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

This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [HelgeKn/SATHAME-generator-train](https://huggingface.co/datasets/HelgeKn/SATHAME-generator-train) dataset 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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 4 classes
- **Training Dataset:** [HelgeKn/SATHAME-generator-train](https://huggingface.co/datasets/HelgeKn/SATHAME-generator-train)
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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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3     | <ul><li>'The art of change-ringing is peculiar to the English , and , like most English peculiarities , unintelligible to the rest of the world . '</li><li>'Of all scenes that evoke rural England , this is one of the loveliest : An ancient stone church stands amid the fields , the sound of bells cascading from its tower , calling the faithful to evensong . '</li><li>'In the tower , five men and women pull rhythmically on ropes attached to the same five bells that first sounded here in 1614 . '</li></ul>                                  |
| 1     | <ul><li>'The parishioners of St. Michael and All Angels stop to chat at the church door , as members here always have . '</li><li>'History , after all , is not on his side . '</li><li>"According to a nationwide survey taken a year ago , nearly a third of England 's church bells are no longer rung on Sundays because there is no one to ring them . "</li></ul>                                                                                                                                                                                       |
| 2     | <ul><li>'Now , only one local ringer remains : 64-year-old Derek Hammond . '</li><li>'The others here today live elsewhere . '</li><li>'No one speaks , and the snaking of the ropes seems to make as much sound as the bells themselves , muffled by the ceiling . '</li></ul>                                                                                                                                                                                                                                                                               |
| 0     | <ul><li>'`` To ring for even one service at this tower , we have to scrape , `` says Mr. Hammond , a retired water-authority worker . `` '</li><li>'When their changes are completed , and after they have worked up a sweat , ringers often skip off to the local pub , leaving worship for others below . '</li><li>"Two years ago , the Rev. Jeremy Hummerstone , vicar of Great Torrington , Devon , got so fed up with ringers who did n't attend service he sacked the entire band ; the ringers promptly set up a picket line in protest . "</li></ul> |

## 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("HelgeKn/Testing-blub")
# Run inference
preds = model("The others here today live elsewhere . ")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 8   | 27.275 | 45  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 10                    |
| 1     | 10                    |
| 2     | 10                    |
| 3     | 10                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- 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 |
|:-----:|:----:|:-------------:|:---------------:|
| 0.01  | 1    | 0.2799        | -               |
| 0.5   | 50   | 0.1155        | -               |
| 1.0   | 100  | 0.0023        | -               |
| 1.5   | 150  | 0.0008        | -               |
| 2.0   | 200  | 0.0017        | -               |

### Framework Versions
- Python: 3.9.13
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.36.0
- PyTorch: 2.1.1+cpu
- Datasets: 2.15.0
- Tokenizers: 0.15.0

## 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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{"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "datasets": ["HelgeKn/SATHAME-generator-train"], "metrics": ["accuracy"], "widget": [{"text": "`` So crunch , crunch , crunch , bang , bang , bang -- here come the ringers from above , making a very obvious exit while the congregation is at prayer , `` he says . "}, {"text": "The others here today live elsewhere . "}, {"text": "Then , at a signal , the ringers begin varying the order in which the bells sound without altering the steady rhythm of the striking . "}, {"text": "Mr. Hammond worries that old age and the flightiness of youth will diminish the ranks of the East Anglian group that keeps the Aslacton bells pealing . "}], "pipeline_tag": "text-classification", "inference": true, "base_model": "sentence-transformers/paraphrase-mpnet-base-v2"}
setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer dataset:HelgeKn/SATHAME-generator-train arxiv:2209.11055 base_model:sentence-transformers/paraphrase-mpnet-base-v2 region:us