---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/all-MiniLM-L12-v2
metrics:
- accuracy
widget:
- text: Could you provide the average temperature, annual rainfall in Paris?
- text: Can you provide a summary of the key points discussed about urban development?
- text: Compare ces deux documents
- text: What are the steps required to apply for a passport?
- text: What is the basic definition of seismic design?
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with sentence-transformers/all-MiniLM-L12-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.7333333333333333
name: Accuracy
---
# SetFit with sentence-transformers/all-MiniLM-L12-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L12-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-MiniLM-L12-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L12-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 tokens
- **Number of Classes:** 5 classes
### 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 |
|:-----------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| sub_queries | - 'How can I use 3D print to build a bridge and how much would it be?'
- 'Pourriez-vous détailler les critères spécifiques utilisés pour évaluer la durabilité des matériaux de construction, les types de systèmes HVAC les plus efficaces actuellement en usage dans les bâtiments verts, et les différentes méthodes employées pour réduire les déchets pendant la phase de construction ?'
- 'Comment faire une etude de marche? Quelles sont les meilleures sources?'
|
| summary | - 'Quelles informations primordiales me conseillez-vous de mémoriser de ce document'
- 'Quels sont les points principaux à retenir'
- 'What is the primary theme of the document ?'
|
| exchange | - 'Pourriez-vous me fournir un résumé des points clés abordés dans notre discussion précédente ?'
- 'Quels sont les points clés abordés dans notre discussion précédente ?'
- 'Could you restate the main points discussed about acoustic engineering?'
|
| simple_questions | - 'Quelle est le principal moteur de la croissance économique ? Fais un post linkedin sur le sujet'
- 'Pourriez-vous résumer les bénéfices que les utilisateurs peuvent tirer des récentes avancées en matériel informatique ?'
- 'What is the purpose of environmental impact assessments?'
|
| compare | - 'Compare the methodologies'
- 'Compare the nutritional information provided on these food labels'
- 'Analysez comment la structure narrative de ces manuscrits influence leur message'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.7333 |
