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:
- Fine-tuning a Sentence Transformer with contrastive learning.
- 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:
- Use a spaCy model to select possible aspect span candidates.
- Use a SetFit model to filter these possible aspect span candidates.
- Use this SetFit model to classify the filtered aspect span candidates.
Model Details
Model Description
- Model Type: SetFit
- Sentence Transformer body: cointegrated/rubert-tiny2
- Classification head: a LogisticRegression instance
- spaCy Model: ru_core_news_lg
- SetFitABSA Aspect Model: isolation-forest/setfit-absa-aspect
- SetFitABSA Polarity Model: isolation-forest/setfit-absa-polarity
- Maximum Sequence Length: 2048 tokens
- Number of Classes: 2 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
Label | Examples |
---|---|
Positive |
|
Negative |
|
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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