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: The best thing about this is it drowned out the call from the guy angry cause
    he hadn't gotten a tracking number... http://t.co/QYu8grOrQ1
- text: 'http://t.co/a0v1ybySOD Its the best time of day!! åÊ @Siren_Voice is #liveonstreamate!'
- text: 16yr old PKK suicide bomber who detonated bomb in Turkey Army trench released
    http://t.co/mMkLapX2ok
- text: '#hot  Reddit''s new content policy goes into effect many horrible subreddits
    banned or quarantined http://t.co/HqdCZzdmbN #prebreak #best'
- text: Heat wave warning aa? Ayyo dei. Just when I plan to visit friends after a
    year.
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: 0.8098990736900318
      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>'peanut butter cookie dough blizzard is ??????????????????????'</li><li>'Free Ebay Sniping RT? http://t.co/B231Ul1O1K Lumbar Extender Back Stretcher Excellent Condition!! ?Please Favorite &amp; Share'</li><li>"'13 M. Chapoutier Crozes Hermitage so much purple violets slate crushed gravel white pepper. Yum #france #wine #DC http://t.co/skvWN38HZ7"</li></ul>                                                            |
| 1     | <ul><li>'DUST IN THE WIND: @82ndABNDIV paratroopers move to a loading zone during a dust storm in support of Operation Fury: http://t.co/uGesKLCn8M'</li><li>'Delhi Government to Provide Free Treatment to Acid Attack Victims in Private Hospitals http://t.co/H6PM1W7elL'</li><li>'National Briefing | West: California: Spring Oil Spill Estimate Grows: Documents released on Wednesday disclos... http://t.co/wBi7Laq18E'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8099   |

## 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/catastrophy")
# Run inference
preds = model("Heat wave warning aa? Ayyo dei. Just when I plan to visit friends after a year.")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 2   | 15.3737 | 31  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 222                   |
| 1     | 158                   |

### Training Hyperparameters
- batch_size: (8, 8)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0005 | 1    | 0.3038        | -               |
| 0.0263 | 50   | 0.1867        | -               |
| 0.0526 | 100  | 0.2578        | -               |
| 0.0789 | 150  | 0.2298        | -               |
| 0.1053 | 200  | 0.1253        | -               |
| 0.1316 | 250  | 0.0446        | -               |
| 0.1579 | 300  | 0.1624        | -               |
| 0.1842 | 350  | 0.0028        | -               |
| 0.2105 | 400  | 0.0059        | -               |
| 0.2368 | 450  | 0.0006        | -               |
| 0.2632 | 500  | 0.0287        | -               |
| 0.2895 | 550  | 0.003         | -               |
| 0.3158 | 600  | 0.0004        | -               |
| 0.3421 | 650  | 0.0014        | -               |
| 0.3684 | 700  | 0.0002        | -               |
| 0.3947 | 750  | 0.0001        | -               |
| 0.4211 | 800  | 0.0002        | -               |
| 0.4474 | 850  | 0.0002        | -               |
| 0.4737 | 900  | 0.0002        | -               |
| 0.5    | 950  | 0.0826        | -               |
| 0.5263 | 1000 | 0.0002        | -               |
| 0.5526 | 1050 | 0.0001        | -               |
| 0.5789 | 1100 | 0.0003        | -               |
| 0.6053 | 1150 | 0.0303        | -               |
| 0.6316 | 1200 | 0.0001        | -               |
| 0.6579 | 1250 | 0.0           | -               |
| 0.6842 | 1300 | 0.0001        | -               |
| 0.7105 | 1350 | 0.0           | -               |
| 0.7368 | 1400 | 0.0001        | -               |
| 0.7632 | 1450 | 0.0002        | -               |
| 0.7895 | 1500 | 0.0434        | -               |
| 0.8158 | 1550 | 0.0001        | -               |
| 0.8421 | 1600 | 0.0           | -               |
| 0.8684 | 1650 | 0.0001        | -               |
| 0.8947 | 1700 | 0.0001        | -               |
| 0.9211 | 1750 | 0.0001        | -               |
| 0.9474 | 1800 | 0.0001        | -               |
| 0.9737 | 1850 | 0.0001        | -               |
| 1.0    | 1900 | 0.0           | -               |

### 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->