MattiaTintori/ABSA_Aspect_EN

SetFit Aspect Model with sentence-transformers/all-mpnet-base-v2

This is a SetFit model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses sentence-transformers/all-mpnet-base-v2 as the Sentence Transformer embedding model. A SetFitHead 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. 1.Fine-tuning a Sentence Transformer with contrastive learning.
2. 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. 1.Use a spaCy model to select possible aspect span candidates.
2. 2.Use this SetFit model to filter these possible aspect span candidates.
3. 3.Use a SetFit model to classify the filtered aspect span candidates.

Model Details

Model Description

• —Model Type: SetFit
• —Sentence Transformer body: sentence-transformers/all-mpnet-base-v2
• —Classification head: a SetFitHead instance
• —spaCy Model: encoreweb_trf
• —SetFitABSA Aspect Model: MattiaTintori/Final_aspect_Colab
• —SetFitABSA Polarity Model: setfit-absa-polarity
• —Maximum Sequence Length: 384 tokens
• —Number of Classes: 2 classes <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

• —Repository: SetFit on GitHub
• —Paper: Efficient Few-Shot Learning Without Prompts
• —Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Evaluation

Metrics

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(
    "MattiaTintori/Final_aspect_Colab",
    "setfit-absa-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")

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Training Details

Training Set Metrics

Training Hyperparameters

• —batch_size: (64, 4)
• —num_epochs: (5, 32)
• —max_steps: -1
• —sampling_strategy: oversampling
• —num_iterations: 10
• —bodylearningrate: (8e-05, 8e-05)
• —headlearningrate: 0.04
• —loss: CosineSimilarityLoss
• —distancemetric: cosinedistance
• —margin: 0.25
• —endtoend: False
• —use_amp: True
• —warmup_proportion: 0.1
• —l2_weight: 0.01
• —seed: 42
• —evalmaxsteps: -1
• —loadbestmodelatend: True

Training Results

• —The bold row denotes the saved checkpoint.

Framework Versions

• —Python: 3.10.12
• —SetFit: 1.0.3
• —Sentence Transformers: 3.0.1
• —spaCy: 3.7.6
• —Transformers: 4.39.0
• —PyTorch: 2.3.1+cu121
• —Datasets: 2.21.0
• —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}
}

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