metadata

library_name: setfit
tags:
  - setfit
  - absa
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      The food at Cafe Asean:The food at Cafe Asean is to die for, and the
      prices are unmatchable.
  - text: >-
      its a cool place to come with:its a cool place to come with a bunch of
      people or with a date for maybe a mild dinner or some drinks.
  - text: >-
      times, the food is always good:Although the service can be a bit brusque
      at times, the food is always good, hearty and hot.
  - text: >-
      we found the food to be so:Came recommended to us, but we found the food
      to be so-so, the service good, but we were told we could not order desert
      since the table we were at had a reservation waiting.
  - text: >-
      warned that this place can get pretty:Be warned that this place can get
      pretty crowded, though the $3 bloody mary's at the bar and the killer DJ
      make the wait more than bearable.
pipeline_tag: text-classification
inference: false
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
  - name: SetFit Polarity Model with sentence-transformers/paraphrase-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.45161290322580644
            name: Accuracy

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

This is a SetFit model 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: NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect
SetFitABSA Polarity Model: NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-polarity
Maximum Sequence Length: 512 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	"best sit down food I've had:It might be the best sit down food I've had in the area, so if you are going to the upright citizen brigade, or the garden, it could be just the place for you." 'generous and the staff brings out multiple:Portions are fairly generous and the staff brings out multiple little bites and treats throughout dinner.' 'casual Middle Eastern menu looks familar,:The Food The casual Middle Eastern menu looks familar, but the food--made to order in the open kitchen--is a notch above its peers.'
neutral	"be just the place for you.:It might be the best sit down food I've had in the area, so if you are going to the upright citizen brigade, or the garden, it could be just the place for you." ') other food is served in:) other food is served in too-small portions, but at least it leaves room for dessert.' "room while the food on other peoples:Upon entering, I was impressed by the room while the food on other peoples' tables seemed enticing."
negative	'Though the service might be a:Though the service might be a little slow, the waitresses are very friendly.' 'was expecting poor service and ambience but:After reading other reviews I was expecting poor service and ambience but was pleasantly surprised by our more than helpful waiter.' 'we found the food to be so:Came recommended to us, but we found the food to be so-so, the service good, but we were told we could not order desert since the table we were at had a reservation waiting.'

Evaluation

Metrics

Label	Accuracy
all	0.4516

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(
    "NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect",
    "NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-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	17	28.6364	53

Label	Training Sample Count
negative	10
neutral	12
positive	11

Training Hyperparameters

batch_size: (16, 2)
num_epochs: (1, 16)
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: False
warmup_proportion: 0.1
seed: 42
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0217	1	0.2212	-

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 2.4.0
spaCy: 3.7.4
Transformers: 4.37.2
PyTorch: 2.1.0+cu121
Datasets: 2.17.1
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}
}