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

This is a SetFit model 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:

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/paraphrase-mpnet-base-v2
Classification head: a SetFitHead instance
Maximum Sequence Length: 512 tokens
Number of Classes: 7 classes

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
6	'3 -RRB- Republican congressional representatives , because of their belief in a minimalist state , are less willing to engage in local benefit-seeking than are Democratic members of Congress . ' 'That is the way the system works . ' 'Duck swarms . '
2	'It explains how the Committee for Medicinal Products for Veterinary Use ( CVMP ) assessed the studies performed , to reach their recommendations on how to use the medicine . ' 'Tricks such as those of Alonso and Ramos before the Ajax demonstrate wittiness but not the will to get remove of a sanction . ' 'The next day , Sunday , the hangover reminded Haney where he had been the night before . '
3	'If it is , it will be treated as an operator , if it is not , it will be treated as a user function . ' 'Back in the chase car , we drove around some more , got stuck in a ditch , enlisted the aid of a local farmer to get out the trailer hitch and pull us out of the ditch . ' "It was the most exercise we 'd had all morning and it was followed by our driving immediately to the nearest watering hole . "
5	'The discovery of a strange bacteria that can use arsenic as one of its nutrients widens the scope for finding new forms of life on Earth and possibly beyond . ' 'I felt the temblor begin and glanced at the table next to mine , smiled that guilty smile and we both mouthed the words , `Earth-quake !` together . ' 'Already two major pharmaceutical companies , the Squibb unit of Bristol-Myers Squibb Co. and Hoffmann-La Roche Inc. , are collaborating with gene hunters to turn the anticipated cascade of discoveries into predictive tests and , maybe , new therapies . '
0	'Prior to 1932 , the pattern was nearly the opposite . ' 'A minor contrast to Costa Rica , comparing the 22 players called by both countries for the friendly game today , at 3:05 pm at the National Stadium in San Jose . ' 'Never in my life have I been so frightened . '
4	'`To ring for even one service at this tower , we have to scrape ,` says Mr. Hammond , a retired water-authority worker . `'</li><li>'It is a passion that usually stays in the tower , however . '</li><li>'One writer , signing his letter as` Red-blooded , balanced male , `remarked on the` frequency of women fainting in peals , `and suggested that they` settle back into their traditional role of making tea at meetings . `` '
1	'Bribe by bribe , Mr. Sternberg and his co-author , Matthew C. Harrison Jr. , lead us along the path Wedtech traveled , from its inception as a small manufacturing company to the status of full-fledged defense contractor , entrusted with the task of producing vital equipment for the Army and Navy . ' "kalgebra 's console is useful as a calculator . " 'Then a wild thought ran circles through his clouded brain . '

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/SemEval-multi-class-10")
# Run inference
preds = model("To break the uncomfortable silence , Haney began to talk . ")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	28.1286	74

Label	Training Sample Count
0	10
1	10
2	10
3	10
4	10
5	10
6	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
0.0057	1	0.2488	-
0.2857	50	0.2041	-
0.5714	100	0.1094	-
0.8571	150	0.0478	-
1.1429	200	0.0378	-
1.4286	250	0.0089	-
1.7143	300	0.0036	-
2.0	350	0.0029	-

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}
}

HelgeKn
/

SemEval-multi-class-10