metadata

library_name: setfit
tags:
  - setfit
  - absa
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      bar:After really enjoying ourselves at the bar we sat down at a table and
      had dinner.
  - text: >-
      interior decor:this little place has a cute interior decor and affordable
      city prices.
  - text: >-
      cuisine:The cuisine from what I've gathered is authentic Taiwanese, though
      its very different from what I've been accustomed to in Taipei.
  - text: >-
      dining:Go here for a romantic dinner but not for an all out wow dining
      experience.
  - text: >-
      Taipei:The cuisine from what I've gathered is authentic Taiwanese, though
      its very different from what I've been accustomed to in Taipei.
pipeline_tag: text-classification
inference: false
co2_eq_emissions:
  emissions: 8.62132655272333
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 0.111
  hardware_used: 1 x NVIDIA GeForce RTX 3090
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
  - name: SetFit Aspect 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: 0.8779507785032646
            name: Accuracy

SetFit Aspect 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 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/paraphrase-mpnet-base-v2
Classification head: a LogisticRegression instance
spaCy Model: en_core_web_lg
SetFitABSA Aspect Model: tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect
SetFitABSA Polarity Model: tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity
Maximum Sequence Length: 512 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	Accuracy
all	0.8780

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(
    "tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect",
    "tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-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	17.9296	37

Label	Training Sample Count
no aspect	71
aspect	128

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
0.0007	1	0.3388	-
0.0370	50	0.2649	-
0.0740	100	0.1562	-
0.1109	150	0.1072	-
0.1479	200	0.0021	-
0.1849	250	0.0007	-
0.2219	300	0.0008	-
0.2589	350	0.0003	-
0.2959	400	0.0002	-
0.3328	450	0.0003	-
0.3698	500	0.0002	-
0.4068	550	0.0001	-
0.4438	600	0.0001	-
0.4808	650	0.0001	-
0.5178	700	0.0001	-
0.5547	750	0.0001	-
0.5917	800	0.0001	-
0.6287	850	0.0002	-
0.6657	900	0.0001	-
0.7027	950	0.0001	-
0.7396	1000	0.0001	-
0.7766	1050	0.0001	-
0.8136	1100	0.0001	-
0.8506	1150	0.0001	-
0.8876	1200	0.0001	-
0.9246	1250	0.0001	-
0.9615	1300	0.0001	-
0.9985	1350	0.0	-

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Carbon Emitted: 0.009 kg of CO2
Hours Used: 0.111 hours

Training Hardware

On Cloud: No
GPU Model: 1 x NVIDIA GeForce RTX 3090
CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
RAM Size: 31.78 GB

Framework Versions

Python: 3.9.16
SetFit: 1.0.0.dev0
Sentence Transformers: 2.2.2
spaCy: 3.7.2
Transformers: 4.29.0
PyTorch: 1.13.1+cu117
Datasets: 2.15.0
Tokenizers: 0.13.3

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}
}