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setfit-absa-books-aspect

Text Classification
setfit
Safetensors
sentence-transformers
bert
absa
generated_from_setfit_trainer
Eval Results
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setfit-absa-books-aspect / README.md
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metadata
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - absa
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: world:Though Arthur skips to another world, he's clearly from our own
  - text: >-
      attire:Among those are the army of doglike and winged creatures, all
      dressed in attire befitting a civilization one hundred and fifty years ago
  - text: >-
      Mister Monday:This is a 361 page book about a boy named Arthur Penhaligon
      who is destined to die an early death, but is saved by a key given to him
      by a mysterious man named Mister Monday
  - text: >-
      parents:Do their parents understand or even care about them? Are they
      ready for sex? Meanwhile can Maggie and Dennis learn to communicate enough
      to stay together?
  - text: >-
      boy:This is a 361 page book about a boy named Arthur Penhaligon who is
      destined to die an early death, but is saved by a key given to him by a
      mysterious man named Mister Monday
inference: false
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: accuracy
            value: 0.8541666666666666
            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
  • 'Poirot:After reading nearly 30 novels, Poirot had been a part of life'
  • 'Michael Dobbs:The cast of characters in this sweeping story by Michael Dobbs of political maneuvering, skullduggery, and backstabbing is an historical Who's Who of the times: the ailing, haughty, and pacifist Chamberlain, who personifies England's bitter memories of the Great War and the popular concept of "never again"; the ambitious and self-absorbed Churchill, whose pugnacity sometimes clouds prudence; the defeatist, philandering, and anti-Semitic U'
  • "Jack:Jack is a wonderful beleaguered hero who starts off by quickly realizing he don't know jack even about himself and as he investigates realizes each new clue proves he knows even less than he thought"
no aspect
  • 'novels:After reading nearly 30 novels, Poirot had been a part of life'
  • 'part:After reading nearly 30 novels, Poirot had been a part of life'
  • 'life:After reading nearly 30 novels, Poirot had been a part of life'

Evaluation

Metrics

Label Accuracy
all 0.8542

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(
    "omymble/setfit-absa-books-aspect",
    "omymble/setfit-absa-books-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 6 34.7122 79
Label Training Sample Count
no aspect 280
aspect 57

Training Hyperparameters

  • batch_size: (256, 256)
  • num_epochs: (2, 2)
  • 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
0.0031 1 0.3698 -
0.1558 50 0.3449 0.3303
0.3115 100 0.3032 0.294
0.4673 150 0.2878 0.266
0.6231 200 0.2414 0.2535
0.7788 250 0.2456 0.2494
0.9346 300 0.2374 0.2477
1.0903 350 0.2407 0.2472
1.2461 400 0.2406 0.2467
1.4019 450 0.2276 0.2465
1.5576 500 0.2248 0.2465
1.7134 550 0.2241 0.2464
1.8692 600 0.2245 0.2463
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 3.0.1
  • spaCy: 3.7.4
  • Transformers: 4.39.0
  • PyTorch: 2.3.1+cu121
  • Datasets: 2.20.0
  • 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}
}