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ABSA_review_game_geometry-aspect

Text Classification
setfit
Safetensors
sentence-transformers
bert
absa
generated_from_setfit_trainer
text-embeddings-inference
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SetFit Aspect Model

This is a SetFit model that can be used for Aspect Based Sentiment Analysis (ABSA). 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
  • 'level:sih game level nya sulit banget level clutter funk susah nya ampun level 11 nya level sulit nya level 10 sulit menyerah sabar banting hp saking sulit nya ya geometri dash kebanyakan level nya sulit mah geometri dash game stress saking susah nya'
  • 'iklan:iklan emang sih level iklan muncul masuk level mending kayak mah'
  • 'game:game apasih sampe strees gitu final boss level the tower susahnya ampun kenapasih kalo hijau pas udah nya jatuh mati sih cube nya cuman 1 hp ya ngeselin sih'
no aspect
  • 'sih game level:sih game level nya sulit banget level clutter funk susah nya ampun level 11 nya level sulit nya level 10 sulit menyerah sabar banting hp saking sulit nya ya geometri dash kebanyakan level nya sulit mah geometri dash game stress saking susah nya'
  • 'level clutter funk:sih game level nya sulit banget level clutter funk susah nya ampun level 11 nya level sulit nya level 10 sulit menyerah sabar banting hp saking sulit nya ya geometri dash kebanyakan level nya sulit mah geometri dash game stress saking susah nya'
  • 'level:sih game level nya sulit banget level clutter funk susah nya ampun level 11 nya level sulit nya level 10 sulit menyerah sabar banting hp saking sulit nya ya geometri dash kebanyakan level nya sulit mah geometri dash game stress saking susah nya'

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(
    "jetri20/ABSA_review_game_geometry-aspect",
    "jetri20/ABSA_review_game_geometry-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 2 23.5963 67
Label Training Sample Count
no aspect 754
aspect 321

Training Hyperparameters

  • batch_size: (4, 4)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 5
  • 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
0.0004 1 0.3713 -
0.0186 50 0.2045 -
0.0372 100 0.1548 -
0.0558 150 0.3116 -
0.0744 200 0.2066 -
0.0930 250 0.2932 -
0.1116 300 0.3138 -
0.1302 350 0.1258 -
0.1488 400 0.3442 -
0.1674 450 0.0558 -
0.1860 500 0.2819 -
0.2046 550 0.2211 -
0.2232 600 0.1269 -
0.2418 650 0.0098 -
0.2604 700 0.2395 -
0.2790 750 0.4382 -
0.2976 800 0.488 -
0.3162 850 0.6662 -
0.3348 900 0.1811 -
0.3534 950 0.2431 -
0.3720 1000 0.2032 -
0.3906 1050 0.0475 -
0.4092 1100 0.177 -
0.4278 1150 0.0556 -
0.4464 1200 0.3048 -
0.4650 1250 0.0015 -
0.4836 1300 0.0841 -
0.5022 1350 0.0105 -
0.5208 1400 0.0036 -
0.5394 1450 0.2296 -
0.5580 1500 0.0045 -
0.5766 1550 0.0134 -
0.5952 1600 0.0367 -
0.6138 1650 0.0044 -
0.6324 1700 0.0068 -
0.6510 1750 0.1408 -
0.6696 1800 0.0092 -
0.6882 1850 0.1926 -
0.7068 1900 0.0014 -
0.7254 1950 0.0003 -
0.7440 2000 0.2094 -
0.7626 2050 0.0329 -
0.7812 2100 0.0028 -
0.7999 2150 0.0144 -
0.8185 2200 0.1555 -
0.8371 2250 0.0005 -
0.8557 2300 0.0067 -
0.8743 2350 0.1485 -
0.8929 2400 0.0034 -
0.9115 2450 0.0044 -
0.9301 2500 0.2752 -
0.9487 2550 0.1342 -
0.9673 2600 0.0108 -
0.9859 2650 0.0106 -
1.0 2688 - 0.2236

Framework Versions

  • Python: 3.10.13
  • SetFit: 1.0.3
  • Sentence Transformers: 3.0.1
  • spaCy: 3.7.5
  • Transformers: 4.36.2
  • PyTorch: 2.1.2
  • Datasets: 2.19.2
  • 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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