## 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("egis-group/router_mini_lm_l6")
# Run inference
preds = model("Compare ces deux documents")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 3 | 13.4636 | 48 |
| Label | Training Sample Count |
|:---------|:----------------------|
| negative | 0 |
| positive | 0 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- 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: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0003 | 1 | 0.3239 | - |
| 0.0152 | 50 | 0.3443 | - |
| 0.0304 | 100 | 0.2282 | - |
| 0.0456 | 150 | 0.2576 | - |
| 0.0608 | 200 | 0.2587 | - |
| 0.0760 | 250 | 0.1747 | - |
| 0.0912 | 300 | 0.1916 | - |
| 0.1064 | 350 | 0.1638 | - |
| 0.1216 | 400 | 0.1459 | - |
| 0.1368 | 450 | 0.1322 | - |
| 0.1520 | 500 | 0.038 | - |
| 0.1672 | 550 | 0.0636 | - |
| 0.1824 | 600 | 0.0613 | - |
| 0.1976 | 650 | 0.0322 | - |
| 0.2128 | 700 | 0.0159 | - |
| 0.2280 | 750 | 0.0029 | - |
| 0.2432 | 800 | 0.0012 | - |
| 0.2584 | 850 | 0.0019 | - |
| 0.2736 | 900 | 0.0025 | - |
| 0.2888 | 950 | 0.0028 | - |
| 0.3040 | 1000 | 0.001 | - |
| 0.3192 | 1050 | 0.0014 | - |
| 0.3344 | 1100 | 0.0007 | - |
| 0.3497 | 1150 | 0.001 | - |
| 0.3649 | 1200 | 0.0014 | - |
| 0.3801 | 1250 | 0.0003 | - |
| 0.3953 | 1300 | 0.0005 | - |
| 0.4105 | 1350 | 0.0003 | - |
| 0.4257 | 1400 | 0.0004 | - |
| 0.4409 | 1450 | 0.0003 | - |
| 0.4561 | 1500 | 0.0004 | - |
| 0.4713 | 1550 | 0.0003 | - |
| 0.4865 | 1600 | 0.0002 | - |
| 0.5017 | 1650 | 0.0004 | - |
| 0.5169 | 1700 | 0.0003 | - |
| 0.5321 | 1750 | 0.0003 | - |
| 0.5473 | 1800 | 0.0004 | - |
| 0.5625 | 1850 | 0.0002 | - |
| 0.5777 | 1900 | 0.0001 | - |
| 0.5929 | 1950 | 0.0001 | - |
| 0.6081 | 2000 | 0.0003 | - |
| 0.6233 | 2050 | 0.0002 | - |
| 0.6385 | 2100 | 0.0001 | - |
| 0.6537 | 2150 | 0.0002 | - |
| 0.6689 | 2200 | 0.0002 | - |
| 0.6841 | 2250 | 0.0001 | - |
| 0.6993 | 2300 | 0.0002 | - |
| 0.7145 | 2350 | 0.0003 | - |
| 0.7297 | 2400 | 0.0002 | - |
| 0.7449 | 2450 | 0.0002 | - |
| 0.7601 | 2500 | 0.0001 | - |
| 0.7753 | 2550 | 0.0002 | - |
| 0.7905 | 2600 | 0.0001 | - |
| 0.8057 | 2650 | 0.0001 | - |
| 0.8209 | 2700 | 0.0001 | - |
| 0.8361 | 2750 | 0.0001 | - |
| 0.8513 | 2800 | 0.0001 | - |
| 0.8665 | 2850 | 0.0001 | - |
| 0.8817 | 2900 | 0.0001 | - |
| 0.8969 | 2950 | 0.0001 | - |
| 0.9121 | 3000 | 0.0001 | - |
| 0.9273 | 3050 | 0.0001 | - |
| 0.9425 | 3100 | 0.0001 | - |
| 0.9577 | 3150 | 0.0001 | - |
| 0.9729 | 3200 | 0.0001 | - |
| 0.9881 | 3250 | 0.0001 | - |
| 1.0 | 3289 | - | 0.0982 |
