---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/all-mpnet-base-v2
datasets:
- hojzas/proj9-lab1
metrics:
- accuracy
widget:
- text: '    try:\n        async with aiohttp.ClientSession(headers = fake_headers)
    as session:\n            async with session.get(url) as response:\n                outcome
    = response.status\n    except Exception as e:\n        outcome = e.__class__.__name__\n    return
    (outcome, url)'
- text: '    async with aiohttp.ClientSession() as current_session:\n        pairs
    = [fetch_url(current_session, url) for url in url_list]\n                res_pairs
    = await asyncio.gather(*pairs)\n        return res_pairs'
- text: tasks = [asyncio.create_task(fetch_single_url(url)) for url in urls]\n    results
    = asyncio.gather(*tasks)\n    return results
- text: '    coros = [get_url(url) for url in urls]\n    results = asyncio.get_event_loop().run_until_complete(asyncio.gather(*coros))\n    return
    results'
- text: '    tasks = [download_url(url) for url in urls]\n    results = asyncio.gather(*tasks)\n    return
    results'
pipeline_tag: text-classification
inference: true
---

# SetFit with sentence-transformers/all-mpnet-base-v2

This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [hojzas/proj9-lab1](https://huggingface.co/datasets/hojzas/proj9-lab1) dataset 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:** [hojzas/proj9-lab1](https://huggingface.co/datasets/hojzas/proj9-lab1)
<!-- - **Language:** Unknown -->
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### 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>'    async with aiohttp.ClientSession() as session:\\n        tasks = [fetch_url(session, url) for url in urls]\\n        return await asyncio.gather(*tasks)'</li><li>'    tasks = [download_url(url) for url in urls]\\n    results = await asyncio.gather(*tasks)\\n    return results'</li><li>'    async with ClientSession() as client_session:\\n        tasks = [asyncio.create_task(fetch_single_url(client_session, url)) for url in urls]\\n        results = await asyncio.gather(*tasks)\\n        return results'</li></ul> |
| 1     | <ul><li>'    coros = [get_url(url) for url in urls]\\n    results = asyncio.get_event_loop().run_until_complete(asyncio.gather(*coros))\\n    return results'</li><li>'    with aiohttp.ClientSession() as client:\\n        tasks = [retrieve_data(client, target) for target in urls]\\n        outcomes = asyncio.gather(*tasks)\\n        return outcomes'</li><li>'tasks = [asyncio.create_task(fetch_single_url(url)) for url in urls]\\n    results = asyncio.gather(*tasks)\\n    return results'</li></ul>                               |

## 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("hojzas/proj9")
# Run inference
preds = model("    tasks = [download_url(url) for url in urls]\n    results = asyncio.gather(*tasks)\n    return results")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 18  | 37.7333 | 76  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 8                     |
| 1     | 7                     |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0263 | 1    | 0.3316        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.2
- PyTorch: 2.3.0+cu121
- Datasets: 2.19.1
- Tokenizers: 0.19.1

## 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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