metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: I know you are searching for a flat to live for the whole next year .
  - text: Dear sir Dimara .
  - text: I have been doing Judo for the past 11 years with a lot of prizes .
  - text: >-
      my village is the place where I live so I am trying to keep its
      environment non - polluted and valid for life .
  - text: >-
      I learnt from Research that you can do everything in anytime in addition a
      little tired can change the life for the better .
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: 0.175
            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
1	'I consider that is more convenient to drive a car because you carry on more things in your own car than travelling by car .' 'In the last few years forensic biology has developed many aspects like better sensibility , robustness of results and less time required for analyze a sample , but what struck me most is how fast this change happens .' "The car is n't the best way for for the transport , because it produce much pollution , however the public transport is better to do a journey ."
6	'On the one hand travel by car are really much more convenient as give the chance to you to be independent .' 'When most people think about an important historical place in Italy , they think of Duomo , in Milano .' 'I like personality with childlike , so I like children .'
5	'Yours sincerely ,' 'This practice is considered those activities that anyone can do without any kind of special preparation .' 'Secondly , the public vehicle route are more far than usual route .'
7	'This conclusion become more prominent if we look into the data of the car companies and exponential growth in their sales figure and with low budget private cars in picture , scenario ddrastically changed in past 10 years' 'Recently I saw the thriller of mokingjay part 2 .' "An example of that is the marriage of homosexual where some state admit this marriage , others do n't ."
3	'After that , the sports day began formally .' 'In those years I lived the worst moments in my life .' 'On the one hand , in my country there are a lot of place to travel .'
2	"Sharing houses or rooms have many advantages such as , cheap , safe , close to the university , and learn how to share everything with others . saving money and time will be more Obvious in university dormitories because monthly payments will be less than four times than hiring an apartment , and because it will be closer to the university , saving money and time is more efficient by reducing transportation 's costs" 'So , finally I suggest that it would be a great idea to combine the different types of activities , both popular and the newest .' 'Wszysycy residents of my village , they try to , so that our village was clear that pollute the environment as little as possible .'
4	'During summer I love to go to the beach and having sunbathing with my friends other than getting fun with them playing volleyball or run inside the water of the sea !' 'Jose is the best song . he is singing and talking in the party .' "She fell sleep again , didn't she ?"
0	'I work for the same large company for 25 years , now is the time to change and find new job opportunities .' 'A problem which was caused by us , human beings , with their target of making money without thinking of the effects .' 'He was waiting 2 hours for her .'

Evaluation

Metrics

Label	Accuracy
all	0.175

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-20")
# Run inference
preds = model("Dear sir Dimara .")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	22.0	82

Label	Training Sample Count
0	20
1	20
2	20
3	20
4	20
5	20
6	20
7	20

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
0.0025	1	0.3724	-
0.125	50	0.2732	-
0.25	100	0.3001	-
0.375	150	0.2525	-
0.5	200	0.1934	-
0.625	250	0.1164	-
0.75	300	0.0874	-
0.875	350	0.0624	-
1.0	400	0.052	-
1.125	450	0.0569	-
1.25	500	0.0248	-
1.375	550	0.0071	-
1.5	600	0.0124	-
1.625	650	0.0087	-
1.75	700	0.0086	-
1.875	750	0.066	-
2.0	800	0.0194	-

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}
}