SetFit
This is a SetFit model that can be used for Text Classification. 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
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 128 tokens
- Number of Classes: 44 classes
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 |
|---|---|
| Shopping / electronics & multimedia |
|
| Other / kids |
|
| Bank services / other |
|
| Housing / rent |
|
| Transportation / other |
|
| Bank services / transfers |
|
| Investment / retirement & savings |
|
| Other / taxes |
|
| Healthy & Beauty / other |
|
| Investment / securities |
|
| Housing / other |
|
| Housing / house loan |
|
| Housing / utilities & bills |
|
| Bank services / general fees |
|
| Leisure & Entertainment / culture & events |
|
| Transportation / taxi & carpool |
|
| Shopping / other |
|
| Recurrent Payments / loans |
|
| Healthy & Beauty / doctor fees |
|
| Bank services / withdrawal |
|
| Other / other |
|
| Healthy & Beauty / pharmacy |
|
| Transportation / fuel |
|
| Shopping / sporting goods |
|
| Food & Drinks / groceries |
|
| Other / pets |
|
| Investment / real estate |
|
| Shopping / clothing |
|
| Shopping / housing equipment |
|
| Transportation / maitenance |
|
| Recurrent Payments / other |
|
| Recurrent Payments / insurance |
|
| Healthy & Beauty / veterinary |
|
| Transportation / public transportation |
|
| Healthy & Beauty / beauty & self-care |
|
| Leisure & Entertainment / other |
|
| Food & Drinks / eating out |
|
| Housing / services & maintenance |
|
| Leisure & Entertainment / travel |
|
| Leisure & Entertainment / sports & hobbies |
|
| Investment / other |
|
| Transportation / car loan & leasing |
|
| Recurrent Payments / subscription |
|
| Food & Drinks / other |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.25 |
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("HEN10/setfit-particular-transaction-solon-embeddings-labels-large-kaggle-automatisation-v1")
# Run inference
preds = model("achat académie dressage canin carte")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 3 | 6.0455 | 10 |
| Label | Training Sample Count |
|---|---|
| Housing / rent | 2 |
| Housing / house loan | 2 |
| Housing / utilities & bills | 2 |
| Housing / services & maintenance | 2 |
| Housing / other | 2 |
| Food & Drinks / groceries | 2 |
| Food & Drinks / eating out | 2 |
| Food & Drinks / other | 2 |
| Leisure & Entertainment / sports & hobbies | 2 |
| Leisure & Entertainment / culture & events | 2 |
| Leisure & Entertainment / travel | 2 |
| Leisure & Entertainment / other | 2 |
| Transportation / car loan & leasing | 2 |
| Transportation / fuel | 2 |
| Transportation / public transportation | 2 |
| Transportation / taxi & carpool | 2 |
| Transportation / maitenance | 2 |
| Transportation / other | 2 |
| Recurrent Payments / loans | 2 |
| Recurrent Payments / insurance | 2 |
| Recurrent Payments / subscription | 2 |
| Recurrent Payments / other | 2 |
| Investment / securities | 2 |
| Investment / retirement & savings | 2 |
| Investment / real estate | 2 |
| Investment / other | 2 |
| Shopping / clothing | 2 |
| Shopping / electronics & multimedia | 2 |
| Shopping / sporting goods | 2 |
| Shopping / housing equipment | 2 |
| Shopping / other | 2 |
| Healthy & Beauty / doctor fees | 2 |
| Healthy & Beauty / pharmacy | 2 |
| Healthy & Beauty / beauty & self-care | 2 |
| Healthy & Beauty / veterinary | 2 |
| Healthy & Beauty / other | 2 |
| Bank services / transfers | 2 |
| Bank services / withdrawal | 2 |
| Bank services / general fees | 2 |
| Bank services / other | 2 |
| Other / taxes | 2 |
| Other / kids | 2 |
| Other / pets | 2 |
| Other / other | 2 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- 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: True
- use_amp: False
- warmup_proportion: 0.1
- seed: 6
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0021 | 1 | 0.1662 | - |
| 0.1057 | 50 | 0.1483 | - |
| 0.2114 | 100 | 0.0681 | - |
| 0.3171 | 150 | 0.0298 | - |
| 0.4228 | 200 | 0.0245 | - |
| 0.5285 | 250 | 0.0117 | - |
| 0.6342 | 300 | 0.032 | - |
| 0.7400 | 350 | 0.0112 | - |
| 0.8457 | 400 | 0.0072 | - |
| 0.9514 | 450 | 0.0176 | - |
Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 2.6.1
- Transformers: 4.39.3
- PyTorch: 2.1.2
- Datasets: 2.17.0
- 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}
}
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