SetFit Polarity Model with sentence-transformers/all-mpnet-base-v2
This is a SetFit model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses sentence-transformers/all-mpnet-base-v2 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: sentence-transformers/all-mpnet-base-v2
- Classification head: a LogisticRegression instance
- spaCy Model: id_core_news_trf
- SetFitABSA Aspect Model: Funnyworld1412/ABSA_mpnet_MiniLM-L6-aspect
- SetFitABSA Polarity Model: Funnyworld1412/ABSA_mpnet_MiniLM-L6-polarity
- Maximum Sequence Length: 384 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 |
---|---|
negatif |
|
positif |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.8551 |
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(
"Funnyworld1412/ABSA_mpnet_MiniLM-L6-aspect",
"Funnyworld1412/ABSA_mpnet_MiniLM-L6-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 | 28.3626 | 83 |
Label | Training Sample Count |
---|---|
negatif | 738 |
positif | 528 |
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 |
---|---|---|---|
0.0003 | 1 | 0.2658 | - |
0.0158 | 50 | 0.148 | - |
0.0316 | 100 | 0.3023 | - |
0.0474 | 150 | 0.2508 | - |
0.0632 | 200 | 0.2206 | - |
0.0790 | 250 | 0.151 | - |
0.0948 | 300 | 0.2409 | - |
0.1106 | 350 | 0.2025 | - |
0.1264 | 400 | 0.1535 | - |
0.1422 | 450 | 0.1796 | - |
0.1580 | 500 | 0.0979 | - |
0.1738 | 550 | 0.2438 | - |
0.1896 | 600 | 0.221 | - |
0.2054 | 650 | 0.0416 | - |
0.2212 | 700 | 0.2027 | - |
0.2370 | 750 | 0.0484 | - |
0.2528 | 800 | 0.0606 | - |
0.2686 | 850 | 0.2689 | - |
0.2844 | 900 | 0.0505 | - |
0.3002 | 950 | 0.4393 | - |
0.3160 | 1000 | 0.1642 | - |
0.3318 | 1050 | 0.0824 | - |
0.3476 | 1100 | 0.3209 | - |
0.3633 | 1150 | 0.459 | - |
0.3791 | 1200 | 0.0042 | - |
0.3949 | 1250 | 0.2194 | - |
0.4107 | 1300 | 0.0088 | - |
0.4265 | 1350 | 0.0092 | - |
0.4423 | 1400 | 0.2324 | - |
0.4581 | 1450 | 0.0007 | - |
0.4739 | 1500 | 0.0004 | - |
0.4897 | 1550 | 0.0006 | - |
0.5055 | 1600 | 0.0232 | - |
0.5213 | 1650 | 0.2382 | - |
0.5371 | 1700 | 0.0154 | - |
0.5529 | 1750 | 0.0014 | - |
0.5687 | 1800 | 0.0817 | - |
0.5845 | 1850 | 0.0038 | - |
0.6003 | 1900 | 0.2101 | - |
0.6161 | 1950 | 0.0003 | - |
0.6319 | 2000 | 0.0003 | - |
0.6477 | 2050 | 0.0019 | - |
0.6635 | 2100 | 0.0003 | - |
0.6793 | 2150 | 0.0004 | - |
0.6951 | 2200 | 0.0004 | - |
0.7109 | 2250 | 0.0002 | - |
0.7267 | 2300 | 0.2335 | - |
0.7425 | 2350 | 0.0009 | - |
0.7583 | 2400 | 0.0004 | - |
0.7741 | 2450 | 0.018 | - |
0.7899 | 2500 | 0.0002 | - |
0.8057 | 2550 | 0.0003 | - |
0.8215 | 2600 | 0.0628 | - |
0.8373 | 2650 | 0.0002 | - |
0.8531 | 2700 | 0.0006 | - |
0.8689 | 2750 | 0.0003 | - |
0.8847 | 2800 | 0.0002 | - |
0.9005 | 2850 | 0.0008 | - |
0.9163 | 2900 | 0.0001 | - |
0.9321 | 2950 | 0.0001 | - |
0.9479 | 3000 | 0.0003 | - |
0.9637 | 3050 | 0.0001 | - |
0.9795 | 3100 | 0.0007 | - |
0.9953 | 3150 | 0.0003 | - |
1.0 | 3165 | - | 0.2173 |
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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Base model
sentence-transformers/all-mpnet-base-v2