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---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: bar:After really enjoying ourselves at the bar we sat down at a table and
had dinner.
- text: interior decor:this little place has a cute interior decor and affordable
city prices.
- text: cuisine:The cuisine from what I've gathered is authentic Taiwanese, though
its very different from what I've been accustomed to in Taipei.
- text: dining:Go here for a romantic dinner but not for an all out wow dining experience.
- text: Taipei:The cuisine from what I've gathered is authentic Taiwanese, though
its very different from what I've been accustomed to in Taipei.
inference: false
---
# SetFit Aspect Model with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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](https://www.sbert.net) 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 Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **spaCy Model:** en_core_web_lg
- **SetFitABSA Aspect Model:** [Davide1999/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect](https://huggingface.co/Davide1999/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect)
- **SetFitABSA Polarity Model:** [Davide1999/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity](https://huggingface.co/Davide1999/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:----------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| aspect | <ul><li>'staff:But the staff was so horrible to us.'</li><li>"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."</li><li>"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."</li></ul> |
| no aspect | <ul><li>"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."</li><li>"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."</li><li>"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."</li></ul> |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import AbsaModel
# Download from the 🤗 Hub
model = AbsaModel.from_pretrained(
"Davide1999/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect",
"Davide1999/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")
```
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## 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 | 0.3344 | - |
| 0.0370 | 50 | 0.2609 | - |
| 0.0740 | 100 | 0.1838 | - |
| 0.1109 | 150 | 0.0581 | - |
| 0.1479 | 200 | 0.0028 | - |
| 0.1849 | 250 | 0.0005 | - |
| 0.2219 | 300 | 0.0005 | - |
| 0.2589 | 350 | 0.0005 | - |
| 0.2959 | 400 | 0.0002 | - |
| 0.3328 | 450 | 0.0003 | - |
| 0.3698 | 500 | 0.0001 | - |
| 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 | 0.0 | - |
| 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 | 0.0001 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- spaCy: 3.7.5
- Transformers: 4.39.0
- PyTorch: 2.3.0+cu121
- Datasets: 2.20.0
- Tokenizers: 0.15.2
## Citation
### BibTeX
```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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