Efficient Few-Shot Learning Without Prompts
Paper • 2209.11055 • Published • 7
SetFit with Hcompany/NeoMME-260M-Retriever-ST-dense
This is a SetFit model that can be used for Text Classification. This SetFit model uses Hcompany/NeoMME-260M-Retriever-ST-dense 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: Hcompany/NeoMME-260M-Retriever-ST-dense
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 16384 tokens
- Number of Classes: 16 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 |
|---|---|
| questionnaire |
|
| invoice |
|
| advertisement |
|
| scientific publication |
|
| letter |
|
| file folder |
|
| form |
|
|
|
| budget |
|
| specification |
|
| news article |
|
| scientific report |
|
| handwritten |
|
| resume |
|
| presentation |
|
| memo |
|
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from PIL import Image
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("oneryalcin/rvl-cdip-setfit-neomme")
# Run inference on images
preds = model([Image.open("example.png")])
Training Details
Training Hyperparameters
- batch_size: (4, 4)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 5
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0031 | 1 | 0.1619 | - |
| 0.1562 | 50 | 0.2096 | - |
| 0.3125 | 100 | 0.1618 | - |
| 0.4688 | 150 | 0.1933 | - |
| 0.625 | 200 | 0.1195 | - |
| 0.7812 | 250 | 0.1068 | - |
| 0.9375 | 300 | 0.0836 | - |
Framework Versions
- Python: 3.12.12
- SetFit: 1.3.0.dev0
- Sentence Transformers: 6.0.1
- Transformers: 5.17.0
- PyTorch: 2.14.0+cu130
- Datasets: 5.0.1
- Tokenizers: 0.23.2
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}
}
Evaluation (jordyvl/rvl_cdip_100_examples_per_class, split test)
accuracy 0.532, macro F1 0.516 on 400 images; 8 training images per class; body Hcompany/NeoMME-260M-Retriever-ST-dense, task document; trained in 769s on cuda.
precision recall f1-score support
advertisement 0.56 0.76 0.64 25
budget 0.41 0.28 0.33 25
email 0.64 0.56 0.60 25
file folder 0.54 0.76 0.63 25
form 0.41 0.48 0.44 25
handwritten 0.83 0.76 0.79 25
invoice 0.33 0.24 0.28 25
letter 0.58 0.60 0.59 25
memo 0.30 0.32 0.31 25
news article 0.50 0.64 0.56 25
presentation 0.33 0.24 0.28 25
questionnaire 0.38 0.36 0.37 25
resume 1.00 0.92 0.96 25
scientific publication 0.61 0.76 0.68 25
scientific report 0.43 0.12 0.19 25
specification 0.53 0.72 0.61 25
accuracy 0.53 400
macro avg 0.52 0.53 0.52 400
weighted avg 0.52 0.53 0.52 400
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