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hojzas/
proj4-uniq_srt-lab2

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

This is a SetFit model trained on the hojzas/proj4-uniq_srt-lab2 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: 3 classes
Training Dataset: hojzas/proj4-uniq_srt-lab2

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	' it = list(dict.fromkeys(it))\n it.sort()\n return it' ' sequence = []\n for i in it:\n if i in sequence:\n pass\n else:\n sequence.append(i)\n sequence.sort()\n return sequence' ' unique = list(set(it))\n unique.sort()\n return unique'
2	'return sorted(list({word : it.count(word) for (word) in set(it)}.keys())) ' 'return list(dict.fromkeys(sorted(it)))' 'return sorted((list(dict.fromkeys(it)))) '
1	' unique_items = set(it)\n return sorted(list(unique_items))' ' letters = set(it)\n sorted_letters = sorted(letters)\n return sorted_letters' 'return list(sorted(set(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_srt-lab2")
# Run inference
preds = model("it=sorted(set(list(it)))
    return it")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	20.7778	117

Label	Training Sample Count
0	10
1	9
2	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
0.0147	1	0.2285	-
0.7353	50	0.0208	-

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Carbon Emitted: 0.001 kg of CO2
Hours Used: 0.003 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}
}