metadata
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: >-
louder and the mouse didnt break:I wish the volume could be louder and the
mouse didnt break after only a month.
- text: >-
+ + (sales, service,:BEST BUY - 5 STARS + + + (sales, service, respect for
old men who aren't familiar with the technology) DELL COMPUTERS - 3 stars
DELL SUPPORT - owes a me a couple
- text: >-
back and my built-in webcam and built-: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.
- text: >-
after i install Mozzilla firfox i love every:the only fact i dont like
about apples is they generally use safari and i dont use safari but after
i install Mozzilla firfox i love every single bit about it.
- text: >-
in webcam and built-in mic were shorting out: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-mpnet-base-v2
model-index:
- name: SetFit Polarity Model with sentence-transformers/all-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: tomaarsen/setfit-absa-semeval-laptops
type: unknown
split: test
metrics:
- type: accuracy
value: 0.7007874015748031
name: Accuracy
SetFit Polarity 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 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-mpnet-base-v2
- Classification head: a LogisticRegression instance
- spaCy Model: en_core_web_sm
- SetFitABSA Aspect Model: joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-aspect
- SetFitABSA Polarity Model: joshuasundance/setfit-absa-all-mpnet-base-v2-laptops-polarity
- Maximum Sequence Length: 384 tokens
- Number of Classes: 4 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 |
|
| conflict |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.7008 |
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 | 3 | 25.5873 | 48 |
| Label | Training Sample Count |
|---|---|
| conflict | 2 |
| negative | 45 |
| neutral | 30 |
| positive | 49 |
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 |
|---|---|---|---|
| 0.0120 | 1 | 0.2721 | - |
| 0.6024 | 50 | 0.0894 | 0.2059 |
| 1.2048 | 100 | 0.0014 | 0.2309 |
| 1.8072 | 150 | 0.0006 | 0.2359 |
| 2.4096 | 200 | 0.0005 | 0.2373 |
| 3.0120 | 250 | 0.0004 | 0.2364 |
| 3.6145 | 300 | 0.0003 | 0.2371 |
- 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}
}