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metadata
base_model: BAAI/bge-large-en-v1.5
datasets:
  - nazhan/qa-lookup-dataset-iter-1
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: Get me the first names of employees working in the 'Legal' department.
  - text: >-
      Provide the value of the export tariff paid on shipments to 'Country Z' in
      2024.
  - text: >-
      Show me the value of the freight charges for the shipment made on October
      10, 2023.
  - text: >-
      Show me the value of the refund issued to 'Customer K' for a defective
      product.
  - text: Provide the value of the environmental compliance cost for 2023.
inference: true
model-index:
  - name: SetFit with BAAI/bge-large-en-v1.5
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: nazhan/qa-lookup-dataset-iter-1
          type: nazhan/qa-lookup-dataset-iter-1
          split: test
        metrics:
          - type: accuracy
            value: 1
            name: Accuracy

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

This is a SetFit model trained on the nazhan/qa-lookup-dataset-iter-1 dataset that can be used for Text Classification. This SetFit model uses BAAI/bge-large-en-v1.5 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 Sources

Model Labels

Label Examples
Lookup
  • 'Get me the list of customers who placed their first order in 2024.'
  • "Filter by products in the 'Gadgets' category and show me their prices."
  • 'Get me the email addresses of customers who have made a purchase.'
qa
  • 'Provide the value of the accrued vacation liability as of June 2023.'
  • 'Show me the value of the courier service charges for November 2023.'
  • "Provide the value of the consulting contract with 'Client N' finalized in 2023."

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("nazhan/bge-large-en-v1.5-brahmaputra-qa-lookup-iter-1-2-epoch")
# Run inference
preds = model("Provide the value of the environmental compliance cost for 2023.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 8 12.8309 19
Label Training Sample Count
Lookup 65
qa 71

Training Hyperparameters

  • batch_size: (32, 32)
  • num_epochs: (2, 2)
  • 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
0.0034 1 0.1823 -
0.1701 50 0.0031 -
0.3401 100 0.0012 -
0.5102 150 0.0011 -
0.6803 200 0.0009 -
0.8503 250 0.0008 -
1.0 294 - 0.0004
1.0204 300 0.0008 -
1.1905 350 0.0008 -
1.3605 400 0.0007 -
1.5306 450 0.0006 -
1.7007 500 0.0006 -
1.8707 550 0.0006 -
2.0 588 - 0.0003
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.11.9
  • SetFit: 1.1.0.dev0
  • Sentence Transformers: 3.0.1
  • Transformers: 4.44.2
  • PyTorch: 2.4.0+cu121
  • Datasets: 2.21.0
  • 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}
}