---
language: en
license: apache-2.0
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
datasets:
- tomaarsen/setfit-absa-semeval-restaurants
metrics:
- accuracy
widget:
- text: bottles of wine:bottles of wine are cheap and good.
- text: world:I also ordered the Change Mojito, which was out of this world.
- text: bar:We were still sitting at the bar while we drank the sangria, but facing
away from the bar when we turned back around, the $2 was gone the people next
to us said the bartender took it.
- text: word:word of advice, save room for pasta dishes and never leave until you've
had the tiramisu.
- text: bartender:We were still sitting at the bar while we drank the sangria, but
facing away from the bar when we turned back around, the $2 was gone the people
next to us said the bartender took it.
pipeline_tag: text-classification
inference: false
co2_eq_emissions:
emissions: 18.322516829847984
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.303
hardware_used: 1 x NVIDIA GeForce RTX 3090
base_model: BAAI/bge-small-en-v1.5
model-index:
- name: SetFit Aspect Model with BAAI/bge-small-en-v1.5 on SemEval 2014 Task 4 (Restaurants)
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: SemEval 2014 Task 4 (Restaurants)
type: tomaarsen/setfit-absa-semeval-restaurants
split: test
metrics:
- type: accuracy
value: 0.8623188405797102
name: Accuracy
---
# SetFit Aspect Model with BAAI/bge-small-en-v1.5 on SemEval 2014 Task 4 (Restaurants)
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [SemEval 2014 Task 4 (Restaurants)](https://huggingface.co/datasets/tomaarsen/setfit-absa-semeval-restaurants) dataset that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **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:** [tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-aspect](https://huggingface.co/tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-aspect)
- **SetFitABSA Polarity Model:** [tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-polarity](https://huggingface.co/tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-polarity)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
- **Training Dataset:** [SemEval 2014 Task 4 (Restaurants)](https://huggingface.co/datasets/tomaarsen/setfit-absa-semeval-restaurants)
- **Language:** en
- **License:** apache-2.0
### 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> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8623 |
## 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(
"tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-aspect",
"tomaarsen/setfit-absa-bge-small-en-v1.5-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 | 19.3576 | 45 |
| Label | Training Sample Count |
|:----------|:----------------------|
| no aspect | 170 |
| aspect | 255 |
### Training Hyperparameters
- batch_size: (256, 256)
- num_epochs: (5, 5)
- max_steps: 5000
- 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: True
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:----------:|:-------:|:-------------:|:---------------:|
| 0.0027 | 1 | 0.2498 | - |
| 0.1355 | 50 | 0.2442 | - |
| 0.2710 | 100 | 0.2462 | 0.2496 |
| 0.4065 | 150 | 0.2282 | - |
| 0.5420 | 200 | 0.0752 | 0.1686 |
| 0.6775 | 250 | 0.0124 | - |
| 0.8130 | 300 | 0.0128 | 0.1884 |
| 0.9485 | 350 | 0.0062 | - |
| 1.0840 | 400 | 0.0012 | 0.183 |
| 1.2195 | 450 | 0.0009 | - |
| 1.3550 | 500 | 0.0008 | 0.2072 |
| 1.4905 | 550 | 0.0031 | - |
| 1.6260 | 600 | 0.0006 | 0.1716 |
| 1.7615 | 650 | 0.0005 | - |
| **1.8970** | **700** | **0.0005** | **0.1666** |
| 2.0325 | 750 | 0.0005 | - |
| 2.1680 | 800 | 0.0004 | 0.2086 |
| 2.3035 | 850 | 0.0005 | - |
| 2.4390 | 900 | 0.0004 | 0.183 |
| 2.5745 | 950 | 0.0004 | - |
| 2.7100 | 1000 | 0.0036 | 0.1725 |
| 2.8455 | 1050 | 0.0004 | - |
| 2.9810 | 1100 | 0.0003 | 0.1816 |
| 3.1165 | 1150 | 0.0004 | - |
| 3.2520 | 1200 | 0.0003 | 0.1802 |
* The bold row denotes the saved checkpoint.
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.018 kg of CO2
- **Hours Used**: 0.303 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.9.16
- SetFit: 1.0.0.dev0
- Sentence Transformers: 2.2.2
- spaCy: 3.7.2
- Transformers: 4.29.0
- PyTorch: 1.13.1+cu117
- Datasets: 2.15.0
- Tokenizers: 0.13.3
## 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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