HelgeKn/BEA2019-multi-class-4

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:

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: 8 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
7	"When I 've had a very bad and stressful day I can relax doing karate , because It 's the kind of sport that it is n't very hard ." "Also , you 'll meet friendly people who usually ask to you something to be friends and change your telephone number ." 'When I have spare time , I often gather my friends to watch basketball match on television .'
4	"stop shouting . do n't shout ." 'Yours Sincerely .' 'Something that they don know was that the whole thing was a movie !'
1	'She stay sleeping in the bed and doing nothing all day .' 'People collects trash of their house and await the trash truck that carried the trash to a landfill located outside the village .' "Travelling by car is n't so much more convenient unless it is so much more comfortable , but actually we do n't think about the contamination in our planet ."
6	'When the concert finished , we went to cloakroom to get signatures from musicians .' 'We have solar panels and a place to make compost at the last garden , with worms who eat and degrade all the organic waste of the school .' 'The aim of this report is to give you my personal point of view of the course I did in your branch in Madrid last month .'
5	'You can also bought a lot of gifts like key chains , statue , or what else memories to be made before returning to Malaysia .' 'I always said that I passed that test and I was sure of that .' 'In addition , to decrease the risk of negative comments or posts , Facebook and Twitter would improve their futures to solve the less personal privacy problem .'
2	'They were not only really clever people but also excellent co - workers .' 'On balance , learning foreign languages is very positive on different aspect , so if you have the positivity of learning a new language do it , because it will bring you many benefits .' 'In many years of work I have honed my skills in managing non - standard situations , analyzing the problem , finding and implementing practical and easy solutions .'
0	'It is very funny .' 'In China , English is took to be a foreign language which many students choose to learn .' 'We also value that they have specialised studies in Cloud technology , and hosting management .'
3	"Usually there are generation problems , sons do n't understand parents and vicecersa , but dialoging and listening emotions and facts , everyone can have another point of view ." 'the two boys heard that he was planing to steal some money and kill people so the boys start their adventure on stoping Injuin Joe ...' 'As an example , if you are able to get alone with your travel companion could enjoy each moment of the trip , exchange some pictures , eat together , and visit places with common interest such as museums or malls .'

Evaluation

Metrics

Label	Accuracy
all	0.1315

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/BEA2019-multi-class-4")
# Run inference
preds = model("Had 12 years old .")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	19.1562	42

Label	Training Sample Count
0	4
1	4
2	4
3	4
4	4
5	4
6	4
7	4

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
0.0125	1	0.1886	-
0.625	50	0.0778	-
1.25	100	0.0194	-
1.875	150	0.0069	-

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}
}