README.md

---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
datasets:
- hojzas/proj8-lab2
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(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'
- text: def first_with_given_key(iterable, key=repr):\n    used_keys = dict()\n    get_key
    = return_key(key)\n    for index in iterable:\n        index_key = get_key(index)\n        if
    index_key in used_keys.keys():\n            continue\n        try:\n            used_keys[hash(index_key)]
    = repr(index)\n        except TypeError:\n            used_keys[repr(index_key)]
    = repr(index)\n        yield index
- 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: true
co2_eq_emissions:
  emissions: 2.099245090500422
  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/proj8-lab2](https://huggingface.co/datasets/hojzas/proj8-lab2) 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:** 3 classes
- **Training Dataset:** [hojzas/proj8-lab2](https://huggingface.co/datasets/hojzas/proj8-lab2)
<!-- - **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>                                                                                                                                                                                             |
| 2     | <ul><li>'def first_with_given_key(iterable, key=repr):\\n    prev_keys = {}\\n    lamb_key = lambda item: key(item)\\n    for obj in iterable:\\n        obj_key = lamb_key(obj)\\n        if(obj_key) in prev_keys.keys():\\n            continue\\n        try:\\n            prev_keys[hash(obj_key)] = repr(obj)\\n        except TypeError:\\n            prev_keys[repr(obj_key)] = repr(obj)\\n        yield obj'</li><li>'def first_with_given_key(iterable, key=repr):\\n    used_keys = dict()\\n    get_key = lambda index: key(index)\\n    for index in iterable:\\n        index_key = get_key(index)\\n        if index_key in used_keys.keys():\\n            continue\\n        try:\\n            used_keys[hash(index_key)] = repr(index)\\n        except TypeError:\\n            used_keys[repr(index_key)] = repr(index)\\n        yield index'</li><li>'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'</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>                                                                 |

## 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-lab2")
# 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))")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 43  | 92.2069 | 125 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 13                    |
| 1     | 8                     |
| 2     | 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0137 | 1    | 0.4142        | -               |
| 0.6849 | 50   | 0.0024        | -               |

### 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->