metadata

tags:
  - setfit
  - absa
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: >-
      bench:Get your food to go, find a bench, and kick back with a plate of
      dumplings.
  - text: >-
      comparison:Frankly, when you compare what you can have here for lunch,
      versus McDs or so many other sandwich shops in the city, there is no
      comparison.
  - text: >-
      ton:We had crawfish boiled and despite making a mess, it was a ton of fun
      and quite tasty as well.
  - text: >-
      traffic noise:It is set far from the small street it's on, and there is no
      traffic noise.
  - text: >-
      food:The only thing more wonderful than the food (which is exceptional) is
      the service.
metrics:
  - f1_micro
  - f1_macro
  - precision_macro
  - recall_macro
pipeline_tag: text-classification
library_name: setfit
inference: false
base_model: sentence-transformers/all-MiniLM-L6-v2
model-index:
  - name: SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: f1_micro
            value: 0.8516772438803264
            name: F1_Micro
          - type: f1_macro
            value: 0.8441110611976916
            name: F1_Macro
          - type: precision_macro
            value: 0.8482610861593047
            name: Precision_Macro
          - type: recall_macro
            value: 0.8409649439480325
            name: Recall_Macro

SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2

This is a SetFit model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses sentence-transformers/all-MiniLM-L6-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification. In particular, this model is in charge of filtering aspect span candidates.

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 this SetFit model to filter these possible aspect span candidates.
Use a SetFit model to classify the filtered aspect span candidates.

Model Details

Model Description

Model Type: SetFit
Sentence Transformer body: sentence-transformers/all-MiniLM-L6-v2
Classification head: a LogisticRegression instance
spaCy Model: en_core_web_lg
SetFitABSA Aspect Model: ronalhung/setfit-absa-restaurants-aspect
SetFitABSA Polarity Model: ronalhung/setfit-absa-restaurants-polarity
Maximum Sequence Length: 256 tokens
Number of Classes: 2 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
aspect	'staff:But the staff was so horrible to us.' "food:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora." "food:The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not."
no aspect	"factor:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora." "deficiencies:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora." "Teodora:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."

Label

Examples

aspect

'staff:But the staff was so horrible to us.'
"food:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."
"food:The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not."

no aspect

"factor:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."
"deficiencies:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."
"Teodora:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."

Evaluation

Metrics

Label	F1_Micro	F1_Macro	Precision_Macro	Recall_Macro
all	0.8517	0.8441	0.8483	0.8410

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(
    "ronalhung/setfit-absa-restaurants-aspect",
    "ronalhung/setfit-absa-restaurants-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	4	19.4181	45

Label	Training Sample Count
no aspect	167
aspect	254

Training Hyperparameters

batch_size: (64, 64)
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: False
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
0.0007	1	0.3998	-
0.0345	50	0.3187	0.3072
0.0689	100	0.2744	0.2600
0.1034	150	0.2494	0.2504
0.1378	200	0.2459	0.2408
0.1723	250	0.2242	0.2210
0.2068	300	0.1802	0.1815
0.2412	350	0.1085	0.1787
0.2757	400	0.0435	0.1918
0.3101	450	0.0143	0.1832
0.3446	500	0.0063	0.1971
0.3790	550	0.004	0.1945
0.4135	600	0.002	0.2005

Framework Versions

Python: 3.11.13
SetFit: 1.1.2
Sentence Transformers: 4.1.0
spaCy: 3.8.7
Transformers: 4.52.4
PyTorch: 2.6.0+cu124
Datasets: 3.6.0
Tokenizers: 0.21.1

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}
}