metadata

library_name: setfit
tags:
  - setfit
  - absa
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: camera:It has no camera but, I can always buy and install one easy.
  - text: >-
      Acer:Acer was no help and Garmin could not determine the problem(after
      spending about 2 hours with me), so I returned it and purchased a Toshiba
      R700 that seems even nicer and I was able to load all of my software with
      no problem.
  - text: >-
      memory:I've been impressed with the battery life and the performance for
      such a small amount of memory.
  - text: >-
      speed:Yes, a Mac is much more money than the average laptop out there, but
      there is no comparison in style, speed and just cool factor.
  - text: >-
      fiance:I got it back and my built-in webcam and built-in mic were shorting
      out anytime I touched the lid, (mind you this was my means of
      communication with my fiance who was deployed) but I suffered thru it and
      would constandly have to reset the computer to be able to use my cam and
      mic anytime they went out.
pipeline_tag: text-classification
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: tomaarsen/setfit-absa-semeval-laptops
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.8239700374531835
            name: Accuracy

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_sm
SetFitABSA Aspect Model: joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-aspect
SetFitABSA Polarity Model: joshuasundance/setfit-absa-all-mpnet-base-v2-laptops-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	'cord:I charge it at night and skip taking the cord with me because of the good battery life.' 'battery life:I charge it at night and skip taking the cord with me because of the good battery life.' 'service center:The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the "sales" team, which is the retail shop which I bought my netbook from.'
no aspect	'night:I charge it at night and skip taking the cord with me because of the good battery life.' 'skip:I charge it at night and skip taking the cord with me because of the good battery life.' 'exchange:The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the "sales" team, which is the retail shop which I bought my netbook from.'

Evaluation

Metrics

Label	Accuracy
all	0.8240

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(
    "joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-aspect",
    "joshuasundance/setfit-absa-all-mpnet-base-v2-laptops-polarity",
    spacy_model="en_core_web_sm",
)
# Run inference
preds = model("This laptop meets every expectation and Windows 7 is great!")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	21.1510	42

Label	Training Sample Count
no aspect	119
aspect	126

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
0.0042	1	0.3776	-
0.2110	50	0.2644	0.2622
0.4219	100	0.2248	0.2437
0.6329	150	0.0059	0.2238
0.8439	200	0.0017	0.2326
1.0549	250	0.0012	0.2382
1.2658	300	0.0008	0.2455
1.4768	350	0.0006	0.2328
1.6878	400	0.0005	0.243

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.7
SetFit: 1.0.3
Sentence Transformers: 2.3.0
spaCy: 3.7.2
Transformers: 4.37.2
PyTorch: 2.1.2+cu118
Datasets: 2.16.1
Tokenizers: 0.15.1

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}
}