| 1.0033 | 3300 | 0.0001 | - |
| 1.0185 | 3350 | 0.0001 | - |
| 1.0337 | 3400 | 0.0001 | - |
| 1.0490 | 3450 | 0.0001 | - |
| 1.0642 | 3500 | 0.0001 | - |
| 1.0794 | 3550 | 0.0249 | - |
| 1.0946 | 3600 | 0.0002 | - |
| 1.1098 | 3650 | 0.0001 | - |
| 1.1250 | 3700 | 0.0001 | - |
| 1.1402 | 3750 | 0.0001 | - |
| 1.1554 | 3800 | 0.0001 | - |
| 1.1706 | 3850 | 0.0001 | - |
| 1.1858 | 3900 | 0.0001 | - |
| 1.2010 | 3950 | 0.0001 | - |
| 1.2162 | 4000 | 0.0001 | - |
| 1.2314 | 4050 | 0.0 | - |
| 1.2466 | 4100 | 0.0001 | - |
| 1.2618 | 4150 | 0.0 | - |
| 1.2770 | 4200 | 0.0001 | - |
| 1.2922 | 4250 | 0.0 | - |
| 1.3074 | 4300 | 0.0001 | - |
| 1.3226 | 4350 | 0.0001 | - |
| 1.3378 | 4400 | 0.0001 | - |
| 1.3530 | 4450 | 0.0001 | - |
| 1.3682 | 4500 | 0.0001 | - |
| 1.3834 | 4550 | 0.0001 | - |
| 1.3986 | 4600 | 0.0001 | - |
| 1.4138 | 4650 | 0.0001 | - |
| 1.4290 | 4700 | 0.0001 | - |
| 1.4442 | 4750 | 0.0001 | - |
| 1.4594 | 4800 | 0.0001 | - |
| 1.4746 | 4850 | 0.0001 | - |
| 1.4898 | 4900 | 0.0 | - |
| 1.5050 | 4950 | 0.0 | - |
| 1.5202 | 5000 | 0.0 | - |
| 1.5354 | 5050 | 0.0 | - |
| 1.5506 | 5100 | 0.0 | - |
| 1.5658 | 5150 | 0.0001 | - |
| 1.5810 | 5200 | 0.0001 | - |
| 1.5962 | 5250 | 0.0 | - |
| 1.6114 | 5300 | 0.0 | - |
| 1.6266 | 5350 | 0.0001 | - |
| 1.6418 | 5400 | 0.0001 | - |
| 1.6570 | 5450 | 0.0 | - |
| 1.6722 | 5500 | 0.0001 | - |
| 1.6874 | 5550 | 0.0 | - |
| 1.7026 | 5600 | 0.0001 | - |
| 1.7178 | 5650 | 0.0 | - |
| 1.7330 | 5700 | 0.0001 | - |
| 1.7483 | 5750 | 0.0001 | - |
| 1.7635 | 5800 | 0.0001 | - |
| 1.7787 | 5850 | 0.0001 | - |
| 1.7939 | 5900 | 0.0 | - |
| 1.8091 | 5950 | 0.0001 | - |
| 1.8243 | 6000 | 0.0001 | - |
| 1.8395 | 6050 | 0.0 | - |
| 1.8547 | 6100 | 0.0001 | - |
| 1.8699 | 6150 | 0.0 | - |
| 1.8851 | 6200 | 0.0 | - |
| 1.9003 | 6250 | 0.0 | - |
| 1.9155 | 6300 | 0.0 | - |
| 1.9307 | 6350 | 0.0001 | - |
| 1.9459 | 6400 | 0.0 | - |
| 1.9611 | 6450 | 0.0 | - |
| 1.9763 | 6500 | 0.0001 | - |
| 1.9915 | 6550 | 0.0 | - |
| **2.0** | **6578** | **-** | **0.0939** |
| 2.0067 | 6600 | 0.0001 | - |
| 2.0219 | 6650 | 0.0001 | - |
| 2.0371 | 6700 | 0.0001 | - |
| 2.0523 | 6750 | 0.0001 | - |
| 2.0675 | 6800 | 0.0 | - |
| 2.0827 | 6850 | 0.0 | - |
| 2.0979 | 6900 | 0.0 | - |
| 2.1131 | 6950 | 0.0 | - |
| 2.1283 | 7000 | 0.0001 | - |
| 2.1435 | 7050 | 0.0001 | - |
| 2.1587 | 7100 | 0.0 | - |
| 2.1739 | 7150 | 0.0 | - |
| 2.1891 | 7200 | 0.0001 | - |
| 2.2043 | 7250 | 0.0001 | - |
| 2.2195 | 7300 | 0.0 | - |
| 2.2347 | 7350 | 0.0 | - |
| 2.2499 | 7400 | 0.0 | - |
| 2.2651 | 7450 | 0.0 | - |
