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nazhan
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bge-large-en-v1.5-brahmaputra-iter-10-3rd

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
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metadata
base_model: BAAI/bge-large-en-v1.5
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: I don't want to handle any filtering tasks.
  - text: Show me all customers who have the last name 'Doe'.
  - text: What tables are available for data analysis in starhub_data_asset?
  - text: what do you think it is?
  - text: Provide data_asset_001_pcc product category details.
inference: true
model-index:
  - name: SetFit with BAAI/bge-large-en-v1.5
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.9818181818181818
            name: Accuracy

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

This is a SetFit model 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
Aggregation
  • 'Show me median Intangible Assets'
  • 'Can I have sum Cost_Entertainment?'
  • 'Get me min RevenueVariance_Actual_vs_Forecast.'
Lookup_1
  • 'Show me data_asset_kpi_cf details.'
  • 'Retrieve data_asset_kpi_cf details.'
  • 'Show M&A deal size by sector.'
Viewtables
  • 'What tables are included in the starhub_data_asset database that are required for performing a basic data analysis?'
  • 'What is the full list of tables available for use in queries within the starhub_data_asset database?'
  • 'What are the table names within the starhub_data_asset database that enable data analysis of customer feedback?'
Tablejoin
  • 'Is it possible to merge the Employees and Orders tables to see which employee handled each order?'
  • 'Join data_asset_001_ta with data_asset_kpi_cf.'
  • 'How can I connect the Customers and Orders tables to find customers who made purchases during a specific promotion?'
Lookup
  • 'Filter by customers who have placed more than 3 orders and get me their email addresses.'
  • "Filter by customers in the city 'New York' and show me their phone numbers."
  • "Can you filter by employees who work in the 'Research' department?"
Generalreply
  • "Oh, I just stepped outside and it's actually quite lovely! The sun is shining and there's a light breeze. How about you?"
  • "One of my short-term goals is to learn a new skill, like coding or cooking. I also want to save up enough money for a weekend trip with friends. How about you, any short-term goals you're working towards?"
  • 'Hey! My day is going pretty well, thanks for asking. How about yours?'
Rejection
  • 'I have no interest in generating more data.'
  • "I don't want to engage in filtering operations."
  • "I'd rather not filter this dataset."

Evaluation

Metrics

Label Accuracy
all 0.9818

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-iter-10-3rd")
# Run inference
preds = model("what do you think it is?")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 8.7137 62
Label Training Sample Count
Tablejoin 128
Rejection 73
Aggregation 222
Lookup 55
Generalreply 75
Viewtables 76
Lookup_1 157

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (1, 1)
  • max_steps: 2450
  • 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
0.0000 1 0.2001 -
0.0022 50 0.1566 -
0.0045 100 0.0816 -
0.0067 150 0.0733 -
0.0089 200 0.0075 -
0.0112 250 0.0059 -
0.0134 300 0.0035 -
0.0156 350 0.0034 -
0.0179 400 0.0019 -
0.0201 450 0.0015 -
0.0223 500 0.0021 -
0.0246 550 0.003 -
0.0268 600 0.0021 -
0.0290 650 0.0011 -
0.0313 700 0.0015 -
0.0335 750 0.0011 -
0.0357 800 0.001 -
0.0380 850 0.001 -
0.0402 900 0.0012 -
0.0424 950 0.0012 -
0.0447 1000 0.0011 -
0.0469 1050 0.0008 -
0.0491 1100 0.0009 -
0.0514 1150 0.001 -
0.0536 1200 0.0008 -
0.0558 1250 0.0011 -
0.0581 1300 0.0009 -
0.0603 1350 0.001 -
0.0625 1400 0.0007 -
0.0647 1450 0.0008 -
0.0670 1500 0.0007 -
0.0692 1550 0.001 -
0.0714 1600 0.0007 -
0.0737 1650 0.0007 -
0.0759 1700 0.0006 -
0.0781 1750 0.0008 -
0.0804 1800 0.0006 -
0.0826 1850 0.0005 -
0.0848 1900 0.0006 -
0.0871 1950 0.0005 -
0.0893 2000 0.0007 -
0.0915 2050 0.0005 -
0.0938 2100 0.0006 -
0.0960 2150 0.0007 -
0.0982 2200 0.0005 -
0.1005 2250 0.0008 -
0.1027 2300 0.0005 -
0.1049 2350 0.0008 -
0.1072 2400 0.0007 -
0.1094 2450 0.0007 0.0094
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.11.9
  • SetFit: 1.0.3
  • Sentence Transformers: 2.7.0
  • Transformers: 4.42.4
  • 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}
}