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: 'Just finished yet another amazing book by Susan Elizabeth Philips God I love
    her books so much Which one to read next hmmm '
- text: I still miss him And i do nt think hes coming back
- text: 'seagull hates me and i m utterly depressed about it i miss him '
- text: ' eeek Your coming I m soo excited to see you on Thursday '
- text: 'Playin City of Villains wishin my buddies were playin with me '
inference: true
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: 0.7301231802911534
      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 [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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 3 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                                                                                                                                                                                                                                                                                                    |
|:---------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| neutral  | <ul><li>'ordered my new shirt'</li><li>'Yay got the Internet on my itouch working'</li><li>'Getting ready for work and the sun is shining plus its the w e Bgt tonight what am I gon na do after its finished '</li></ul>                                                                                   |
| positive | <ul><li>'Finally home after a night of dinner and drinking with friends Going to sleep now hoping the bed doesnt spin too much '</li><li>' Thank you I love my tattoos they are all very special to me My feet ones are beautiful '</li><li>'Day is going well so far Meeting until four though '</li></ul> |
| negative | <ul><li>' Oh final msg Why didnt you review my boardgame BookchaseA AA12 when you were on telly We didnt even get a nice letter '</li><li>'have to wear my glasses today cos my right eye is swollen and i dont know why'</li><li>' how crappy for him'</li></ul>                                           |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.7301   |

## 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("subham18/setfit-paraphrase-mpnet-base-v2-twitter-sentiment-cleaned-73")
# Run inference
preds = model("I still miss him And i do nt think hes coming back")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 13.9   | 31  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| Negative | 0                     |
| Positive | 0                     |
| Neutral  | 0                     |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- max_steps: -1
- sampling_strategy: oversampling
- 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 |
|:-------:|:-------:|:-------------:|:---------------:|
| 0.0011  | 1       | 0.3222        | -               |
| 0.0533  | 50      | 0.223         | -               |
| 0.1066  | 100     | 0.2817        | -               |
| 0.1599  | 150     | 0.1102        | -               |
| 0.2132  | 200     | 0.1271        | -               |
| 0.2665  | 250     | 0.0307        | -               |
| 0.3198  | 300     | 0.0013        | -               |
| 0.3731  | 350     | 0.0006        | -               |
| 0.4264  | 400     | 0.0006        | -               |
| 0.4797  | 450     | 0.0004        | -               |
| 0.5330  | 500     | 0.0006        | -               |
| 0.5864  | 550     | 0.0002        | -               |
| 0.6397  | 600     | 0.0003        | -               |
| 0.6930  | 650     | 0.0002        | -               |
| 0.7463  | 700     | 0.0002        | -               |
| 0.7996  | 750     | 0.0002        | -               |
| 0.8529  | 800     | 0.0002        | -               |
| 0.9062  | 850     | 0.0002        | -               |
| 0.9595  | 900     | 0.0005        | -               |
| **1.0** | **938** | **-**         | **0.2816**      |
| 1.0128  | 950     | 0.0001        | -               |
| 1.0661  | 1000    | 0.0027        | -               |
| 1.1194  | 1050    | 0.0002        | -               |
| 1.1727  | 1100    | 0.0002        | -               |
| 1.2260  | 1150    | 0.0001        | -               |
| 1.2793  | 1200    | 0.0003        | -               |
| 1.3326  | 1250    | 0.0001        | -               |
| 1.3859  | 1300    | 0.0002        | -               |
| 1.4392  | 1350    | 0.0001        | -               |
| 1.4925  | 1400    | 0.0001        | -               |
| 1.5458  | 1450    | 0.0001        | -               |
| 1.5991  | 1500    | 0.0001        | -               |
| 1.6525  | 1550    | 0.0001        | -               |
| 1.7058  | 1600    | 0.0001        | -               |
| 1.7591  | 1650    | 0.0001        | -               |
| 1.8124  | 1700    | 0.0001        | -               |
| 1.8657  | 1750    | 0.0002        | -               |
| 1.9190  | 1800    | 0.0001        | -               |
| 1.9723  | 1850    | 0.0001        | -               |
| 2.0     | 1876    | -             | 0.2846          |
| 2.0256  | 1900    | 0.0001        | -               |
| 2.0789  | 1950    | 0.0001        | -               |
| 2.1322  | 2000    | 0.0001        | -               |
| 2.1855  | 2050    | 0.0001        | -               |
| 2.2388  | 2100    | 0.0001        | -               |
| 2.2921  | 2150    | 0.0001        | -               |
| 2.3454  | 2200    | 0.0002        | -               |
| 2.3987  | 2250    | 0.0001        | -               |
| 2.4520  | 2300    | 0.0001        | -               |
| 2.5053  | 2350    | 0.0001        | -               |
| 2.5586  | 2400    | 0.0001        | -               |
| 2.6119  | 2450    | 0.0007        | -               |
| 2.6652  | 2500    | 0.0001        | -               |
| 2.7186  | 2550    | 0.0001        | -               |
| 2.7719  | 2600    | 0.0002        | -               |
| 2.8252  | 2650    | 0.0001        | -               |
| 2.8785  | 2700    | 0.0001        | -               |
| 2.9318  | 2750    | 0.0001        | -               |
| 2.9851  | 2800    | 0.0001        | -               |
| 3.0     | 2814    | -             | 0.2843          |
| 3.0384  | 2850    | 0.0001        | -               |
| 3.0917  | 2900    | 0.0001        | -               |
| 3.1450  | 2950    | 0.0001        | -               |
| 3.1983  | 3000    | 0.0001        | -               |
| 3.2516  | 3050    | 0.0002        | -               |
| 3.3049  | 3100    | 0.0001        | -               |
| 3.3582  | 3150    | 0.0001        | -               |
| 3.4115  | 3200    | 0.0001        | -               |
| 3.4648  | 3250    | 0.0001        | -               |
| 3.5181  | 3300    | 0.0           | -               |
| 3.5714  | 3350    | 0.0001        | -               |
| 3.6247  | 3400    | 0.0           | -               |
| 3.6780  | 3450    | 0.0           | -               |
| 3.7313  | 3500    | 0.0001        | -               |
| 3.7846  | 3550    | 0.0001        | -               |
| 3.8380  | 3600    | 0.0002        | -               |
| 3.8913  | 3650    | 0.0001        | -               |
| 3.9446  | 3700    | 0.0002        | -               |
| 3.9979  | 3750    | 0.0           | -               |
| 4.0     | 3752    | -             | 0.2861          |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.12.3
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.4.0+cu121
- Datasets: 2.21.0
- 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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