metadata
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: >-
pelayanan lambat pelayan kurang:pelayanan lambat pelayan kurang ajar dan
tidak sopan terlalu banyak ngerumpi ngobrol sesama pelayan akhirnya
kerjaan tidak pokus dan salah kasih pesanan sudah pelayan tidak bagus
pelayanya kurang ajar
- text: >-
batu bandung dengan tempat yang bagus &:Restoran cepat saji 24 jam di buah
batu bandung dengan tempat yang bagus & nyaman, pelayanan yang baik, dan
pelayanan yang cepat. Di sini untuk sarapan dan menghabiskan sekitar 40k
hingga 50k per orang. Saya ingin pergi ke sana lagi lain kali.
- text: >-
kentang gorengnya. rasanya sangat enak berbeda:Pengalaman luar biasa makan
di sini. Tidak hanya makanannya saja yang luar biasa. tempatnya sangat
nyaman untuk berkumpul bersama teman dan keluarga. Jangan lupa pesan
kentang gorengnya. rasanya sangat enak berbeda dengan kentang goreng di
tempat lain
- text: >-
Pelayanannya bagus dan makanannya:Pelayanannya bagus dan makanannya tidak
membosankan😊😊 …
- text: >-
luas. Untuk rasa seperti MCd biasa:Tempat makannya nyaman, lumayan besar,
pegawainya ramah. Tempat parkirnya sungguh luas. Untuk rasa seperti MCd
biasa, enak dan cukup enak. Waktu penyajiannya cukup cepat Menyukainya.
pipeline_tag: text-classification
inference: false
SetFit Polarity Model
This is a SetFit model that can be used for Aspect Based Sentiment Analysis (ABSA). A LogisticRegression 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:
- Fine-tuning a Sentence Transformer with contrastive learning.
- 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:
- Use a spaCy model to select possible aspect span candidates.
- Use a SetFit model to filter these possible aspect span candidates.
- Use this SetFit model to classify the filtered aspect span candidates.
Model Details
Model Description
- Model Type: SetFit
- Classification head: a LogisticRegression instance
- spaCy Model: id_core_news_trf
- SetFitABSA Aspect Model: kaylaisya/absa-aspect
- SetFitABSA Polarity Model: kaylaisya/absa-polarity
- Maximum Sequence Length: 8192 tokens
- Number of Classes: 3 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
| Label | Examples |
|---|---|
| positif |
|
| netral |
|
| negatif |
|
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import AbsaModel
# Download from the 🤗 Hub
model = AbsaModel.from_pretrained(
"kaylaisya/absa-aspect",
"kaylaisya/absa-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 | 6 | 27.2254 | 64 |
| Label | Training Sample Count |
|---|---|
| konflik | 0 |
| negatif | 12 |
| netral | 24 |
| positif | 758 |
Training Hyperparameters
- batch_size: (64, 64)
- num_epochs: (1, 1)
- 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
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- spaCy: 3.7.4
- Transformers: 4.36.2
- PyTorch: 2.3.0+cu121
- Datasets: 2.19.2
- Tokenizers: 0.15.2
Citation
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}
}