metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: Buses are more simple - you just buy a ticket .
  - text: >-
      As citizens of village , we totally care about environment of our village
      .
  - text: >-
      So , finally I suggest that it would be a great idea to combine the
      different types of activities , both popular and the newest .
  - text: Had 12 years old .
  - text: >-
      On the other hand , I have the theoretical knowledge to use new the
      technologies this great project requires .
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
  - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.14782608695652175
            name: Accuracy

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: 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
3	'As a result , governments will invest more in researching the usage of these new types of energy , travelling using public transport will become much cheaper than personal car .' '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 ...' "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 ."
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 asked myself many times what is the aim of our life ?' 'My name is Eider and I am 21 years old . I had read your advertisement in the newspaper !'
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 .' 'It is a job with a lot of interesting aspects ,'
0	'The third section is to print the prepared bank notes .' "That 's why I order all of you to go there and feel the pleasure and have a try their own food ." 'No one can deny that the pollution issue is one of the utmost important thing which should be prevented .'
6	"The water was very cold and I could n't swim , then I played football in the sand of the beach ." '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 .' 'In modern societies , there are lots of friends around our daily lives .'
7	'However , people who use Facebook , Twitter or SMS in general , are not likely to have their own personal privacy that there is a possibility of cycle of bullying .' 'I been twelve years practice volleyball and because of it I knew lot of people who help me to grow up in the sport and life .' "What about you?What 's new in Brazil?As you know , my friend John asked me to help him with the organization at the concert , which was performed last month ."
4	'Something that they don know was that the whole thing was a movie !' "If we think about it the car is better because we do n't need to wait for them has we are waiting for the bus or underground but in another way car cust more money than the public transport ." 'Recently I saw the thriller of mokingjay part 2 .'
1	'If you want to see that movie , you need to watch the first 3 movies before to understand it .' '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 ."

Evaluation

Metrics

Label	Accuracy
all	0.1478

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-8")
# Run inference
preds = model("Had 12 years old .")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	20.7031	56

Label	Training Sample Count
0	8
1	8
2	8
3	8
4	8
5	8
6	8
7	8

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
0.0063	1	0.2999	-
0.3125	50	0.2097	-
0.625	100	0.0868	-
0.9375	150	0.0369	-
1.25	200	0.0208	-
1.5625	250	0.0049	-
1.875	300	0.0038	-

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}
}