SetFit with sentence-transformers/all-MiniLM-L6-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-MiniLM-L6-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:
- 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 Sources
Model Labels
Label |
Examples |
0 |
- 'Why is Coca-Cola losing share?'
- 'which pack segment is contributing most to share change for Resto in Orizaba NCBs'
- 'What is KOF market share in 2021, and how has it changed over the past year For TT OP Cuernavaca'
|
1 |
- 'share the sales for Breezefizz en 2023 jun'
- 'what is ROI trend for Fizzy drinks?'
- 'What is the market share of KOF in Orizaba for FY22?'
|
Evaluation
Metrics
Label |
Accuracy |
all |
0.8667 |
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
model = SetFitModel.from_pretrained("vgarg/query_type_classifier_v2")
preds = model("How has the csd industry evolved in the last two years?")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
5 |
12.9324 |
32 |
Label |
Training Sample Count |
0 |
42 |
1 |
32 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- 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 |
0.0054 |
1 |
0.3438 |
- |
0.2703 |
50 |
0.2209 |
- |
0.5405 |
100 |
0.0806 |
- |
0.8108 |
150 |
0.0048 |
- |
1.0811 |
200 |
0.0048 |
- |
1.3514 |
250 |
0.0025 |
- |
1.6216 |
300 |
0.0026 |
- |
1.8919 |
350 |
0.0022 |
- |
2.1622 |
400 |
0.0017 |
- |
2.4324 |
450 |
0.0009 |
- |
2.7027 |
500 |
0.0015 |
- |
2.9730 |
550 |
0.001 |
- |
Framework Versions
- Python: 3.12.2
- SetFit: 1.0.3
- Sentence Transformers: 2.6.1
- Transformers: 4.39.3
- PyTorch: 2.2.2+cpu
- Datasets: 2.18.0
- Tokenizers: 0.15.2
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}
}