---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-mpnet-base-v2
metrics:
- accuracy
widget:
- text: food portions:The food portions are quite filling, but not too much.
- text: waiters:The waiters are quite alert in helping customers, but cannot always
answer all questions in detail.
- text: experience:The atmosphere here is pleasant, although it doesn't provide an
extraordinary experience.
- text: food:The food does not have a distinctive taste.
- text: restaurant atmosphere:The restaurant atmosphere is too stiff and unpleasant.
pipeline_tag: text-classification
inference: false
model-index:
- name: SetFit Aspect Model with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 1.0
name: Accuracy
---
# 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:** [models/en-setfit-absa-model-aspect](https://huggingface.co/models/en-setfit-absa-model-aspect)
- **SetFitABSA Polarity Model:** [models/en-setfit-absa-model-polarity](https://huggingface.co/models/en-setfit-absa-model-polarity)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
### 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 |
|:----------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| no aspect | - 'food:The food is really delicious! The meat is tender and the spices are well seasoned. I will definitely come back again.'
- 'meat:The food is really delicious! The meat is tender and the spices are well seasoned. I will definitely come back again.'
- 'spices:The food is really delicious! The meat is tender and the spices are well seasoned. I will definitely come back again.'
|
| aspect | - 'Service:Service is standard, nothing extraordinary.'
- 'Service:Service from the staff is very friendly.'
- 'Service:Service from the staff is very fast and professional.'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 1.0 |
## 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(
"models/en-setfit-absa-model-aspect",
"models/en-setfit-absa-model-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 | 4 | 14.3487 | 72 |
| Label | Training Sample Count |
|:----------|:----------------------|
| no aspect | 1701 |
| aspect | 14 |
### Training Hyperparameters
- batch_size: (4, 4)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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 |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0001 | 1 | 0.34 | - |
| 0.0029 | 50 | 0.318 | - |
| 0.0058 | 100 | 0.2344 | - |
| 0.0087 | 150 | 0.1925 | - |
| 0.0117 | 200 | 0.1893 | - |
| 0.0146 | 250 | 0.014 | - |
| 0.0175 | 300 | 0.0017 | - |
| 0.0204 | 350 | 0.0041 | - |
| 0.0233 | 400 | 0.0008 | - |
| 0.0262 | 450 | 0.0008 | - |
| 0.0292 | 500 | 0.0003 | - |
| 0.0321 | 550 | 0.0003 | - |
| 0.0350 | 600 | 0.0004 | - |
| 0.0379 | 650 | 0.0004 | - |
| 0.0408 | 700 | 0.0004 | - |
| 0.0437 | 750 | 0.0008 | - |
| 0.0466 | 800 | 0.0004 | - |
| 0.0496 | 850 | 0.0002 | - |
| 0.0525 | 900 | 0.0003 | - |
| 0.0554 | 950 | 0.0001 | - |
| 0.0583 | 1000 | 0.0001 | - |
| 0.0612 | 1050 | 0.0002 | - |
| 0.0641 | 1100 | 0.0002 | - |
| 0.0671 | 1150 | 0.0002 | - |
