SetFit Aspect Model with sentence-transformers/paraphrase-mpnet-base-v2

This is a SetFit model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification. In particular, this model is in charge of filtering aspect span candidates.

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

Model Details

Model Description

Model Sources

Model Labels

Label Examples
aspect
  • 'staff:But the staff was so horrible to us.'
  • "food:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."
  • "food:The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not."
no aspect
  • "factor:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."
  • "deficiencies:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."
  • "Teodora:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."

Evaluation

Metrics

Label Accuracy
all 0.8780

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 ๐Ÿค— Hub
model = AbsaModel.from_pretrained(
    "tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect",
    "tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-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 4 17.9296 37
Label Training Sample Count
no aspect 71
aspect 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
0.0007 1 0.3388 -
0.0370 50 0.2649 -
0.0740 100 0.1562 -
0.1109 150 0.1072 -
0.1479 200 0.0021 -
0.1849 250 0.0007 -
0.2219 300 0.0008 -
0.2589 350 0.0003 -
0.2959 400 0.0002 -
0.3328 450 0.0003 -
0.3698 500 0.0002 -
0.4068 550 0.0001 -
0.4438 600 0.0001 -
0.4808 650 0.0001 -
0.5178 700 0.0001 -
0.5547 750 0.0001 -
0.5917 800 0.0001 -
0.6287 850 0.0002 -
0.6657 900 0.0001 -
0.7027 950 0.0001 -
0.7396 1000 0.0001 -
0.7766 1050 0.0001 -
0.8136 1100 0.0001 -
0.8506 1150 0.0001 -
0.8876 1200 0.0001 -
0.9246 1250 0.0001 -
0.9615 1300 0.0001 -
0.9985 1350 0.0 -

Environmental Impact

Carbon emissions were measured using CodeCarbon.

  • Carbon Emitted: 0.009 kg of CO2
  • Hours Used: 0.111 hours

Training Hardware

  • On Cloud: No
  • GPU Model: 1 x NVIDIA GeForce RTX 3090
  • CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
  • RAM Size: 31.78 GB

Framework Versions

  • Python: 3.9.16
  • SetFit: 1.0.0.dev0
  • Sentence Transformers: 2.2.2
  • spaCy: 3.7.2
  • Transformers: 4.29.0
  • PyTorch: 1.13.1+cu117
  • Datasets: 2.15.0
  • Tokenizers: 0.13.3

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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