setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/all-mpnet-base-v2 base_model:finetune:sentence-transformers/all-mpnet-base-v2 model-index region:us
---
base_model: sentence-transformers/all-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: I wonder how times someone has wrecked trying to do the 'stare and drive'
    move from 2 Fast 2 Furious
- text: 'Plains All American Pipeline company may have spilled 40% more crude oil
    than previously estimated #KSBYNews @lilitan http://t.co/PegibIqk2w'
- text: 'ThisIsFaz: Anti Collision Rear- #technology #cool http://t.co/KEfxTjTAKB
    Via Techesback #Tech'
- text: Official kinesiology tape of IRONMANå¨ long-lasting durability effectiveness
    on common injuries http://t.co/ejymkZPEEx http://t.co/0IYuntXDUv
- text: Well as I was chaning an iPad screen it fucking exploded and glass went all
    over the place. Looks like my job is going to need a new one.
inference: true
model-index:
- name: SetFit with sentence-transformers/all-mpnet-base-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.8233459202101461
      name: Accuracy
---

# SetFit with sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) 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.

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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **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                                                                                                                                                                                                                                                                                                                                                                                                                   |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>'FOOTBALL IS BACK THIS WEEKEND ITS JUST SUNK IN ??????'</li><li>'Tried orange aftershock today. My life will never be the same'</li><li>"Attack on Titan game on PS Vita yay! Can't wait for 2016"</li></ul>                                                                                                                                                                                                       |
| 1     | <ul><li>'@author_mike Amen today is the Day of Salvation. THX brother Mike for your great encouragement. - http://t.co/cybKsXHF7d Coming US Tsunami'</li><li>". @VELDFest announces refunds after Day two's extreme weather evacuation: http://t.co/PP05eTlK7t http://t.co/3Ol8MhhPMa"</li><li>'http://t.co/lMA39ZRWoY There is a way which seemeth right unto a man but the end thereof are the ways of death.'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8233   |

## 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("pEpOo/catastrophy4")
# Run inference
preds = model("ThisIsFaz: Anti Collision Rear- #technology #cool http://t.co/KEfxTjTAKB Via Techesback #Tech")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 2   | 15.0486 | 30  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 836                   |
| 1     | 686                   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0003 | 1    | 0.4126        | -               |
| 0.0131 | 50   | 0.2779        | -               |
| 0.0263 | 100  | 0.2507        | -               |
| 0.0394 | 150  | 0.2475        | -               |
| 0.0526 | 200  | 0.1045        | -               |
| 0.0657 | 250  | 0.2595        | -               |
| 0.0788 | 300  | 0.1541        | -               |
| 0.0920 | 350  | 0.1761        | -               |
| 0.1051 | 400  | 0.0456        | -               |
| 0.1183 | 450  | 0.1091        | -               |
| 0.1314 | 500  | 0.1335        | -               |
| 0.1445 | 550  | 0.0956        | -               |
| 0.1577 | 600  | 0.0583        | -               |
| 0.1708 | 650  | 0.0067        | -               |
| 0.1840 | 700  | 0.0021        | -               |
| 0.1971 | 750  | 0.0057        | -               |
| 0.2102 | 800  | 0.065         | -               |
| 0.2234 | 850  | 0.0224        | -               |
| 0.2365 | 900  | 0.0008        | -               |
| 0.2497 | 950  | 0.1282        | -               |
| 0.2628 | 1000 | 0.1045        | -               |
| 0.2760 | 1050 | 0.001         | -               |
| 0.2891 | 1100 | 0.0005        | -               |
| 0.3022 | 1150 | 0.0013        | -               |
| 0.3154 | 1200 | 0.0007        | -               |
| 0.3285 | 1250 | 0.0015        | -               |
| 0.3417 | 1300 | 0.0007        | -               |
| 0.3548 | 1350 | 0.0027        | -               |
| 0.3679 | 1400 | 0.0006        | -               |
| 0.3811 | 1450 | 0.0001        | -               |
| 0.3942 | 1500 | 0.0009        | -               |
| 0.4074 | 1550 | 0.0002        | -               |
| 0.4205 | 1600 | 0.0004        | -               |
| 0.4336 | 1650 | 0.0003        | -               |
| 0.4468 | 1700 | 0.0013        | -               |
| 0.4599 | 1750 | 0.0004        | -               |
| 0.4731 | 1800 | 0.0007        | -               |
| 0.4862 | 1850 | 0.0001        | -               |
| 0.4993 | 1900 | 0.0001        | -               |
| 0.5125 | 1950 | 0.0476        | -               |
| 0.5256 | 2000 | 0.0561        | -               |
| 0.5388 | 2050 | 0.0009        | -               |
| 0.5519 | 2100 | 0.0381        | -               |
| 0.5650 | 2150 | 0.017         | -               |
| 0.5782 | 2200 | 0.033         | -               |
| 0.5913 | 2250 | 0.0001        | -               |
| 0.6045 | 2300 | 0.0077        | -               |
| 0.6176 | 2350 | 0.0002        | -               |
| 0.6307 | 2400 | 0.0003        | -               |
| 0.6439 | 2450 | 0.0001        | -               |
| 0.6570 | 2500 | 0.0155        | -               |
| 0.6702 | 2550 | 0.0002        | -               |
| 0.6833 | 2600 | 0.0001        | -               |
| 0.6965 | 2650 | 0.031         | -               |
| 0.7096 | 2700 | 0.0215        | -               |
| 0.7227 | 2750 | 0.0002        | -               |
| 0.7359 | 2800 | 0.0002        | -               |
| 0.7490 | 2850 | 0.0001        | -               |
| 0.7622 | 2900 | 0.0001        | -               |
| 0.7753 | 2950 | 0.0001        | -               |
| 0.7884 | 3000 | 0.0001        | -               |
| 0.8016 | 3050 | 0.0001        | -               |
| 0.8147 | 3100 | 0.0001        | -               |
| 0.8279 | 3150 | 0.0001        | -               |
| 0.8410 | 3200 | 0.0001        | -               |
| 0.8541 | 3250 | 0.0001        | -               |
| 0.8673 | 3300 | 0.0001        | -               |
| 0.8804 | 3350 | 0.0001        | -               |
| 0.8936 | 3400 | 0.0           | -               |
| 0.9067 | 3450 | 0.0156        | -               |
| 0.9198 | 3500 | 0.0           | -               |
| 0.9330 | 3550 | 0.0           | -               |
| 0.9461 | 3600 | 0.0001        | -               |
| 0.9593 | 3650 | 0.0208        | -               |
| 0.9724 | 3700 | 0.0           | -               |
| 0.9855 | 3750 | 0.0001        | -               |
| 0.9987 | 3800 | 0.0001        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- 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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