README.md

---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
- precision
- recall
- f1
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: temperature salinity profile collected ctd cast nw atlantic limit40 w noaa
    ship delaware ii noaa ship albatross iv 14 january 1997 30 october 1997 data collected
    12 cruise multiple program ctd cast primarily made conjunction bongo plankton
    tow plankton data included
- text: plume height misr 82420 california fire 2020 multiangle imaging spectroradiometer
    misr team nasa jet propulsion laboratory california institute technology pasadena
    california provided map wildfire smoke plume height several wildfire california
    derived data acquired misr instrument board nasa terra satellite august 24 2020
    misr carry nine fixed camera view scene different angle period seven minute accounting
    true motion cloud due wind angular parallax cloud different view used derive height
    smoke plume data contain plume height information czu lightning complex lnu lightning
    complex scu lightning complex fire observed misr approximately 1210 pm local time
    august 24 2020 plume height give indication fire intensity indicates whether smoke
    impacting air quality groundlevel observation plume height also important input
    air quality model predict smoke go might affect downwind misr plume height map
    produced using misr interactive explorer minx software
- text: municipal land transfer tax revenue summary
- text: aggregated broccoli production yield
- text: street furniture bicycle parking
inference: false
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.595
      name: Accuracy
    - type: precision
      value: 0.7037037037037037
      name: Precision
    - type: recall
      value: 0.8407079646017699
      name: Recall
    - type: f1
      value: 0.7661290322580645
      name: F1
---

# SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification.

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.

## Model Details

### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **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)

## Evaluation

### Metrics
| Label   | Accuracy | Precision | Recall | F1     |
|:--------|:---------|:----------|:-------|:-------|
| **all** | 0.595    | 0.7037    | 0.8407 | 0.7661 |

## 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 SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("lgd/setfit-multilabel")
# Run inference
preds = model("street furniture bicycle parking")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 59.4   | 411 |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-----:|:----:|:-------------:|:---------------:|
| 0.002 | 1    | 0.2153        | -               |
| 0.1   | 50   | 0.201         | -               |
| 0.2   | 100  | 0.1433        | -               |
| 0.3   | 150  | 0.0812        | -               |
| 0.4   | 200  | 0.0866        | -               |
| 0.5   | 250  | 0.0306        | -               |
| 0.6   | 300  | 0.1093        | -               |
| 0.7   | 350  | 0.0647        | -               |
| 0.8   | 400  | 0.0255        | -               |
| 0.9   | 450  | 0.0421        | -               |
| 1.0   | 500  | 0.0366        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.3.1+cu121
- Datasets: 2.20.0
- 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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