README.md

---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
datasets:
- hojzas/proj8-multilabel
metrics:
- accuracy
widget:
- text: 'def first_with_given_key(iterable, key=lambda x: x):\n    keys_used = {}\n    for
    item in iterable:\n        rp = repr(key(item))\n        if rp not in keys_used.keys():\n            keys_used[rp]
    = repr(item)\n            yield item'
- text: 'def first_with_given_key(iterable, key=lambda x: x):\n    keys=[]\n    for
    i in iterable:\n        if key(i) not in keys:\n            yield i\n            keys.append(key(i))'
- text: 'def first_with_given_key(iterable, key=repr):\n    set_of_keys = set()\n    lambda_key
    = (lambda x: key(x))\n    for item in iterable:\n        key = lambda_key(item)\n        try:\n            key_for_set
    = hash(key)\n        except TypeError:\n            key_for_set = repr(key)\n        if
    key_for_set in set_of_keys:\n            continue\n        set_of_keys.add(key_for_set)\n        yield
    item'
- text: 'def first_with_given_key(iterable, key = lambda x: x):\n    found_keys={}\n    for
    i in iterable:\n        if key(i) not in found_keys.keys():\n            found_keys[key(i)]=i\n            yield
    i'
- text: 'def first_with_given_key(the_iterable, key=lambda x: x):\n    temp_keys=[]\n    for
    i in range(len(the_iterable)):\n        if (key(the_iterable[i]) not in temp_keys):\n            temp_keys.append(key(the_iterable[i]))\n            yield
    the_iterable[i]\n    del temp_keys'
pipeline_tag: text-classification
inference: false
co2_eq_emissions:
  emissions: 0.2716104726718793
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
  ram_total_size: 251.49160385131836
  hours_used: 0.005
base_model: sentence-transformers/paraphrase-mpnet-base-v2
---

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

This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [hojzas/proj8-multilabel](https://huggingface.co/datasets/hojzas/proj8-multilabel) dataset 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 OneVsRestClassifier 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 OneVsRestClassifier instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
- **Training Dataset:** [hojzas/proj8-multilabel](https://huggingface.co/datasets/hojzas/proj8-multilabel)
<!-- - **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)

## 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/setfit-proj8-multilabel")
# Run inference
preds = model("def first_with_given_key(iterable, key=lambda x: x):\n    keys=[]\n    for i in iterable:\n        if key(i) not in keys:\n            yield i\n            keys.append(key(i))")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 43  | 92.5185 | 125 |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0147 | 1    | 0.3001        | -               |
| 0.7353 | 50   | 0.0104        | -               |

### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.000 kg of CO2
- **Hours Used**: 0.005 hours

### Training Hardware
- **On Cloud**: No
- **GPU Model**: No GPU used
- **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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