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:

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-large-en-v1.5
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 2 classes
Training Dataset: nazhan/qa-lookup-dataset-iter-1

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
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}
}