| 0.0700 | 1200 | 0.0001 | - |
| 0.0729 | 1250 | 0.0002 | - |
| 0.0758 | 1300 | 0.0001 | - |
| 0.0787 | 1350 | 0.0 | - |
| 0.0816 | 1400 | 0.0001 | - |
| 0.0845 | 1450 | 0.0001 | - |
| 0.0875 | 1500 | 0.0001 | - |
| 0.0904 | 1550 | 0.0001 | - |
| 0.0933 | 1600 | 0.0001 | - |
| 0.0962 | 1650 | 0.0001 | - |
| 0.0991 | 1700 | 0.0 | - |
| 0.1020 | 1750 | 0.0001 | - |
| 0.1050 | 1800 | 0.0001 | - |
| 0.1079 | 1850 | 0.0001 | - |
| 0.1108 | 1900 | 0.0001 | - |
| 0.1137 | 1950 | 0.0 | - |
| 0.1166 | 2000 | 0.0001 | - |
| 0.1195 | 2050 | 0.0001 | - |
| 0.1224 | 2100 | 0.0 | - |
| 0.1254 | 2150 | 0.0006 | - |
| 0.1283 | 2200 | 0.0002 | - |
| 0.1312 | 2250 | 0.0 | - |
| 0.1341 | 2300 | 0.0 | - |
| 0.1370 | 2350 | 0.2106 | - |
| 0.1399 | 2400 | 0.0 | - |
| 0.1429 | 2450 | 0.0001 | - |
| 0.1458 | 2500 | 0.0001 | - |
| 0.1487 | 2550 | 0.0 | - |
| 0.1516 | 2600 | 0.0 | - |
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| 0.1662 | 2850 | 0.0001 | - |
| 0.1691 | 2900 | 0.0 | - |
| 0.1720 | 2950 | 0.0 | - |
| 0.1749 | 3000 | 0.0 | - |
| 0.1778 | 3050 | 0.0001 | - |
| 0.1808 | 3100 | 0.0 | - |
| 0.1837 | 3150 | 0.0 | - |
| 0.1866 | 3200 | 0.0001 | - |
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| 0.1924 | 3300 | 0.0001 | - |
| 0.1953 | 3350 | 0.0001 | - |
| 0.1983 | 3400 | 0.0 | - |
| 0.2012 | 3450 | 0.0 | - |
| 0.2041 | 3500 | 0.0 | - |
| 0.2070 | 3550 | 0.0 | - |
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| 0.2245 | 3850 | 0.0 | - |
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| 0.2332 | 4000 | 0.0 | - |
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| 0.4869 | 8350 | 0.0001 | - |
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| 0.8863 | 15200 | 0.0 | - |
| 0.8892 | 15250 | 0.0 | - |
| 0.8921 | 15300 | 0.0 | - |
| 0.8950 | 15350 | 0.0 | - |
| 0.8980 | 15400 | 0.0 | - |
| 0.9009 | 15450 | 0.0 | - |
| 0.9038 | 15500 | 0.0 | - |
| 0.9067 | 15550 | 0.0 | - |
| 0.9096 | 15600 | 0.0 | - |
| 0.9125 | 15650 | 0.0 | - |
| 0.9155 | 15700 | 0.0 | - |
| 0.9184 | 15750 | 0.0 | - |
| 0.9213 | 15800 | 0.0 | - |
| 0.9242 | 15850 | 0.0 | - |
| 0.9271 | 15900 | 0.0 | - |
| 0.9300 | 15950 | 0.0 | - |
| 0.9329 | 16000 | 0.0 | - |
| 0.9359 | 16050 | 0.0 | - |
| 0.9388 | 16100 | 0.0 | - |
| 0.9417 | 16150 | 0.0 | - |
| 0.9446 | 16200 | 0.0 | - |
| 0.9475 | 16250 | 0.0 | - |
| 0.9504 | 16300 | 0.0 | - |
| 0.9534 | 16350 | 0.0 | - |
| 0.9563 | 16400 | 0.0 | - |
| 0.9592 | 16450 | 0.0 | - |
| 0.9621 | 16500 | 0.0 | - |
| 0.9650 | 16550 | 0.0 | - |
| 0.9679 | 16600 | 0.0 | - |
| 0.9708 | 16650 | 0.0 | - |
| 0.9738 | 16700 | 0.0 | - |
| 0.9767 | 16750 | 0.0 | - |
| 0.9796 | 16800 | 0.0 | - |
| 0.9825 | 16850 | 0.0 | - |
| 0.9854 | 16900 | 0.0 | - |
| 0.9883 | 16950 | 0.0 | - |
| 0.9913 | 17000 | 0.0 | - |
| 0.9942 | 17050 | 0.0 | - |
| 0.9971 | 17100 | 0.0 | - |
| 1.0 | 17150 | 0.0 | - |
### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- spaCy: 3.7.4
- Transformers: 4.39.3
- PyTorch: 2.1.2
- Datasets: 2.18.0
- 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}
}
```