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Duplicate from joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-aspect
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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:

  1. Fine-tuning a Sentence Transformer with contrastive learning.
  2. 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:

  1. Use a spaCy model to select possible aspect span candidates.
  2. Use this SetFit model to filter these possible aspect span candidates.
  3. Use a SetFit model to classify the filtered aspect span candidates.

Model Details

Model Description

Model Sources

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}
}