---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
datasets:
- hojzas/proj8-lab1
metrics:
- accuracy
widget:
- text: "def first_with_given_key(iterable, key=repr):\n    res = []\n    keys = set()\n\
    \    for item in iterable:\n        if key(item) not in keys:\n            keys.add(key(item))\n\
    \    return res"
- text: "def first_with_given_key(iterable, key=repr):\n\tget_key = get_key_l(key)\n\
    \tused_keys = []\n\tfor item in iterable:\n\t\tkey_item = get_key(item)\n\t\t\t\
    \n\t\tif key_item in used_keys:\n\t\t\tcontinue\n\t\t\n\t\ttry:\n\t\t\tused_keys.append(hash(key_item))\n\
    \t\texcept TypeError:\n\t\t\tused_keys.apppend(repr(key_item))\n\t\t\t\n\t\tyield\
    \ item"
- text: "def first_with_given_key(iterable, key=repr):\n    set_of_keys = set()\n\
    \    key_lambda = _get_lambda(key)\n    for item in iterable:\n        key = key_lambda(item)\n\
    \        try:\n            key_to_set = hash(key)\n        except TypeError:\n\
    \            key_to_set = repr(key)\n\n        if key_to_set in set_of_keys:\n\
    \            continue\n        set_of_keys.add(key_to_set)\n        yield item"
- text: "def first_with_given_key(iterable, key=lambda y: y):\n    result = list()\n\
    \    func_it = iter(iterable)\n    while True:\n        try:\n            value\
    \ = next(func_it)\n            if key(value) not in result:\n                yield\
    \ value\n                result.insert(-1, key(value))\n        except StopIteration:\n\
    \            break"
- text: "def first_with_given_key(iterable, key=repr):\n    used_keys = {}\n    get_key\
    \ = return_key(key)\n    for item in iterable:\n        item_key = get_key(item)\n\
    \        if item_key in used_keys.keys():\n            continue\n        try:\n\
    \            used_keys[hash(item_key)] = repr(item)\n        except TypeError:\n\
    \            used_keys[repr(item_key)] = repr(item)\n        yield item"
pipeline_tag: text-classification
inference: true
co2_eq_emissions:
  emissions: 2.0314927247192536
  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
model-index:
- name: SetFit with sentence-transformers/all-mpnet-base-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: hojzas/proj8-lab1
      type: hojzas/proj8-lab1
      split: test
    metrics:
    - type: accuracy
      value: 0.9722222222222222
      name: Accuracy
---

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

This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [hojzas/proj8-lab1](https://huggingface.co/datasets/hojzas/proj8-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/proj8-lab1](https://huggingface.co/datasets/hojzas/proj8-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>'def first_with_given_key(iterable, key=lambda x: x):\\n    keys_in_list = []\\n    for it in iterable:\\n    if key(it) not in keys_in_list:\\n        keys_in_list.append(key(it))\\n        yield it'</li><li>'def first_with_given_key(iterable, key=lambda value: value):\\n    it = iter(iterable)\\n    saved_keys = []\\n    while True:\\n        try:\\n            value = next(it)\\n            if key(value) not in saved_keys:\\n                saved_keys.append(key(value))\\n                yield value\\n        except StopIteration:\\n            break'</li><li>'def first_with_given_key(iterable, key=None):\\n    if key is None:\\n        key = lambda x: x\\n    item_list = []\\n    key_set = set()\\n    for item in iterable:\\n        generated_item = key(item)\\n        if generated_item not in item_list:\\n            item_list.append(generated_item)\\n            yield item'</li></ul>                                                                                                                             |
| 1     | <ul><li>'def first_with_given_key(lst, key = lambda x: x):\\n    res = set()\\n    for i in lst:\\n        if repr(key(i)) not in res:\\n            res.add(repr(key(i)))\\n            yield i'</li><li>'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'</li><li>'def first_with_given_key(iterable, key=None):\\n    if key is None:\\n        key = identity\\n    appeared_keys = set()\\n    for item in iterable:\\n        generated_key = key(item)\\n        if not generated_key.__hash__:\\n            generated_key = repr(generated_key)\\n        if generated_key not in appeared_keys:\\n            appeared_keys.add(generated_key)\\n            yield item'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9722   |

## 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/proj8-lab1")
# Run inference
preds = model("def first_with_given_key(iterable, key=repr):
    res = []
    keys = set()
    for item in iterable:
        if key(item) not in keys:
            keys.add(key(item))
    return res")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 43  | 91.6071 | 125 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 20                    |
| 1     | 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0143 | 1    | 0.4043        | -               |
| 0.7143 | 50   | 0.0022        | -               |

### 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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