You can deploy this model and start earning money today!

Askinkaty/
setfit-finance-polarity

Name: setfit-finance-polarity
Availability: InStock

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

sentence-transformers/all-mpnet-base-v2

finetune

Views

No views yet

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: /Askinkaty/setfit-finance-aspect
SetFitABSA Polarity Model: /Askinkaty/setfit-finance-polarity
Maximum Sequence Length: 384 tokens
Number of Classes: 3 classes

Model Labels

Label	Examples
neutral	'Ponzi schemes: Sebi seeks quarterly meetings:Ponzi schemes: Sebi seeks quarterly meetings of state panels' 'European shares steady, pegged:European shares steady, pegged back by Vodafone' 'Bajaj Auto Q2 net at:Bajaj Auto Q2 net at Rs 591 crore'
negative	'pegged back by Vodafone:European shares steady, pegged back by Vodafone' 'M&M Finance plunges 8.5%:M&M Finance plunges 8.5% as brokers cut target price post Q3 results' "' rating on Tata Motors; prefer Hero:Have 'sell' rating on Tata Motors; prefer Hero MotoCorp among auto stocks: Harendra Kumar"
positive	"Buy' on Wipro with target of:Maintain 'Buy' on Wipro with target of Rs 528: Sharekhan" "Motors; prefer Hero MotoCorp among auto stocks:Have 'sell' rating on Tata Motors; prefer Hero MotoCorp among auto stocks: Harendra Kumar" 'Servalakshmi Paper debuts at over:Servalakshmi Paper debuts at over 3 pc premium on BSE'

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(
    "Askinkaty/setfit-finance-aspect",
    "Askinkaty/setfit-finance-polarity",
)
# Run inference
preds = model("Banking stocks to see lot of traction: Mitesh Thacker.")

Training Hyperparameters

batch_size: 64
num_epochs: 2
max_steps: -1
sampling_strategy: oversampling
body_learning_rate: 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

Framework Versions

Python: 3.11.11
SetFit: 1.1.0
Sentence Transformers: 3.3.1
spaCy: 3.7.5
Transformers: 4.42.1
PyTorch: 2.5.1+cu124
Datasets: 3.2.0
Tokenizers: 0.19.1

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}
}