HelgeKn/Testing-blub

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

This is a SetFit model trained on the HelgeKn/SATHAME-generator-train dataset that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A SetFitHead 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/paraphrase-mpnet-base-v2
• Classification head: a SetFitHead instance
• Maximum Sequence Length: 512 tokens
• Number of Classes: 4 classes
• Training Dataset: HelgeKn/SATHAME-generator-train

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
3	'The art of change-ringing is peculiar to the English , and , like most English peculiarities , unintelligible to the rest of the world . ' 'Of all scenes that evoke rural England , this is one of the loveliest : An ancient stone church stands amid the fields , the sound of bells cascading from its tower , calling the faithful to evensong . ' 'In the tower , five men and women pull rhythmically on ropes attached to the same five bells that first sounded here in 1614 . '
1	'The parishioners of St. Michael and All Angels stop to chat at the church door , as members here always have . ' 'History , after all , is not on his side . ' "According to a nationwide survey taken a year ago , nearly a third of England 's church bells are no longer rung on Sundays because there is no one to ring them . "
2	'Now , only one local ringer remains : 64-year-old Derek Hammond . ' 'The others here today live elsewhere . ' 'No one speaks , and the snaking of the ropes seems to make as much sound as the bells themselves , muffled by the ceiling . '
0	'To ring for even one service at this tower , we have to scrape , says Mr. Hammond , a retired water-authority worker . `` ' 'When their changes are completed , and after they have worked up a sweat , ringers often skip off to the local pub , leaving worship for others below . ' "Two years ago , the Rev. Jeremy Hummerstone , vicar of Great Torrington , Devon , got so fed up with ringers who did n't attend service he sacked the entire band ; the ringers promptly set up a picket line in protest . "

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("HelgeKn/Testing-blub")
# Run inference
preds = model("The others here today live elsewhere . ")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	8	27.275	45

Label	Training Sample Count
0	10
1	10
2	10
3	10

Training Hyperparameters

• batch_size: (16, 16)
• num_epochs: (2, 2)
• 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
0.01	1	0.2799	-
0.5	50	0.1155	-
1.0	100	0.0023	-
1.5	150	0.0008	-
2.0	200	0.0017	-

Framework Versions

• Python: 3.9.13
• SetFit: 1.0.1
• Sentence Transformers: 2.2.2
• Transformers: 4.36.0
• PyTorch: 2.1.1+cpu
• Datasets: 2.15.0
• Tokenizers: 0.15.0

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}
}