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hojzas/
proj4-uniq_orig_order-lab1

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SetFit with sentence-transformers/all-mpnet-base-v2

This is a SetFit model trained on the hojzas/proj4-uniq_orig_order-lab1 dataset that can be used for Text Classification. This SetFit model uses sentence-transformers/all-mpnet-base-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 Type: SetFit
Sentence Transformer body: sentence-transformers/all-mpnet-base-v2
Classification head: a LogisticRegression instance
Maximum Sequence Length: 384 tokens
Number of Classes: 2 classes
Training Dataset: hojzas/proj4-uniq_orig_order-lab1

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
0	' outputSequence = []\n for input in it:\n found = 0\n for output in outputSequence:\n if output == input:\n found = 1\n break\n if not found:\n outputSequence.append(input)\n return outputSequence' ' uniq = []\n for char in it:\n if not char in uniq:\n uniq.append(char)\n return uniq' 'return sorted(set(it), key=lambda y: it.index(y)) '
1	'return [tmp for tmp in dict.fromkeys(it).keys()]' 'return [i for i in dict.fromkeys(it)]' 'return list(dict.fromkeys(it))'

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

python

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("hojzas/proj4-uniq_orig_order-lab1")
# Run inference
preds = model("return list(dict.fromkeys(it))")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	20.9524	111

Label	Training Sample Count
0	13
1	8

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (1, 1)
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
0.0189	1	0.1783	-
0.9434	50	0.0013	-

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Carbon Emitted: 0.001 kg of CO2
Hours Used: 0.002 hours

Training Hardware

On Cloud: No
GPU Model: 4 x NVIDIA RTX A5000
CPU Model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
RAM Size: 251.49 GB

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 2.2.2
Transformers: 4.36.1
PyTorch: 2.1.2+cu121
Datasets: 2.14.7
Tokenizers: 0.15.1

Citation

BibTeX

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}
}