---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
datasets:
- hojzas/proj4-all-labs
metrics:
- accuracy
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"
pipeline_tag: text-classification
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
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/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)
### 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 |
- " perms = all_permutations_substrings(string)\\n return set(''.join(perm) for word in words for perm in perms if word == perm)"
- ' 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'
- ' perms = all_permutations_substrings(string)\\n return set(word for word in words if word in perms)'
|
| 1 | - ' perms = all_permutations_substrings(string)\\n return perms.intersection(words)'
- ' perms = all_permutations_substrings(string)\\n return set.intersection(perms,words)'
- ' perms = all_permutations_substrings(string)\\n return set(perms).intersection(words)'
|
| 3 | - ' it = list(dict.fromkeys(it))\n it.sort()\n return it'
- ' 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'
- ' unique = list(set(it))\n unique.sort()\n return unique'
|
| 2 | - 'return sorted(list({word : it.count(word) for (word) in set(it)}.keys())) '
- 'return list(dict.fromkeys(sorted(it)))'
- 'return sorted((list(dict.fromkeys(it)))) '
|
| 4 | - ' unique_items = set(it)\n return sorted(list(unique_items))'
- ' letters = set(it)\n sorted_letters = sorted(letters)\n return sorted_letters'
- 'return list(sorted(set(it)))'
|
| 5 | - ' 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'
- ' uniq = []\n for char in it:\n if not char in uniq:\n uniq.append(char)\n return uniq'
- 'return sorted(set(it), key=lambda y: it.index(y)) '
|
| 6 | - 'return [tmp for tmp in dict.fromkeys(it).keys()]'
- 'return [i for i in dict.fromkeys(it)]'
- 'return list(dict.fromkeys(it))'
|
## 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)))")
```
## 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}
}
```