently, our priorities are focused
      elsewhere, and we are not considering new solutions. I would be open to
      revisiting this conversation in [insert timeframe, e.g., 6 months]. Please
      follow up then.
  - text: >-
      Appreciate your email. However, I'm not actively looking into [product
      category] right now. Please check back with me in [insert timeframe, e.g.,
      6 months] for a reassessment.
  - text: >-
      I recently moved to a new apartment. How can I update my address for my
      renter's insurance policy?
  - text: Can you provide an update on the status of my insurance claim?
  - text: I have a new mailing address. Please update it for my records.
pipeline_tag: text-classification
inference: false
model-index:
  - name: SetFit with BAAI/bge-small-en-v1.5
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.8461538461538461
            name: Accuracy

SetFit with BAAI/bge-small-en-v1.5

This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-small-en-v1.5 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:

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: BAAI/bge-small-en-v1.5
Classification head: a MultiOutputClassifier 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

Evaluation

Metrics

Label	Accuracy
all	0.8462

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("Can you provide an update on the status of my insurance claim?")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	14.3077	37

Training Hyperparameters

batch_size: (32, 32)
num_epochs: (1, 1)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 0
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
0.0152	1	0.2404	-
0.7576	50	0.0375	-
1.0	66	-	0.0347

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.8.4
SetFit: 1.0.3
Sentence Transformers: 2.2.2
Transformers: 4.40.2
PyTorch: 2.2.2
Datasets: 2.16.0
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}
}