setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer dataset:hojzas/proj4-all-labs arxiv:2209.11055 base_model:sentence-transformers/all-mpnet-base-v2 base_model:finetune:sentence-transformers/all-mpnet-base-v2 co2_eq_emissions region:us
---
base_model: sentence-transformers/all-mpnet-base-v2
datasets:
- hojzas/proj4-all-labs
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: return list(dict.fromkeys(sorted(it)))
- text: '    perms = all_permutations_substrings(string)\n    result = perms & set(words)\n    return
    set(i for i in words if i in perms)'
- text: return [l for i, l in enumerate(it) if i == it.index(l)]
- text: "    unique_items = set(it)\n    return sorted(list(unique_items))"
- text: "    seen = set()\n    result = []\n    for word in it:\n        if word not\
    \ in seen:\n            result.append(word)\n            seen.add(word)\n    return\
    \ result"
inference: true
co2_eq_emissions:
  emissions: 6.0133985248367114
  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: 0.019
  hardware_used: 4 x NVIDIA RTX A5000
---

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

This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [hojzas/proj4-all-labs](https://huggingface.co/datasets/hojzas/proj4-all-labs) 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:** 7 classes
- **Training Dataset:** [hojzas/proj4-all-labs](https://huggingface.co/datasets/hojzas/proj4-all-labs)
<!-- - **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>"    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>                                                                                                                                                              |
| 3     | <ul><li>'    it = list(dict.fromkeys(it))\n    it.sort()\n    return it'</li><li>'    sequence = []\n    for i in it:\n        if i in sequence:\n           pass\n        else:\n            sequence.append(i)\n    sequence.sort()\n    return sequence'</li><li>'    unique = list(set(it))\n    unique.sort()\n    return unique'</li></ul>                                                                                                                                   |
| 2     | <ul><li>'return sorted(list({word : it.count(word) for (word) in set(it)}.keys())) '</li><li>'return list(dict.fromkeys(sorted(it)))'</li><li>'return sorted((list(dict.fromkeys(it)))) '</li></ul>                                                                                                                                                                                                                                                                                |
| 4     | <ul><li>'    unique_items = set(it)\n    return sorted(list(unique_items))'</li><li>'    letters = set(it)\n    sorted_letters = sorted(letters)\n    return sorted_letters'</li><li>'return list(sorted(set(it)))'</li></ul>                                                                                                                                                                                                                                                      |
| 5     | <ul><li>'    outputSequence = []\n    for input in it:\n        found = 0\n        for output in outputSequence:\n            if output == input:\n                found = 1\n                break\n        if not found:\n            outputSequence.append(input)\n    return outputSequence'</li><li>'  uniq = []\n  for char in it:\n    if not char in uniq:\n      uniq.append(char)\n  return uniq'</li><li>'return sorted(set(it), key=lambda y: it.index(y)) '</li></ul> |
| 6     | <ul><li>'return [tmp for tmp in dict.fromkeys(it).keys()]'</li><li>'return [i for i in dict.fromkeys(it)]'</li><li>'return list(dict.fromkeys(it))'</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-all-labs")
# Run inference
preds = model("return list(dict.fromkeys(sorted(it)))")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 2   | 25.0515 | 140 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 35                    |
| 1     | 14                    |
| 2     | 8                     |
| 3     | 10                    |
| 4     | 9                     |
| 5     | 13                    |
| 6     | 8                     |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0041 | 1    | 0.1745        | -               |
| 0.2058 | 50   | 0.0355        | -               |
| 0.4115 | 100  | 0.0168        | -               |
| 0.6173 | 150  | 0.0042        | -               |
| 0.8230 | 200  | 0.0075        | -               |

### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.006 kg of CO2
- **Hours Used**: 0.019 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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