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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
positive
  • ' Brides a la mode pow wow first thing this morning This past weekends lovely wedding fresh in my mind pics soon '
  • 'My mom just came home and she FINALLY got me a guitar strap yay '
  • ' LaMont yr very young looking dude'
neutral
  • 'Hates untalented being mean to my talented friends'
  • ' quite'
  • 'eating some breakfast at Panera Bread boring cloudy weather lil drizzle'
negative
  • 'Ok Im frustrated there is hella dust between the screens of my blackberry'
  • 'I honestly hate what I have said to some ppl sometimes sorry for makin an of myself to anyone '
  • ' Oh final msg Why didnt you review my boardgame BookchaseA AA12 when you were on telly We didnt even get a nice letter '

Evaluation

Metrics

Label Accuracy
all 0.632

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("subham18/setfit-paraphrase-mpnet-base-v2-twitter-sentiment-cleaned")
# Run inference
preds = model(" oh ok thanks")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 2 14.2083 26
Label Training Sample Count
Negative 0
Positive 0
Neutral 0

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (4, 4)
  • 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: True

Training Results

Epoch Step Training Loss Validation Loss
0.0417 1 0.3272 -
1.0 24 - 0.2372
2.0 48 - 0.2126
2.0833 50 0.0164 -
3.0 72 - 0.2097
4.0 96 - 0.2105
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.12.3
  • SetFit: 1.0.3
  • Sentence Transformers: 3.0.1
  • Transformers: 4.39.0
  • PyTorch: 2.4.0+cu121
  • Datasets: 2.21.0
  • 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}
}
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