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pupugu02/
absa-setfit-resto-polarity

Name: absa-setfit-resto-polarity
Availability: InStock

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

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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: pupugu02/absa-setfit-resto-aspect
SetFitABSA Polarity Model: pupugu02/absa-setfit-resto-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	'time, selain tempat yang Sangat bersih:Mcd selalu jadi tempat ternyaman untuk me time, selain tempat yang Sangat bersih dan nyaman, mcdonals juga selalu menjaga kualitas makanan. Bagi saya mcd sangat affdorable dan worth it, selain itu paling digemari oleh kalangan anak muda dan anak anak sangat menyukai ayam nya.' 'bagus dan ada tempat buat bermain anak2:Makanan nya enak n harga nya juga murah.. View nya bagus dan ada tempat buat bermain anak2.. Naek beca ato perahu angsa..' 'produknya, memberikan pelayanan yang memuaskan,:McDonald’s adalah menjadi restoran cepat saji dengan pelayanan terbaik di dunia. Untuk mencapai visi ini, McDonald’s selalu menjamin mutu produk-produknya, memberikan pelayanan yang memuaskan, menawarkan kebersihan dan keamanan produk …'
negatif	"\nKopi jelly terasa agak 'cawerang:Gang drive-thru sangat sempit sehingga Anda harus ekstra hati-hati.\nLayanan yang pasti cepat dan berbagai pilihan pembayaran.\nKopi jelly terasa agak 'cawerang' tapi okelah. Mereka juga menambahkan segel plastik untuk mencegah tumpah. Pemikiran yang bagus." 'maap banget yaaaa pelayanan nya lama bgt:aduh maap banget yaaaa pelayanan nya lama bgt ga kaya gacoan2 yg lainn, dr smua gacoan yg pernah dine in cuma ini paling lama ,' 'suka terlamat dan pelayanannya kurang bagus..:Tempatnya bersih sejuk cocok buat makan sambil bersantai... Tetapi kalo kondisi ramai pesanan suka terlamat dan pelayanannya kurang bagus.. untuk kondisi sepi masih aman pelayanan bagus ramah..'
netral	'Banyak lalat. Rasanya biasa. Yg:Banyak lalat. Rasanya biasa. Yg lumayan sop iga bakar madu' 'Ada harga, ada rasa:Ada harga, ada rasa' 'D ini, tempat nya menjorok kedalam:Tiba di Bandung, kita mampir di restoran Mc D ini, tempat nya menjorok kedalam, tatanan design nya Mc D semua standard sesuai dengan kapasitas lahan nya. Disini memiliki tempat bermain anak-anak, dan untuk order mereka menyediakan mesin …'

Uses

Direct Use for Inference

First install the SetFit library:

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(
    "pupugu02/absa-setfit-resto-aspect",
    "pupugu02/absa-setfit-resto-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	3	28.0911	62

Label	Training Sample Count
konflik	0
negatif	15
netral	28
positif	363

Training Hyperparameters

batch_size: (128, 128)
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: 3.0.0
spaCy: 3.7.4
Transformers: 4.36.2
PyTorch: 2.3.0+cu121
Datasets: 2.19.2
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}
}