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SetFit Polarity Model with cointegrated/rubert-tiny2

This is a SetFit model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses cointegrated/rubert-tiny2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification. In particular, this model is in charge of classifying aspect polarities.

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.

This model was trained within the context of a larger system for ABSA, which looks like so:

  1. Use a spaCy model to select possible aspect span candidates.
  2. Use a SetFit model to filter these possible aspect span candidates.
  3. Use this SetFit model to classify the filtered aspect span candidates.

Model Details

Model Description

Model Sources

Model Labels

Label Examples
Positive
  • 'И порции " достойные ":И порции " достойные " .'
  • 'Салаты вообще оказались вкуснейшими:Салаты вообще оказались вкуснейшими .'
  • 'порадовала , большая пивная тарелка , действительно оказалась:Кухня порадовала , большая пивная тарелка , действительно оказалась большой и вкусной !'
Negative
  • 'Потом официантка как будто пропала:Потом официантка как будто пропала , было не дозваться , чтобы что - то дозаказать , очень долго приходилось ждать , в итоге посчитали неправильно , в счет внесли на 2 пункта больше , чем мы заказывали .'
  • 'Обслуживание не впечатлило .:Обслуживание не впечатлило .'
  • 'приятно удивлена " китайским интерьером " - диванчики:Была приятно удивлена " китайским интерьером " - диванчики как в бистро , скатерти на столах по типу а - ля столовая , европейские светильники / люстры , в общем в плане интерьера китайского никакого абсолютно !'

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 AbsaModel

# Download from the 🤗 Hub
model = AbsaModel.from_pretrained(
    "isolation-forest/setfit-absa-aspect",
    "isolation-forest/setfit-absa-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 3 28.4766 92
Label Training Sample Count
Negative 128
Positive 128

Training Hyperparameters

  • batch_size: (16, 2)
  • num_epochs: (1, 16)
  • 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
0.0005 1 0.2196 -
0.0242 50 0.2339 -
0.0484 100 0.2258 -
0.0727 150 0.246 -
0.0969 200 0.1963 -
0.1211 250 0.18 -
0.1453 300 0.1176 -
0.1696 350 0.0588 -
0.1938 400 0.0482 -
0.2180 450 0.1131 -
0.2422 500 0.0134 -
0.2665 550 0.0415 -
0.2907 600 0.0144 -
0.3149 650 0.012 -
0.3391 700 0.0091 -
0.3634 750 0.0055 -
0.3876 800 0.0054 -
0.4118 850 0.0055 -
0.4360 900 0.0072 -
0.4603 950 0.0094 -
0.4845 1000 0.0054 -
0.5087 1050 0.0045 -
0.5329 1100 0.003 -
0.5572 1150 0.0067 -
0.5814 1200 0.0041 -
0.6056 1250 0.0048 -
0.6298 1300 0.0053 -
0.6541 1350 0.0048 -
0.6783 1400 0.0038 -
0.7025 1450 0.0037 -
0.7267 1500 0.0031 -
0.7510 1550 0.0038 -
0.7752 1600 0.0032 -
0.7994 1650 0.0039 -
0.8236 1700 0.0032 -
0.8479 1750 0.0023 -
0.8721 1800 0.0029 -
0.8963 1850 0.0041 -
0.9205 1900 0.0026 -
0.9448 1950 0.0027 -
0.9690 2000 0.0035 -
0.9932 2050 0.003 -

Framework Versions

  • Python: 3.10.13
  • SetFit: 1.0.3
  • Sentence Transformers: 2.7.0
  • spaCy: 3.7.2
  • 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}
}
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