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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:

  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 Sources

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}
}