This is a SetFit model trained on the hojzas/proj4-label 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-label

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	" perms = all_permutations_substrings(string)\n return set(''.join(perm) for word in words for perm in perms if word == perm)" ' perms = all_permutations_substrings(string)\n out = set()\n for w in words:\n for s in perms:\n if w == s:\n out.add(w)\n return out' ' perms = all_permutations_substrings(string)\n return set(word for word in words if word in perms)'
1	' perms = all_permutations_substrings(string)\n return perms.intersection(words)' ' perms = all_permutations_substrings(string)\n return set.intersection(perms,words)' ' perms = all_permutations_substrings(string)\n return set(perms).intersection(words)'

Evaluation

Metrics

Label	Accuracy
all	0.9375

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/setfit-proj4-label")
# Run inference
preds = model("    perms = all_permutations_substrings(string)
    
return perms.intersection(words)")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	12	29.1633	140

Label	Training Sample Count
0	35
1	14

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
0.0081	1	0.3668	-
0.4065	50	0.0048	-
0.8130	100	0.0014	-

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Carbon Emitted: 0.000 kg of CO2
Hours Used: 0.006 hours

Training Hardware

On Cloud: No
GPU Model: No GPU used
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}
}