---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
datasets:
- hojzas/proj4-match_permutations_substrings-lab1
metrics:
- accuracy
widget:
- text: '    counter = defaultdict(int)\n    for character in string:\n        counter[character]
    += 1\n    result = set()\n    for word in words:\n        word_counter = defaultdict(int)\n        for
    character in word:\n            word_counter[character] += 1\n        for key,
    count in word_counter.items():\n            if counter[key] < count:\n                break\n        else:\n            result.add(word)\n    return
    result'
- text: '    perms = all_permutations_substrings(string)\n    return set([x for x
    in list(perms) + words if x in list(perms) and x in words])'
- text: '    perms = all_permutations_substrings(string)\n    return set( perms.intersection(words))'
- text: '    perms = all_permutations_substrings(string)\n    for x in perms:\n        words.append(x)\n    dupes
    = [x for n, x in enumerate(words) if x in words[:n]]  \n    return set(dupes)'
- text: '    perms = all_permutations_substrings(string)\n    to_return = []\n    for
    w in words:\n        if w in perms:\n            to_return.append(w)\n    to_return
    = set(to_return)\n    return to_return'
pipeline_tag: text-classification
inference: true
co2_eq_emissions:
  emissions: 1.8025910115185662
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
  ram_total_size: 251.49161911010742
  hours_used: 0.006
  hardware_used: 4 x NVIDIA RTX A5000
base_model: sentence-transformers/all-mpnet-base-v2
---

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

This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [hojzas/proj4-match_permutations_substrings-lab1](https://huggingface.co/datasets/hojzas/proj4-match_permutations_substrings-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/proj4-match_permutations_substrings-lab1](https://huggingface.co/datasets/hojzas/proj4-match_permutations_substrings-lab1)
<!-- - **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>"    perms = all_permutations_substrings(string)\\n    return set(''.join(perm) for word in words for perm in perms if word == perm)"</li><li>'    perms = all_permutations_substrings(string)\\n    out = set()\\n    for w in words:\\n        for s in perms:\\n            if w == s:\\n                out.add(w)\\n    return out'</li><li>'    perms = all_permutations_substrings(string)\\n    return set(word for word in words if word in perms)'</li></ul> |
| 1     | <ul><li>'    perms = all_permutations_substrings(string)\\n    return perms.intersection(words)'</li><li>'    perms = all_permutations_substrings(string)\\n    return set.intersection(perms,words)'</li><li>'    perms = all_permutations_substrings(string)\\n    return set(perms).intersection(words)'</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/proj4-match_permutations_substrings-lab1")
# Run inference
preds = model("    perms = all_permutations_substrings(string)\n    return set( perms.intersection(words))")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 12  | 29.1633 | 140 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 35                    |
| 1     | 14                    |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0081 | 1    | 0.3668        | -               |
| 0.4065 | 50   | 0.0048        | -               |
| 0.8130 | 100  | 0.0014        | -               |

### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.002 kg of CO2
- **Hours Used**: 0.006 hours

### Training Hardware
- **On Cloud**: No
- **GPU Model**: 4 x NVIDIA RTX A5000
- **CPU Model**: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
- **RAM Size**: 251.49 GB

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- Transformers: 4.36.1
- PyTorch: 2.1.2+cu121
- Datasets: 2.14.7
- Tokenizers: 0.15.1

## 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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