hojzas/proj4-uniq_srt-lab2

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:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. 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.

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

@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}
}