metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
base_model: Omar-Nasr/setfitmodel
metrics:
  - accuracy
widget:
  - text: ' Like jumping into a pool, or starting an assignment'
  - text: ' That''s kind of the nature of my volunteer work, but you could volunteer with a food bank or boys  and girls club, which would involve more social interaction Just breaking that cycle by going for a short walk around the neighbourhood is a good idea'
  - text: ' And you will have the confidence inside of you, so you wont have to worry about the outside so much'
  - text: ' Do your make up then, get out of that hotel room and take your notes with you! Go for a walk, try to focus on your senses (the smells, the sounds, the winds and the temperature, possible the sun burning your skin)'
  - text: >-
      I would disagree as I usually read people well and could see that he was
      not comfortable talking with me, in the first lunch break he left after 5
      minutes and said he wanted to take a walk around the building
pipeline_tag: text-classification
inference: true
model-index:
  - name: SetFit with Omar-Nasr/setfitmodel
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.4666666666666667
            name: Accuracy

SetFit with Omar-Nasr/setfitmodel

This is a SetFit model that can be used for Text Classification. This SetFit model uses Omar-Nasr/setfitmodel as the Sentence Transformer embedding model. A LogisticRegression 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: Omar-Nasr/setfitmodel
Classification head: a LogisticRegression instance
Maximum Sequence Length: 256 tokens
Number of Classes: 4 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
1.0	' Go out for a walk once a day additionally and slowly start increasing the time you spend outside Go out for a walk once a day additionally and slowly start increasing the time you spend outside Start doing sport, either outdoors or at a gym If you can, try to take your dog to a dog park or something like that' " Now I'm not saying to go to a party on the spot, just go out, shop, take a walk in the park, that kind of thing Now I'm not saying to go to a party on the spot, just go out, shop, take a walk in the park, that kind of thing" ' antidepressants, therapy, meditation, walking in nature (these work for me idk about other people) antidepressants, therapy, meditation, walking in nature (these work for me idk about other people) '
2.0	'Try playing soccer haha' " I didn't go outside too much" " But we can't sleep in a hotel, we have go camping"
3.0	" I don't know anyone in the city, I don't think I have enough courage to go outside alone" " But even then, I didn't have any other problems outside school I still had no friends at European school, I haven't had any walks which I had constantly with my friends back in Ukraine" " For the past two months I've been doing my usual; not going outside just watching South Park and only going out when someone drags me For the past two months I've been doing my usual; not going outside just watching South Park and only going out when someone drags me"
0.0	' I would do this too but then I run the risk of meeting someone I know lol' " Does it matter you are alone? NO Does it matter you can't run the full marathon at top speed? NO Are you useless? NO Seriously nobody is going to pay attention to you and think you are a lonely loser" ' If anything you should be thinking about wearing sun screen so you retain your good skin as it becomes your ally as you age outside'

Evaluation

Metrics

Label	Accuracy
all	0.4667

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("Omar-Nasr/setfitmodel")
# Run inference
preds = model(" Like jumping into a pool, or starting an assignment")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	49.85	1083

Label	Training Sample Count
0.0	20
1.0	20
2.0	20
3.0	20

Training Hyperparameters

batch_size: (8, 8)
num_epochs: (1, 1)
max_steps: -1
sampling_strategy: oversampling
body_learning_rate: (2e-05, 1e-05)
head_learning_rate: 0.01
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
0.0017	1	0.0931	-
0.0833	50	0.001	-
0.1667	100	0.0002	-
0.25	150	0.0	-
0.3333	200	0.0	-
0.4167	250	0.0	-
0.5	300	0.0	-
0.5833	350	0.0	-
0.6667	400	0.0	-
0.75	450	0.0	-
0.8333	500	0.0	-
0.9167	550	0.0	-
1.0	600	0.0	-

Framework Versions

Python: 3.10.13
SetFit: 1.0.3
Sentence Transformers: 2.7.0
Transformers: 4.39.3
PyTorch: 2.1.2
Datasets: 2.18.0
Tokenizers: 0.15.2

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}
}