setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/paraphrase-mpnet-base-v2 base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2 model-index region:us
---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: But the author is Bharath Ganesh.
- text: The documents, which suggest all the adults were involved in the training,
    say a person serving as a foster parent caring for one of the kids revealed the
    details about the training.
- text: Louis Farrakhan, the 84-year-old head of the Nation of Islam, has been back
    in the headlines after a previously unreleased photo of him with President Barack
    Obama was published in January and Mr. Farrakhan gave an anti-Semitic speech at
    his organization’s annual convention last month.
- text: The name of that CIA official whose torture activities the Post described
    is Gina Haspel.
- text: This is not just about Facebook or Twitter.
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: 0.7125193199381762
      name: Accuracy
---

# 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 |
|:--------|:---------|
| **all** | 0.7125   |

## 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("anismahmahi/G2-with-noPropaganda-multilabel-setfit-model")
# Run inference
preds = model("But the author is Bharath Ganesh.")
```

<!--
### 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   | 1   | 23.3972 | 129 |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- 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: True

### Training Results
| Epoch   | Step     | Training Loss | Validation Loss |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0003  | 1        | 0.3874        | -               |
| 0.0135  | 50       | 0.3734        | -               |
| 0.0270  | 100      | 0.2741        | -               |
| 0.0405  | 150      | 0.2802        | -               |
| 0.0539  | 200      | 0.2355        | -               |
| 0.0674  | 250      | 0.2616        | -               |
| 0.0809  | 300      | 0.262         | -               |
| 0.0944  | 350      | 0.2302        | -               |
| 0.1079  | 400      | 0.1962        | -               |
| 0.1214  | 450      | 0.1438        | -               |
| 0.1348  | 500      | 0.2001        | -               |
| 0.1483  | 550      | 0.2126        | -               |
| 0.1618  | 600      | 0.1244        | -               |
| 0.1753  | 650      | 0.1968        | -               |
| 0.1888  | 700      | 0.1473        | -               |
| 0.2023  | 750      | 0.2407        | -               |
| 0.2157  | 800      | 0.1607        | -               |
| 0.2292  | 850      | 0.1376        | -               |
| 0.2427  | 900      | 0.145         | -               |
| 0.2562  | 950      | 0.1439        | -               |
| 0.2697  | 1000     | 0.0418        | -               |
| 0.2832  | 1050     | 0.0822        | -               |
| 0.2967  | 1100     | 0.1042        | -               |
| 0.3101  | 1150     | 0.0381        | -               |
| 0.3236  | 1200     | 0.17          | -               |
| 0.3371  | 1250     | 0.0253        | -               |
| 0.3506  | 1300     | 0.1009        | -               |
| 0.3641  | 1350     | 0.1355        | -               |
| 0.3776  | 1400     | 0.0314        | -               |
| 0.3910  | 1450     | 0.2185        | -               |
| 0.4045  | 1500     | 0.0774        | -               |
| 0.4180  | 1550     | 0.0512        | -               |
| 0.4315  | 1600     | 0.0814        | -               |
| 0.4450  | 1650     | 0.0169        | -               |
| 0.4585  | 1700     | 0.0591        | -               |
| 0.4720  | 1750     | 0.1232        | -               |
| 0.4854  | 1800     | 0.0941        | -               |
| 0.4989  | 1850     | 0.1024        | -               |
| 0.5124  | 1900     | 0.0031        | -               |
| 0.5259  | 1950     | 0.037         | -               |
| 0.5394  | 2000     | 0.1418        | -               |
| 0.5529  | 2050     | 0.0685        | -               |
| 0.5663  | 2100     | 0.0326        | -               |
| 0.5798  | 2150     | 0.0143        | -               |
| 0.5933  | 2200     | 0.064         | -               |
| 0.6068  | 2250     | 0.0612        | -               |
| 0.6203  | 2300     | 0.0689        | -               |
| 0.6338  | 2350     | 0.1402        | -               |
| 0.6472  | 2400     | 0.288         | -               |
| 0.6607  | 2450     | 0.0075        | -               |
| 0.6742  | 2500     | 0.0785        | -               |
| 0.6877  | 2550     | 0.0339        | -               |
| 0.7012  | 2600     | 0.0668        | -               |
| 0.7147  | 2650     | 0.0319        | -               |
| 0.7282  | 2700     | 0.0622        | -               |
| 0.7416  | 2750     | 0.1169        | -               |
| 0.7551  | 2800     | 0.0249        | -               |
| 0.7686  | 2850     | 0.0218        | -               |
| 0.7821  | 2900     | 0.0621        | -               |
| 0.7956  | 2950     | 0.0698        | -               |
| 0.8091  | 3000     | 0.0562        | -               |
| 0.8225  | 3050     | 0.0412        | -               |
| 0.8360  | 3100     | 0.0048        | -               |
