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_lg
- SetFitABSA Aspect Model: omymble/setfit-absa-books-aspect
- SetFitABSA Polarity Model: omymble/setfit-absa-books-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 |
|
no aspect |
|
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}
}
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Model tree for omymble/setfit-absa-books-aspect
Base model
sentence-transformers/all-MiniLM-L6-v2