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: >-
be an absolute thrill to read when:Having said that, this must be an
absolute thrill to read when you're nine or ten
- text: >-
market followed classical economic laws:Levi describes how the market
followed classical economic laws
- text: >-
This fantasy will certainly hit:This fantasy will certainly hit the mark
for anyone who enjoys the genre
- text: >-
a bit of brutal reality and a rape:There is quite a bit of brutal reality
and a rape too terrible to even think about, but Val McDermid has created
characters and a story that I just couldn't put down
- text: >-
Kingston is no Steinem:Kingston is no Steinem and doesn't suggest that a
woman needs a man like a fish needs a bicycle (though she is unmarried)
inference: false
model-index:
- name: SetFit Polarity 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: 0.7142857142857143
name: Accuracy
SetFit Polarity 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 classifying aspect polarities.
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 a SetFit model to filter these possible aspect span candidates.
- Use this 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: 3 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 |
---|---|
neutral |
|
positive |
|
negative |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.7143 |
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 | 9 | 30.2105 | 84 |
Label | Training Sample Count |
---|---|
negative | 6 |
neutral | 42 |
positive | 9 |
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 |
---|---|---|---|
0.125 | 1 | 0.3786 | - |
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}
}