hojzas/proj9

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

This is a SetFit model trained on the hojzas/proj9-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:

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: 2 classes
• Training Dataset: hojzas/proj9-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	' async with aiohttp.ClientSession() as session:\n tasks = [fetch_url(session, url) for url in urls]\n return await asyncio.gather(*tasks)' ' tasks = [download_url(url) for url in urls]\n results = await asyncio.gather(*tasks)\n return results' ' async with ClientSession() as client_session:\n tasks = [asyncio.create_task(fetch_single_url(client_session, url)) for url in urls]\n results = await asyncio.gather(*tasks)\n return results'
1	' coros = [get_url(url) for url in urls]\n results = asyncio.get_event_loop().run_until_complete(asyncio.gather(*coros))\n return results' ' with aiohttp.ClientSession() as client:\n tasks = [retrieve_data(client, target) for target in urls]\n outcomes = asyncio.gather(*tasks)\n return outcomes' 'tasks = [asyncio.create_task(fetch_single_url(url)) for url in urls]\n results = asyncio.gather(*tasks)\n return results'

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/proj9")
# Run inference
preds = model("    tasks = [download_url(url) for url in urls]\n    results = asyncio.gather(*tasks)\n    return results")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	18	37.7333	76

Label	Training Sample Count
0	8
1	7

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
0.0263	1	0.3316	-

Framework Versions

• Python: 3.10.12
• SetFit: 1.0.3
• Sentence Transformers: 2.7.0
• Transformers: 4.40.2
• PyTorch: 2.3.0+cu121
• Datasets: 2.19.1
• Tokenizers: 0.19.1

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}
}