---
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)
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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>'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))")
```
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## 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}
}
```
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