metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: i honestly thought impossible at this point i feel pretty
  - text: >-
      i feel convinced that im going to shy away from whatever is really good
      for me
  - text: i feel guilt that i should be more caring and im not
  - text: >-
      i found myself feeling nostalgic as i thought about the temporarily
      abandoned little bishop chronicles
  - text: i am feeling very indecisive and spontaneous
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: 0.5225
            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 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: sentence-transformers/paraphrase-mpnet-base-v2
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 6 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 feel so much better about that number' 'i feel like i have reached a plateau where im not buying as much as i use to and feeling more satisfied with my wardrobe and personal style' 'i feel especially thankful'
3	'i feel so violent just want to break some glass' 'i always feel rushed on the way to visit no comments' 'i think maybe about how strongly she feels about him and being there for him but brad looks really distracted'
5	'i feel like when i was a kid it was constantly impressed upon me how awesome ants are' 'i feel like it s a boy i would be pretty shocked if it was so somewhere in there my gut or my brain is saying girl' 'i feel like every day i walk around with so much stress and sadness that im literally amazed im still here that i still function that im still basically a friendly stable person'
0	'i would feel that a few words would be not only inadequate but a travesty' 'i attributed this depression to feeling inadequate against the unrealistic ideals of the lds church and while i still hold those ideals somewhat responsible i recognize this pattern of behavior' 'ive been resting and feeling generally unpleasant and queasy but in that frustrating background way where you dont feel right but cant place an exact cause'
4	'i was starting to feel scared for both of their safety and i wish those officers hadn t left no matter how much i hated them' 'i am already feeling frantic' 'i believe in you moment we all feel til then it s one more skeptical song'
2	'i do feel sympathetic to the parties involved now that their careers are down the drain' 'i like frappes and shit when im feeling naughty but i drink tea daily' 'i will pay a month for months and feel shame every time i grill a hot dog from that point on'

Evaluation

Metrics

Label	Accuracy
all	0.5225

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("vidhi0206/setfit-paraphrase-mpnet-emotion")
# Run inference
preds = model("i am feeling very indecisive and spontaneous")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	19.3333	48

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

Training Hyperparameters

batch_size: (8, 8)
num_epochs: (1, 1)
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
0.0042	1	0.3009	-
0.2083	50	0.1916	-
0.4167	100	0.0393	-
0.625	150	0.0129	-
0.8333	200	0.0034	-

Framework Versions

Python: 3.8.10
SetFit: 1.0.3
Sentence Transformers: 2.3.1
Transformers: 4.37.2
PyTorch: 2.2.0+cu121
Datasets: 2.17.0
Tokenizers: 0.15.1

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}
}