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

This is a SetFit model trained on the omymble/setfit-books-categories dataset that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2
Classification head: a LogisticRegression instance
spaCy Model: en_core_web_lg
SetFitABSA Aspect Model: setfit-absa-aspect
SetFitABSA Polarity Model: omymble/books-categories
Maximum Sequence Length: 512 tokens
Number of Classes: 6 classes
Training Dataset: omymble/setfit-books-categories

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
BOOK#AUDIENCE	'I recommend this for fans of fantasy, or other books by Garth Nix' 'I first got this book when I was eight and I totally loved it! I have read it every year since then! It is about a pair of twins who are born one VERY good and one EXTREMELY bad' 'However, I did feel one particular scene might be rather nightmare-inducing for the youngest readers - so recommend this for the ages of 12 and above'
BOOK#AUTHOR	'Banks has writin better books than this book,' "Now in is astonishing new novel, Michael Dobbs throws brilliant fresh light upon Churchill's relationship with the Soviet spy and the twenty months of conspiracy, chance and outright treachery that were to propel Churchill from outcast to messiah and change the course of history" 'Paul focuses on the problems of an intimate relationship and the decisions the teens make at that moment'
BOOK#GENERAL	'This is the first book in the Keys to the Kingdom series by Garth Nix' 'The book is a great read right until the end, so rare in non-fiction' 'Anne Kingston did a marvellous job on this book'
BOOK#TITLE	'Personal I loved My Darling My Hamburger' 'But THE INTRUDERS is pretty much a middling effort, at least when it comes to the plot' 'After reading several pages I relented and purchased Mister Monday'
CONTENT#CHARACTERS	"She's not a great writer but she's a fabulous storyteller and her Tony Hill/Carol Jordan mysteries are the best of the bunch" 'but before he can do that he has to dodge fechters, run from enemys like Noon and Dawn, run from dinosaurs, try not to get killed, and try to prevent himself from having a asthma atackk!! But, thankfully he has some help from a girl named suzy, a guy named Dusk, and a talking toad' 'But when a fight emerges between the two figures - Mister Monday and Sneezer - they both disappear without any further regard to Arthur'
CONTENT#GENRE	'I love fantasy and science fiction, but this storyteller forgot something very important' 'At first I was amused an entertained by Angela and Diabola the novel by Lynne Reid Banks, but as it progressed and became exceedingly darker, I read the jacket to find that this book was recommended for ages 9-12' "Here's a thriller that really thrills"

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(
    "setfit-absa-aspect",
    "omymble/books-categories",
)
# 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	2	21.0917	78

Label	Training Sample Count
BOOK#AUDIENCE	20
BOOK#AUTHOR	20
BOOK#GENERAL	20
BOOK#TITLE	20
CONTENT#CHARACTERS	20
CONTENT#GENRE	20

Training Hyperparameters

batch_size: (128, 128)
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
seed: 42
eval_max_steps: -1
load_best_model_at_end: True

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0106	1	0.2623	-
0.5319	50	0.1293	-
1.0638	100	0.0132	-
1.5957	150	0.0022	-
2.1277	200	0.0027	-
2.6596	250	0.0013	-
3.1915	300	0.0017	-
3.7234	350	0.0015	-
4.2553	400	0.0029	-
4.7872	450	0.0015	-
0.0106	1	0.0115	-
0.5319	50	0.009	0.1324
1.0638	100	0.0094	0.1267
1.5957	150	0.0007	0.1194
2.1277	200	0.0017	0.1256
2.6596	250	0.0008	0.1293
3.1915	300	0.0007	0.1173
3.7234	350	0.0008	0.1231
4.2553	400	0.0023	0.1272
4.7872	450	0.0008	0.1241

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 3.1.0
spaCy: 3.7.4
Transformers: 4.39.0
PyTorch: 2.3.1+cu121
Datasets: 2.20.0
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}
}

omymble
/

books-categories