metadata
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: BAAI/bge-small-en-v1.5
metrics:
- accuracy
widget:
- text: >-
closures:Runa Sarkar, a professor at the Indian Institute of Management
Calcutta, said the coal mining region most affected by mine closures is
West Bengal.
- text: >-
comment:Neither the Russian nor the Chinese defence ministries responded
to Reuters' requests for comment.
- text: >-
Canada:The statements made in Canada's parliament were finally an
acknowledgement of the reality that young Sikhs like me have lived through
for decades: Sikh dissidents expressing their support for an independent
state may face the risk of imminent harm, even in the diaspora.
- text: >-
France:The Paris Agreement, a legally binding international treaty on
climate change adopted by 196 parties at the UN Climate Change Conference
(COP21) in Paris, France in December 2015, aims to hold the increase in
the global average temperature to well below 2°C above pre-industrial
levels.
- text: >-
risk:The statements made in Canada's parliament were finally an
acknowledgement of the reality that young Sikhs like me have lived through
for decades: Sikh dissidents expressing their support for an independent
state may face the risk of imminent harm, even in the diaspora.
pipeline_tag: text-classification
inference: false
model-index:
- name: SetFit Aspect Model with BAAI/bge-small-en-v1.5
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.7630057803468208
name: Accuracy
SetFit Aspect Model with BAAI/bge-small-en-v1.5
This is a SetFit model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses BAAI/bge-small-en-v1.5 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: BAAI/bge-small-en-v1.5
- Classification head: a LogisticRegression instance
- spaCy Model: en_core_web_lg
- SetFitABSA Aspect Model: asadnaqvi/setfitabsa-aspect
- SetFitABSA Polarity Model: asadnaqvi/setfitabsa-polarity
- Maximum Sequence Length: 512 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 | 0.7630 |
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(
"asadnaqvi/setfitabsa-aspect",
"asadnaqvi/setfitabsa-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 | 8 | 25.2939 | 40 |
| Label | Training Sample Count |
|---|---|
| no aspect | 248 |
| aspect | 99 |
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 |
|---|---|---|---|
| 0.0018 | 1 | 0.2598 | - |
| 0.0893 | 50 | 0.2458 | 0.2547 |
| 0.1786 | 100 | 0.2418 | 0.2522 |
| 0.2679 | 150 | 0.2427 | 0.2452 |
| 0.3571 | 200 | 0.1272 | 0.2419 |
| 0.4464 | 250 | 0.0075 | 0.2853 |
| 0.5357 | 300 | 0.0023 | 0.3134 |
| 0.625 | 350 | 0.0021 | 0.3138 |
| 0.7143 | 400 | 0.0037 | 0.3502 |
| 0.8036 | 450 | 0.011 | 0.3437 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- spaCy: 3.7.4
- Transformers: 4.40.1
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- Tokenizers: 0.19.1
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}
}