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---
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: People:Based partly on Chinese military journals, internal speeches by senior
People's Liberation Army (PLA) officers, and patent data, the paper charts more
than 50 years of the PLA navy's often-glacial nuclear submarine development.
- text: Qingdao:Chinese Navy's nuclear-powered submarine Long March 11 takes part
in a naval parade off the eastern port city of Qingdao to mark the 70th anniversary
of the founding of the Chinese People's Liberation Army Navy.
- text: warfare drills:Anti-submarine warfare drills are increasing, as are deployments
of sub-hunting P-8 Poseidon aircraft around Southeast Asia and the Indian Ocean.
- text: devices:The research also details potential breakthroughs in specific areas,
including pump-jet propulsion and internal quieting devices, based on 'imitative
innovation' of Russian technology.
- text: axe 73,800 jobs:State-run miner Coal India Limited (CIL), which has the biggest
workforce among listed government undertakings, is likely to axe 73,800 jobs by
2050 as India pledges to move from fossil fuels to green power, according to a
research report released by the US-based think tank Global Energy Monitor (GEM)
on October 10.
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: 0.7874720357941835
name: Accuracy
---
# SetFit Aspect Model with BAAI/bge-small-en-v1.5
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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. Use a spaCy model to select possible aspect span candidates.
2. **Use this SetFit model to filter these possible aspect span candidates.**
3. 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](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **spaCy Model:** en_core_web_lg
- **SetFitABSA Aspect Model:** [asadnaqvi/setfitabsa-aspect](https://huggingface.co/asadnaqvi/setfitabsa-aspect)
- **SetFitABSA Polarity Model:** [asadnaqvi/setfitabsa-polarity](https://huggingface.co/asadnaqvi/setfitabsa-polarity)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
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### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:----------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| aspect | <ul><li>"visit:The upcoming visit of Saudi Arabia's crown prince Mohammed bin Salman (MBS) to India is not a routine affair."</li><li>"Mohammed bin Salman:The upcoming visit of Saudi Arabia's crown prince Mohammed bin Salman (MBS) to India is not a routine affair."</li><li>'legitimacy:The trip to India is evidently timed to burnish his legitimacy after the international opprobrium that followed the murder of The Washington Post journalist Jamal Khashoggi.'</li></ul> |
| no aspect | <ul><li>"Saudi Arabia:The upcoming visit of Saudi Arabia's crown prince Mohammed bin Salman (MBS) to India is not a routine affair."</li><li>"MBS:The upcoming visit of Saudi Arabia's crown prince Mohammed bin Salman (MBS) to India is not a routine affair."</li><li>"India:The upcoming visit of Saudi Arabia's crown prince Mohammed bin Salman (MBS) to India is not a routine affair."</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.7875 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
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.")
```
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## 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 | 0.2552 |
| 0.1786 | 100 | 0.2418 | 0.2527 |
| **0.2679** | **150** | **0.2427** | **0.2459** |
| 0.3571 | 200 | 0.1272 | 0.2566 |
| 0.4464 | 250 | 0.0075 | 0.3028 |
| 0.5357 | 300 | 0.0023 | 0.3251 |
| 0.625 | 350 | 0.0021 | 0.328 |
| 0.7143 | 400 | 0.0037 | 0.355 |
* 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
```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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