setfit safetensors bert absa sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:BAAI/bge-small-en-v1.5 base_model:finetune:BAAI/bge-small-en-v1.5 model-index region:us
---
base_model: BAAI/bge-small-en-v1.5
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: mine employment is starker at mid-:The future of coal mine employment is starker
    at mid-century.
- text: are considered relatively noisy - a major:But the Type 094s, which carry China's
    most advanced submarine-launched JL-3 missile, are considered relatively noisy
    - a major handicap for military submarines.
- text: March this year raised an objection, saying that:Initially, the registrar's
    office of the IHC in March this year raised an objection, saying that the high
    court was not an appropriate forum and asked the petitioner to approach the relevant
    authorities.
- text: mine closures is West Bengal.: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: FIA DG to proceed in accordance with the law.:When the petition was heard
    by the chief justice, he asked the FIA DG to proceed in accordance with the law.
inference: false
model-index:
- name: SetFit Polarity 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: 0.7065217391304348
      name: Accuracy
---

# SetFit Polarity 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 classifying aspect polarities.

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 a SetFit model to filter these possible aspect span candidates.
3. **Use this 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:** 4 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
|:------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Informative | <ul><li>"The upcoming visit of Saudi Arabia:The upcoming visit of Saudi Arabia's crown prince Mohammed bin Salman (MBS) to India is not a routine affair."</li><li>"'s crown prince Mohammed bin Salman (MBS):The upcoming visit of Saudi Arabia's crown prince Mohammed bin Salman (MBS) to India is not a routine affair."</li><li>'to burnish his legitimacy after the international: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>                                                                                                                                                      |
| Negative    | <ul><li>'that followed the murder of The Washington: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><li>"Arabia's disastrous military intervention in Yemen or:India for its part has refrained from even a hint of disapproval of Saudi Arabia's disastrous military intervention in Yemen or its misguided attempts to isolate Qatar, never mind the brutal assassination of Khashoggi."</li><li>'condemn the Soviet invasion but privately urged:India sought to adopt a more nuanced stance; it did not openly condemn the Soviet invasion but privately urged Moscow to pull back.'</li></ul> |
| Positive    | <ul><li>"in fostering stronger relations with countries in:Prime Minister Narendra Modi's government has invested considerable time and energy in fostering stronger relations with countries in West Asia."</li><li>"has invested considerable time and energy in fostering stronger:Prime Minister Narendra Modi's government has invested considerable time and energy in fostering stronger relations with countries in West Asia."</li><li>"security and economic ties with Saudi Arabia:Modi's visit to Riyadh in 2016 gave a fillip to security and economic ties with Saudi Arabia."</li></ul>                                                                                                                                |
| Ambivalent  | <ul><li>"a hint of disapproval of Saudi Arabia:India for its part has refrained from even a hint of disapproval of Saudi Arabia's disastrous military intervention in Yemen or its misguided attempts to isolate Qatar, never mind the brutal assassination of Khashoggi."</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                                                  |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.7065   |

## 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   | 11  | 27.7071 | 45  |

| Label       | Training Sample Count |
|:------------|:----------------------|
| Ambivalent  | 1                     |
| Informative | 73                    |
| Negative    | 20                    |
| Positive    | 5                     |

### 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 |
|:----------:|:------:|:-------------:|:---------------:|
| 0.0217     | 1      | 0.2599        | -               |
| **1.0870** | **50** | **0.0608**    | **0.3526**      |
| 2.1739     | 100    | 0.0253        | 0.4091          |
| 3.2609     | 150    | 0.0159        | 0.4497          |
| 4.3478     | 200    | 0.0035        | 0.4437          |

* 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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