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---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: yg sama. Rasanya konsisten dari dulu:Kalo ke Bandung, wajib banget nyobain
makan siang disini. Tempatnya selalu ramee walau cabangnya ada bbrp di 1 jalan
yg sama. Rasanya konsisten dari dulu mah, enakkk! Ayam bakar sama sayur asem wajib
dipesen. Dan sambelnya yg selalu juara pedesnya, siap2 keringetan
- text: jam lebih dan tempatnya panas. Makanannya:Di satu deretan ada 3 warung bu
imas dan rame semua Nunggu makan dateng sekitar 1 jam lebih dan tempatnya panas.
Makanannya sebenarnya enak2 semua tapi kalo harus antri lama dan temptnya kurang
oke mending cari warung makan sunda lain
- text: Dari makanan yang luar biasa:Dari makanan yang luar biasa, hingga suasana
yang hangat, hingga layanan yang ramah, tempat lingkungan pusat kota ini tidak
ketinggalan.
- text: Favorite sambal terasi dadak di Bandung sejauh:Favorite sambal terasi dadak
di Bandung sejauh ini Harganya pun ramah. Next time balik lagi.
- text: ayam goreng/ati-ampela goreng gurih asinnya pas:Rasa ayam goreng/ati-ampela
goreng gurih asinnya pas, sayur asem yang isinya banyak dan ras asam-manisnya
nyambung, dan sambal leunca-nya enak beutullll.... Pakai petai dan tempe/tahu
lebih sempurna.
pipeline_tag: text-classification
inference: false
model-index:
- name: SetFit Polarity Model
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.8636363636363636
name: Accuracy
---
# SetFit Polarity Model
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). 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 classifying aspect polarities.
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 a SetFit model to filter these possible aspect span candidates.
3. **Use this SetFit model to classify the filtered aspect span candidates.**
## Model Details
### Model Description
- **Model Type:** SetFit
<!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **spaCy Model:** id_core_news_trf
- **SetFitABSA Aspect Model:** [pahri/setfit-indo-resto-RM-ibu-imas-aspect](https://huggingface.co/pahri/setfit-indo-resto-RM-ibu-imas-aspect)
- **SetFitABSA Polarity Model:** [pahri/setfit-indo-resto-RM-ibu-imas-polarity](https://huggingface.co/pahri/setfit-indo-resto-RM-ibu-imas-polarity)
- **Maximum Sequence Length:** 8192 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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 |
|:---------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| positive | <ul><li>'air krispi dan ayam bakar:Warung Sunda murah meriah dan makanannya enak. Favorit selada air krispi dan ayam bakar'</li><li>'Ayam bakar,sambel leunca:Ayam bakar,sambel leunca sambel terasi merah enak banget 9/10, perkedel jagung 8/10 makan pakai sambel mantap. Makan berdua sekitar 77k'</li><li>',sambel leunca sambel terasi merah enak banget 9:Ayam bakar,sambel leunca sambel terasi merah enak banget 9/10, perkedel jagung 8/10 makan pakai sambel mantap. Makan berdua sekitar 77k'</li></ul> |
| negative | <ul><li>', minus di menu tidak di cantumkan:Makanan biasa saja, minus di menu tidak di cantumkan harga. Posi nasi standar, kelebihan sambal sudah disediakan di mangkok. '</li><li>'lebih diatur kah antriannya, kayanya pakai:It wasnt bad food at all. Tapi please mungkin bisa lebih diatur kah antriannya, kayanya pakai waiting list gak sesulit itu deh.'</li><li>'rasanya standar. Harga bisa dibilang murah:Tahu tempe perkedel rasanya standar. Harga bisa dibilang murah. Kalau yang masih penasaran ya boleh dateng coba tapi menurut saya overall biasa saja, tidak nemu wah nya dimana..'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8636 |
## 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(
"pahri/setfit-indo-resto-RM-ibu-imas-aspect",
"pahri/setfit-indo-resto-RM-ibu-imas-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 | 7 | 35.3922 | 90 |
| Label | Training Sample Count |
|:--------|:----------------------|
| konflik | 0 |
| negatif | 0 |
| netral | 0 |
| positif | 0 |
### Training Hyperparameters
- batch_size: (6, 6)
- 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: True
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0036 | 1 | 0.2676 | - |
| 0.1799 | 50 | 0.0064 | - |
| 0.3597 | 100 | 0.0015 | - |
| 0.5396 | 150 | 0.0007 | - |
| 0.7194 | 200 | 0.0005 | - |
| 0.8993 | 250 | 0.0006 | - |
### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- spaCy: 3.7.4
- Transformers: 4.36.2
- 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}
}
```
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