| 0.8495  | 3150     | 0.0085        | -               |
| 0.8630  | 3200     | 0.0122        | -               |
| 0.8765  | 3250     | 0.0387        | -               |
| 0.8900  | 3300     | 0.0053        | -               |
| 0.9035  | 3350     | 0.0032        | -               |
| 0.9169  | 3400     | 0.0156        | -               |
| 0.9304  | 3450     | 0.0013        | -               |
| 0.9439  | 3500     | 0.001         | -               |
| 0.9574  | 3550     | 0.0009        | -               |
| 0.9709  | 3600     | 0.0025        | -               |
| 0.9844  | 3650     | 0.0006        | -               |
| 0.9978  | 3700     | 0.0832        | -               |
| 1.0     | 3708     | -             | 0.2776          |
| 1.0113  | 3750     | 0.0735        | -               |
| 1.0248  | 3800     | 0.0053        | -               |
| 1.0383  | 3850     | 0.0614        | -               |
| 1.0518  | 3900     | 0.0005        | -               |
| 1.0653  | 3950     | 0.0046        | -               |
| 1.0787  | 4000     | 0.0024        | -               |
| 1.0922  | 4050     | 0.0004        | -               |
| 1.1057  | 4100     | 0.0016        | -               |
| 1.1192  | 4150     | 0.0789        | -               |
| 1.1327  | 4200     | 0.0016        | -               |
| 1.1462  | 4250     | 0.0018        | -               |
| 1.1597  | 4300     | 0.0005        | -               |
| 1.1731  | 4350     | 0.0051        | -               |
| 1.1866  | 4400     | 0.0139        | -               |
| 1.2001  | 4450     | 0.0021        | -               |
| 1.2136  | 4500     | 0.0064        | -               |
| 1.2271  | 4550     | 0.0025        | -               |
| 1.2406  | 4600     | 0.0054        | -               |
| 1.2540  | 4650     | 0.0022        | -               |
| 1.2675  | 4700     | 0.0734        | -               |
| 1.2810  | 4750     | 0.026         | -               |
| 1.2945  | 4800     | 0.0004        | -               |
| 1.3080  | 4850     | 0.0574        | -               |
| 1.3215  | 4900     | 0.0043        | -               |
| 1.3350  | 4950     | 0.0975        | -               |
| 1.3484  | 5000     | 0.0125        | -               |
| 1.3619  | 5050     | 0.0045        | -               |
| 1.3754  | 5100     | 0.0011        | -               |
| 1.3889  | 5150     | 0.0061        | -               |
| 1.4024  | 5200     | 0.0004        | -               |
| 1.4159  | 5250     | 0.0278        | -               |
| 1.4293  | 5300     | 0.005         | -               |
| 1.4428  | 5350     | 0.0302        | -               |
| 1.4563  | 5400     | 0.0341        | -               |
| 1.4698  | 5450     | 0.0007        | -               |
| 1.4833  | 5500     | 0.0128        | -               |
| 1.4968  | 5550     | 0.0459        | -               |
| 1.5102  | 5600     | 0.0128        | -               |
| 1.5237  | 5650     | 0.0003        | -               |
| 1.5372  | 5700     | 0.004         | -               |
| 1.5507  | 5750     | 0.0005        | -               |
| 1.5642  | 5800     | 0.0005        | -               |
| 1.5777  | 5850     | 0.001         | -               |
| 1.5912  | 5900     | 0.0069        | -               |
| 1.6046  | 5950     | 0.0124        | -               |
| 1.6181  | 6000     | 0.0026        | -               |
| 1.6316  | 6050     | 0.0143        | -               |
| 1.6451  | 6100     | 0.0005        | -               |
| 1.6586  | 6150     | 0.0362        | -               |
| 1.6721  | 6200     | 0.0002        | -               |
| 1.6855  | 6250     | 0.0608        | -               |
| 1.6990  | 6300     | 0.0006        | -               |
| 1.7125  | 6350     | 0.0003        | -               |
| 1.7260  | 6400     | 0.0041        | -               |
| 1.7395  | 6450     | 0.0045        | -               |
| 1.7530  | 6500     | 0.0005        | -               |
| 1.7665  | 6550     | 0.0014        | -               |
| 1.7799  | 6600     | 0.0004        | -               |
| 1.7934  | 6650     | 0.0211        | -               |
| 1.8069  | 6700     | 0.0002        | -               |
| 1.8204  | 6750     | 0.0048        | -               |
| 1.8339  | 6800     | 0.0368        | -               |
| 1.8474  | 6850     | 0.0107        | -               |
| 1.8608  | 6900     | 0.0045        | -               |
| 1.8743  | 6950     | 0.0062        | -               |
| 1.8878  | 7000     | 0.0003        | -               |
| 1.9013  | 7050     | 0.0001        | -               |
| 1.9148  | 7100     | 0.0096        | -               |
| 1.9283  | 7150     | 0.0008        | -               |
| 1.9417  | 7200     | 0.0184        | -               |
| 1.9552  | 7250     | 0.0006        | -               |
| 1.9687  | 7300     | 0.0291        | -               |
| 1.9822  | 7350     | 0.0335        | -               |
| 1.9957  | 7400     | 0.0149        | -               |
| **2.0** | **7416** | **-**         | **0.2666**      |

* The bold row denotes the saved checkpoint.
### 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: 2.16.1
- 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.*
-->