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najwaa/
absa-combined-p2-polarity

Name: absa-combined-p2-polarity
Availability: InStock

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

sentence-transformers/all-mpnet-base-v2

finetune

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SetFit Polarity Model with sentence-transformers/all-mpnet-base-v2

This is a SetFit model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses sentence-transformers/all-mpnet-base-v2 as the Sentence Transformer embedding model. 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
Sentence Transformer body: sentence-transformers/all-mpnet-base-v2
Classification head: a LogisticRegression instance
spaCy Model: en_core_web_sm
SetFitABSA Aspect Model: najwaa/absa-combined-p2-aspect
SetFitABSA Polarity Model: najwaa/absa-combined-p2-polarity
Maximum Sequence Length: 384 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
positive	'computer is so light weight and easy to:this computer is so light weight and easy to carry.' 'and easy to carry.:this computer is so light weight and easy to carry.' 'very lightweight.:very lightweight.'
negative	"it's surprisingly heavy for daily commuting:The build quality feels premium but it's surprisingly heavy for daily commuting." ', though the keyboard feels cramped during:Fantastic display clarity and vibrant colors make this perfect for photo editing, though the keyboard feels cramped during long typing sessions.' ', but the screen brightness is disappointing in:The laptop boots up incredibly fast thanks to the SSD, but the screen brightness is disappointing in outdoor conditions.'
negative	'Screen could be better:Screen could be better' 'not worth the price value.:definitely not worth the price value.'

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(
    "najwaa/absa-combined-p2-aspect",
    "najwaa/absa-combined-p2-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	21.1833	57

Label	Training Sample Count
negative	215
negative	2
positive	263

Training Hyperparameters

batch_size: (32, 32)
num_epochs: (5, 5)
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
l2_weight: 0.01
seed: 42
eval_max_steps: -1
load_best_model_at_end: True

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0003	1	0.3951	-
0.0028	10	-	0.3058
0.0055	20	-	0.3027
0.0083	30	-	0.2976
0.0110	40	-	0.2907
0.0138	50	0.3125	0.2827
0.0166	60	-	0.2732
0.0193	70	-	0.2622
0.0221	80	-	0.2505
0.0248	90	-	0.2405
0.0276	100	0.2767	0.2297
0.0304	110	-	0.2195
0.0331	120	-	0.2085
0.0359	130	-	0.1961
0.0387	140	-	0.1811
0.0414	150	0.2217	0.1629
0.0442	160	-	0.1405
0.0469	170	-	0.1143
0.0497	180	-	0.0882
0.0525	190	-	0.0641
0.0552	200	0.1468	0.0483
0.0580	210	-	0.0385
0.0607	220	-	0.0352
0.0635	230	-	0.0328
0.0663	240	-	0.0283
0.0690	250	0.0616	0.0248
0.0718	260	-	0.0237
0.0745	270	-	0.0208
0.0773	280	-	0.0209
0.0801	290	-	0.0174
0.0828	300	0.0298	0.0195
0.0856	310	-	0.0196
0.0883	320	-	0.0166
0.0911	330	-	0.0199
0.0939	340	-	0.0200
0.0966	350	0.017	0.0184
0.0994	360	-	0.0146
0.1022	370	-	0.0220
0.1049	380	-	0.0212
0.1077	390	-	0.0174
0.1104	400	0.0113	0.0237
0.1132	410	-	0.0220

Framework Versions

Python: 3.11.12
SetFit: 1.1.2
Sentence Transformers: 4.1.0
spaCy: 3.7.5
Transformers: 4.51.3
PyTorch: 2.6.0+cu124
Datasets: 3.6.0
Tokenizers: 0.21.1

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}
}