README.md

---
base_model: sentence-transformers/all-mpnet-base-v2
library_name: setfit
metrics:
- f1
pipeline_tag: text-classification
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: bargain:Monday nights are a bargain at the $28 prix fix - this includes a
    three course meal plus *three* glasses of wine paired with each course.
- text: seated:We walked in on a Wednesday night and were seated promptly.
- text: drinks:While most people can attest to spending over $50 on drinks in New
    York bars and hardly feeling a thing, the drinks here are plentiful and unique.
- text: Lassi:I ordered a Lassi and asked 4 times for it but never got it.
- text: stomach:Check it out, it won't hurt your stomach or your wallet.
inference: false
model-index:
- name: SetFit Aspect Model with sentence-transformers/all-mpnet-base-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: f1
      value: 0.923076923076923
      name: F1
---

# SetFit Aspect Model with sentence-transformers/all-mpnet-base-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **spaCy Model:** en_core_web_trf
- **SetFitABSA Aspect Model:** [MattiaTintori/Final_aspect_Colab](https://huggingface.co/MattiaTintori/Final_aspect_Colab)
- **SetFitABSA Polarity Model:** [setfit-absa-polarity](https://huggingface.co/setfit-absa-polarity)
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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>'price:The price is reasonable although the service is poor.'</li><li>'service:The price is reasonable although the service is poor.'</li><li>'service:The place is so cool and the service is prompt and curtious.'</li></ul>                                                         |
| no aspect | <ul><li>'stomach:The food was delicious but do not come here on a empty stomach.'</li><li>'place:I grew up eating Dosa and have yet to find a place in NY to satisfy my taste buds.'</li><li>'NY:I grew up eating Dosa and have yet to find a place in NY to satisfy my taste buds.'</li></ul> |

## Evaluation

### Metrics
| Label   | F1     |
|:--------|:-------|
| **all** | 0.9231 |

## 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(
    "MattiaTintori/Final_aspect_Colab",
    "setfit-absa-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   | 3   | 19.4137 | 62  |

| Label     | Training Sample Count |
|:----------|:----------------------|
| no aspect | 430                   |
| aspect    | 711                   |

### Training Hyperparameters
- batch_size: (64, 4)
- num_epochs: (5, 32)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- body_learning_rate: (8e-05, 8e-05)
- head_learning_rate: 0.04
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: True
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch      | Step   | Training Loss | Validation Loss |
|:----------:|:------:|:-------------:|:---------------:|
| 0.0028     | 1      | 0.2878        | -               |
| 0.0560     | 20     | 0.2409        | 0.2515          |
| 0.1120     | 40     | 0.2291        | 0.2319          |
| 0.1681     | 60     | 0.1354        | 0.1835          |
| **0.2241** | **80** | **0.0654**    | **0.1389**      |
| 0.2801     | 100    | 0.0334        | 0.1818          |
| 0.3361     | 120    | 0.0535        | 0.1408          |
| 0.3922     | 140    | 0.014         | 0.1564          |
| 0.4482     | 160    | 0.0119        | 0.1453          |
| 0.5042     | 180    | 0.0158        | 0.1511          |
| 0.5602     | 200    | 0.0157        | 0.1393          |
| 0.6162     | 220    | 0.005         | 0.1536          |
| 0.6723     | 240    | 0.0002        | 0.1546          |
| 0.7283     | 260    | 0.0002        | 0.1673          |
| 0.7843     | 280    | 0.0004        | 0.1655          |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- spaCy: 3.7.6
- Transformers: 4.39.0
- PyTorch: 2.3.1+cu121
- Datasets: 2.21.0
- Tokenizers: 0.15.2

## 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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