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 SetFitHead 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 SetFitHead instance
- spaCy Model: en_core_web_trf
- SetFitABSA Aspect Model: setfit-absa-aspect
- SetFitABSA Polarity Model: MattiaTintori/Final_polarity_Colab
- Maximum Sequence Length: 384 tokens
- Number of Classes: 3 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 |
---|---|
1 |
|
2 |
|
0 |
|
Evaluation
Metrics
Label | F1 |
---|---|
all | 0.8170 |
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(
"setfit-absa-aspect",
"MattiaTintori/Final_polarity_Colab",
)
# 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 | 1 | 25.0463 | 79 |
Label | Training Sample Count |
---|---|
0 | 1148 |
1 | 607 |
2 | 489 |
Training Hyperparameters
- batch_size: (64, 4)
- num_epochs: (5, 32)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- body_learning_rate: (5e-05, 5e-05)
- head_learning_rate: 0.04
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: True
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0014 | 1 | 0.3084 | - |
0.0285 | 20 | 0.2735 | 0.2591 |
0.0570 | 40 | 0.2228 | 0.2351 |
0.0855 | 60 | 0.2071 | 0.1993 |
0.1140 | 80 | 0.1522 | 0.1696 |
0.1425 | 100 | 0.1441 | 0.1671 |
0.1709 | 120 | 0.1632 | 0.161 |
0.1994 | 140 | 0.0966 | 0.1575 |
0.2279 | 160 | 0.1737 | 0.1504 |
0.2564 | 180 | 0.1092 | 0.1671 |
0.2849 | 200 | 0.1314 | 0.1459 |
0.3134 | 220 | 0.0972 | 0.1483 |
0.3419 | 240 | 0.1014 | 0.1537 |
0.3704 | 260 | 0.0506 | 0.1514 |
0.3989 | 280 | 0.0817 | 0.143 |
0.4274 | 300 | 0.0592 | 0.1526 |
0.4558 | 320 | 0.0311 | 0.1562 |
0.4843 | 340 | 0.038 | 0.1546 |
0.5128 | 360 | 0.0852 | 0.1497 |
0.5413 | 380 | 0.0359 | 0.144 |
0.5698 | 400 | 0.0449 | 0.1639 |
0.5983 | 420 | 0.0314 | 0.1517 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- spaCy: 3.7.6
- Transformers: 4.39.0
- PyTorch: 2.4.0+cu121
- Datasets: 2.21.0
- 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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Base model
sentence-transformers/all-mpnet-base-v2