---
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: The ambience is very calm and quiet:The ambience is very calm and quiet.
- text: For great chinese food nearby, you have Wu:For great chinese food nearby,
    you have Wu Liang Ye and Grand Sichuan just a block away.
- text: The menu choices are similar but the taste:The menu choices are similar but
    the taste lacked more flavor than it looked.
- text: The food was authentic.:The food was authentic.
- text: prompt to jump behind the bar and fix drinks, they:The staff is very kind
    and well trained, they're fast, they are always prompt to jump behind the bar
    and fix drinks, they know details of every item in the menu and make excelent
    recomendations.
inference: false
model-index:
- name: SetFit Polarity 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: 0.8170404156194555
      name: F1
---

# SetFit Polarity 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 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:** [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:** [setfit-absa-aspect](https://huggingface.co./setfit-absa-aspect)
- **SetFitABSA Polarity Model:** [MattiaTintori/Final_polarity_Colab](https://huggingface.co./MattiaTintori/Final_polarity_Colab)
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 3 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1     | <ul><li>'waiter) We got no cheese offered for the pasta,:(food was delivered by a busboy, not waiter) We got no cheese offered for the pasta, our water and wine glasses remained EMPTY our entire meal, when we would have easily spent another $20 on wine.'</li><li>'by a busboy, not waiter) We got no cheese:(food was delivered by a busboy, not waiter) We got no cheese offered for the pasta, our water and wine glasses remained EMPTY our entire meal, when we would have easily spent another $20 on wine.'</li><li>'for the pasta, our water and wine glasses remained EMPTY our entire meal:(food was delivered by a busboy, not waiter) We got no cheese offered for the pasta, our water and wine glasses remained EMPTY our entire meal, when we would have easily spent another $20 on wine.'</li></ul> |
| 2     | <ul><li>'(food was delivered by a busboy:(food was delivered by a busboy, not waiter) We got no cheese offered for the pasta, our water and wine glasses remained EMPTY our entire meal, when we would have easily spent another $20 on wine.'</li><li>'glasses remained EMPTY our entire meal, when we would have:(food was delivered by a busboy, not waiter) We got no cheese offered for the pasta, our water and wine glasses remained EMPTY our entire meal, when we would have easily spent another $20 on wine.'</li><li>'spent another $20 on wine.:(food was delivered by a busboy, not waiter) We got no cheese offered for the pasta, our water and wine glasses remained EMPTY our entire meal, when we would have easily spent another $20 on wine.'</li></ul>                                              |
| 0     | <ul><li>'few cocktails and enjoy our surroundings and each other.:20 minutes for our reservation but it gave us time to have a few cocktails and enjoy our surroundings and each other.'</li><li>'Barbecued codfish was gorgeously moist - as:Barbecued codfish was gorgeously moist - as if poached - yet the fabulous texture was let down by curiously bland seasoning - a spice rub might have overwhelmed, however herb mix or other sauce would have done much to enhance.'</li><li>'Even though its good seafood, the prices are too:Even though its good seafood, the prices are too high.'</li></ul>                                                                                                                                                                                                             |

## Evaluation

### Metrics
| Label   | F1     |
|:--------|:-------|
| **all** | 0.8170 |

## 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(
    "setfit-absa-aspect",
    "MattiaTintori/Final_polarity_Colab",
)
# 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   | 1   | 25.0463 | 79  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 1148                  |
| 1     | 607                   |
| 2     | 489                   |

### Training Hyperparameters
- batch_size: (64, 4)
- num_epochs: (5, 32)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- body_learning_rate: (5e-05, 5e-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 |
|:----------:|:-------:|:-------------:|:---------------:|
| 0.0014     | 1       | 0.3084        | -               |
| 0.0285     | 20      | 0.2735        | 0.2591          |
| 0.0570     | 40      | 0.2228        | 0.2351          |
| 0.0855     | 60      | 0.2071        | 0.1993          |
| 0.1140     | 80      | 0.1522        | 0.1696          |
| 0.1425     | 100     | 0.1441        | 0.1671          |
| 0.1709     | 120     | 0.1632        | 0.161           |
| 0.1994     | 140     | 0.0966        | 0.1575          |
| 0.2279     | 160     | 0.1737        | 0.1504          |
| 0.2564     | 180     | 0.1092        | 0.1671          |
| 0.2849     | 200     | 0.1314        | 0.1459          |
| 0.3134     | 220     | 0.0972        | 0.1483          |
| 0.3419     | 240     | 0.1014        | 0.1537          |
| 0.3704     | 260     | 0.0506        | 0.1514          |
| **0.3989** | **280** | **0.0817**    | **0.143**       |
| 0.4274     | 300     | 0.0592        | 0.1526          |
| 0.4558     | 320     | 0.0311        | 0.1562          |
| 0.4843     | 340     | 0.038         | 0.1546          |
| 0.5128     | 360     | 0.0852        | 0.1497          |
| 0.5413     | 380     | 0.0359        | 0.144           |
| 0.5698     | 400     | 0.0449        | 0.1639          |
| 0.5983     | 420     | 0.0314        | 0.1517          |

* 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: 2.4.0+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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