---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: spumoni ices:It also has great ice cream and spumoni ices.
- text: place:its a cool place to come with a bunch of people or with a date for maybe
a mild dinner or some drinks.
- text: care:The Food Despite a menu that seems larger than the restaurant, great
care goes into the preparation of every dish.
- text: peoples:Upon entering, I was impressed by the room while the food on other
peoples' tables seemed enticing.
- text: group:As if that wasnt enough, after another in the group mentioned that a
portion of the sushi on her plate was not what she had ordered, the waiter came
back with chopsticks and started to remove it (as she was eating!)
pipeline_tag: text-classification
inference: false
base_model: sentence-transformers/paraphrase-mpnet-base-v2
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: 0.9680851063829787
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:** [NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect](https://huggingface.co/NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect)
- **SetFitABSA Polarity Model:** [NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-polarity](https://huggingface.co/NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-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 |
|:----------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| aspect | - "food:It might be the best sit down food I've had in the area, so if you are going to the upright citizen brigade, or the garden, it could be just the place for you."
- "place:It might be the best sit down food I've had in the area, so if you are going to the upright citizen brigade, or the garden, it could be just the place for you."
- 'service:Though the service might be a little slow, the waitresses are very friendly.'
|
| no aspect | - "sit:It might be the best sit down food I've had in the area, so if you are going to the upright citizen brigade, or the garden, it could be just the place for you."
- "area:It might be the best sit down food I've had in the area, so if you are going to the upright citizen brigade, or the garden, it could be just the place for you."
- "citizen brigade:It might be the best sit down food I've had in the area, so if you are going to the upright citizen brigade, or the garden, it could be just the place for you."
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9681 |
## 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(
"NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect",
"NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-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 | 8 | 26.6069 | 52 |
| Label | Training Sample Count |
|:----------|:----------------------|
| no aspect | 229 |
| aspect | 33 |
### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0003 | 1 | 0.2315 | - |
| 0.0149 | 50 | 0.2637 | - |
| 0.0297 | 100 | 0.1795 | - |
| 0.0446 | 150 | 0.1164 | - |
| 0.0595 | 200 | 0.0131 | - |
| 0.0744 | 250 | 0.0036 | - |
| 0.0892 | 300 | 0.0004 | - |
| 0.1041 | 350 | 0.0003 | - |
| 0.1190 | 400 | 0.0001 | - |
| 0.1338 | 450 | 0.0002 | - |
| 0.1487 | 500 | 0.0001 | - |
| 0.1636 | 550 | 0.0001 | - |
| 0.1785 | 600 | 0.0001 | - |
| 0.1933 | 650 | 0.0001 | - |
| 0.2082 | 700 | 0.0 | - |
| 0.2231 | 750 | 0.0001 | - |
| 0.2380 | 800 | 0.0001 | - |
| 0.2528 | 850 | 0.0 | - |
| 0.2677 | 900 | 0.0001 | - |
| 0.2826 | 950 | 0.0003 | - |
| 0.2974 | 1000 | 0.0008 | - |
| 0.3123 | 1050 | 0.0001 | - |
| 0.3272 | 1100 | 0.0 | - |
| 0.3421 | 1150 | 0.0 | - |
| 0.3569 | 1200 | 0.0 | - |
| 0.3718 | 1250 | 0.0 | - |
| 0.3867 | 1300 | 0.0 | - |
| 0.4015 | 1350 | 0.0 | - |
| 0.4164 | 1400 | 0.0 | - |
| 0.4313 | 1450 | 0.0 | - |
| 0.4462 | 1500 | 0.0 | - |
| 0.4610 | 1550 | 0.0 | - |
| 0.4759 | 1600 | 0.0 | - |
| 0.4908 | 1650 | 0.0 | - |
| 0.5057 | 1700 | 0.0 | - |
| 0.5205 | 1750 | 0.0 | - |
| 0.5354 | 1800 | 0.0 | - |
| 0.5503 | 1850 | 0.0 | - |
| 0.5651 | 1900 | 0.0 | - |
| 0.5800 | 1950 | 0.0 | - |
| 0.5949 | 2000 | 0.0 | - |
| 0.6098 | 2050 | 0.0 | - |
| 0.6246 | 2100 | 0.0 | - |
| 0.6395 | 2150 | 0.0 | - |
| 0.6544 | 2200 | 0.0 | - |
| 0.6692 | 2250 | 0.0 | - |
| 0.6841 | 2300 | 0.0 | - |
| 0.6990 | 2350 | 0.0 | - |
| 0.7139 | 2400 | 0.0 | - |
| 0.7287 | 2450 | 0.0 | - |
| 0.7436 | 2500 | 0.0 | - |
| 0.7585 | 2550 | 0.0 | - |
| 0.7733 | 2600 | 0.0 | - |
| 0.7882 | 2650 | 0.0 | - |
| 0.8031 | 2700 | 0.0 | - |
| 0.8180 | 2750 | 0.0 | - |
| 0.8328 | 2800 | 0.0 | - |
| 0.8477 | 2850 | 0.0 | - |
| 0.8626 | 2900 | 0.0 | - |
| 0.8775 | 2950 | 0.0 | - |
| 0.8923 | 3000 | 0.0 | - |
| 0.9072 | 3050 | 0.0 | - |
| 0.9221 | 3100 | 0.0 | - |
| 0.9369 | 3150 | 0.0 | - |
| 0.9518 | 3200 | 0.0 | - |
| 0.9667 | 3250 | 0.0 | - |
| 0.9816 | 3300 | 0.0 | - |
| 0.9964 | 3350 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.4.0
- spaCy: 3.7.4
- Transformers: 4.37.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.1
- 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}
}
```