README.md

---
base_model: sentence-transformers/all-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
- precision
- recall
- f1
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: chatgpt , in response to this , will propose an appropriate emoticon response
    for that comment as well generate the specific text of a comment reply for me
    to give .
- text: 'text - to - speech : once chatgpt has generated a response , you will need
    to convert it into speech using a text - to - speech api , such as google text
    - to - speech or microsoft azure text - to - speech . '
- text: 'yes , i know that chatgpt said this many many times , but this is a fact
    that people need to eat it up and move on . '
- text: 'explanation : this makes it easier for chatgpt to understand and generate
    a response for each individual point . '
- text: 'chatgpt makes choices , algorithms are n''t neutral . '
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: 0.75
      name: Accuracy
    - type: precision
      value: 0.7666666666666666
      name: Precision
    - type: recall
      value: 0.746031746031746
      name: Recall
    - type: f1
      value: 0.7488344988344989
      name: F1
---

# 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:** 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                                                                                                                                                                                                                                                                                                                                                                                                                         |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1     | <ul><li>'i panicked and made chatgpt write everything . '</li><li>'it is fundamental that chatgpt with developer mode can say anything about anyone , at any time for any reason . '</li><li>"chatgpt itself mentioned that homebrewing requires good system mastery and an understanding of the developer 's game philosophy to make properly balanced feats . "</li></ul>                                                      |
| 0     | <ul><li>'chatgpt confirmed it . '</li><li>"chatgpt does n't know that it writing text that is easily detected . "</li><li>"the timing of entering the initial prompt is essential to ensure that chatgpt understands the user 's request and can provide an accurate response . "</li></ul>                                                                                                                                      |
| 2     | <ul><li>'3 . diversion : chatgpt might also create a diversion , directing a group of wasps to move away from the nest and act as a decoy . '</li><li>'chatgpt can generate content on a wide range of subjects , so the possibilities are endless . '</li><li>'does anyone know if chatgpt can generate the code of a sound wave , with the specifications that are requested , as it does with programming codes . '</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy | Precision | Recall | F1     |
|:--------|:---------|:----------|:-------|:-------|
| **all** | 0.75     | 0.7667    | 0.7460 | 0.7488 |

## 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("setfit_model_id")
# Run inference
preds = model("chatgpt makes choices , algorithms are n't neutral . ")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 3   | 20.7848 | 51  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 26                    |
| 1     | 27                    |
| 2     | 26                    |

### Training Hyperparameters
- batch_size: (32, 2)
- num_epochs: (10, 10)
- 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
- l2_weight: 0.01
- seed: 42
- evaluation_strategy: epoch
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0077 | 1    | 0.2555        | -               |
| 0.3846 | 50   | 0.2528        | -               |
| 0.7692 | 100  | 0.1993        | -               |
| 1.0    | 130  | -             | 0.1527          |
| 1.1538 | 150  | 0.0222        | -               |
| 1.5385 | 200  | 0.0023        | -               |
| 1.9231 | 250  | 0.0013        | -               |
| 2.0    | 260  | -             | 0.1461          |
| 2.3077 | 300  | 0.0015        | -               |
| 2.6923 | 350  | 0.0005        | -               |
| 3.0    | 390  | -             | 0.1465          |
| 3.0769 | 400  | 0.0003        | -               |
| 3.4615 | 450  | 0.0002        | -               |
| 3.8462 | 500  | 0.0003        | -               |
| 4.0    | 520  | -             | 0.1353          |
| 4.2308 | 550  | 0.0007        | -               |
| 4.6154 | 600  | 0.0002        | -               |
| 5.0    | 650  | 0.0011        | 0.1491          |
| 5.3846 | 700  | 0.0002        | -               |
| 5.7692 | 750  | 0.0002        | -               |
| 6.0    | 780  | -             | 0.1478          |
| 6.1538 | 800  | 0.0002        | -               |
| 6.5385 | 850  | 0.0001        | -               |
| 6.9231 | 900  | 0.0001        | -               |
| 7.0    | 910  | -             | 0.1472          |
| 7.3077 | 950  | 0.0001        | -               |
| 7.6923 | 1000 | 0.0001        | -               |
| 8.0    | 1040 | -             | 0.1461          |
| 8.0769 | 1050 | 0.0001        | -               |
| 8.4615 | 1100 | 0.0001        | -               |
| 8.8462 | 1150 | 0.0001        | -               |
| 9.0    | 1170 | -             | 0.1393          |
| 9.2308 | 1200 | 0.0001        | -               |
| 9.6154 | 1250 | 0.0001        | -               |
| 10.0   | 1300 | 0.0001        | 0.1399          |

### Framework Versions
- Python: 3.11.7
- SetFit: 1.1.0
- Sentence Transformers: 3.2.0
- Transformers: 4.45.2
- PyTorch: 2.4.1
- Datasets: 3.0.1
- Tokenizers: 0.20.1

## 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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