| 2.2803 | 7500 | 0.0 | - |
| 2.2955 | 7550 | 0.0001 | - |
| 2.3107 | 7600 | 0.0 | - |
| 2.3259 | 7650 | 0.0001 | - |
| 2.3411 | 7700 | 0.0 | - |
| 2.3563 | 7750 | 0.0001 | - |
| 2.3715 | 7800 | 0.0 | - |
| 2.3867 | 7850 | 0.0001 | - |
| 2.4019 | 7900 | 0.0 | - |
| 2.4171 | 7950 | 0.0 | - |
| 2.4324 | 8000 | 0.0 | - |
| 2.4476 | 8050 | 0.0001 | - |
| 2.4628 | 8100 | 0.0001 | - |
| 2.4780 | 8150 | 0.0 | - |
| 2.4932 | 8200 | 0.0001 | - |
| 2.5084 | 8250 | 0.0001 | - |
| 2.5236 | 8300 | 0.0001 | - |
| 2.5388 | 8350 | 0.0 | - |
| 2.5540 | 8400 | 0.0 | - |
| 2.5692 | 8450 | 0.0 | - |
| 2.5844 | 8500 | 0.0 | - |
| 2.5996 | 8550 | 0.0 | - |
| 2.6148 | 8600 | 0.0 | - |
| 2.6300 | 8650 | 0.0 | - |
| 2.6452 | 8700 | 0.0 | - |
| 2.6604 | 8750 | 0.0 | - |
| 2.6756 | 8800 | 0.0 | - |
| 2.6908 | 8850 | 0.0 | - |
| 2.7060 | 8900 | 0.0001 | - |
| 2.7212 | 8950 | 0.0 | - |
| 2.7364 | 9000 | 0.0 | - |
| 2.7516 | 9050 | 0.0001 | - |
| 2.7668 | 9100 | 0.0 | - |
| 2.7820 | 9150 | 0.0 | - |
| 2.7972 | 9200 | 0.0 | - |
| 2.8124 | 9250 | 0.0 | - |
| 2.8276 | 9300 | 0.0 | - |
| 2.8428 | 9350 | 0.0 | - |
| 2.8580 | 9400 | 0.0 | - |
| 2.8732 | 9450 | 0.0 | - |
| 2.8884 | 9500 | 0.0 | - |
| 2.9036 | 9550 | 0.0 | - |
| 2.9188 | 9600 | 0.0 | - |
| 2.9340 | 9650 | 0.0 | - |
| 2.9492 | 9700 | 0.0 | - |
| 2.9644 | 9750 | 0.0 | - |
| 2.9796 | 9800 | 0.0 | - |
| 2.9948 | 9850 | 0.0 | - |
| 3.0 | 9867 | - | 0.0951 |
| 3.0100 | 9900 | 0.0 | - |
| 3.0252 | 9950 | 0.0 | - |
| 3.0404 | 10000 | 0.0 | - |
| 3.0556 | 10050 | 0.0 | - |
| 3.0708 | 10100 | 0.0 | - |
| 3.0860 | 10150 | 0.0 | - |
| 3.1012 | 10200 | 0.0 | - |
| 3.1164 | 10250 | 0.0 | - |
| 3.1317 | 10300 | 0.0 | - |
| 3.1469 | 10350 | 0.0 | - |
| 3.1621 | 10400 | 0.0 | - |
| 3.1773 | 10450 | 0.0001 | - |
| 3.1925 | 10500 | 0.0 | - |
| 3.2077 | 10550 | 0.0 | - |
| 3.2229 | 10600 | 0.0 | - |
| 3.2381 | 10650 | 0.0 | - |
| 3.2533 | 10700 | 0.0 | - |
| 3.2685 | 10750 | 0.0 | - |
| 3.2837 | 10800 | 0.0 | - |
| 3.2989 | 10850 | 0.0 | - |
| 3.3141 | 10900 | 0.0 | - |
| 3.3293 | 10950 | 0.0 | - |
| 3.3445 | 11000 | 0.0 | - |
| 3.3597 | 11050 | 0.0 | - |
| 3.3749 | 11100 | 0.0 | - |
| 3.3901 | 11150 | 0.0 | - |
| 3.4053 | 11200 | 0.0 | - |
| 3.4205 | 11250 | 0.0 | - |
| 3.4357 | 11300 | 0.0 | - |
| 3.4509 | 11350 | 0.0 | - |
| 3.4661 | 11400 | 0.0 | - |
| 3.4813 | 11450 | 0.0 | - |
| 3.4965 | 11500 | 0.0 | - |
| 3.5117 | 11550 | 0.0 | - |
| 3.5269 | 11600 | 0.0 | - |
| 3.5421 | 11650 | 0.0 | - |
| 3.5573 | 11700 | 0.0 | - |
| 3.5725 | 11750 | 0.0 | - |
| 3.5877 | 11800 | 0.0 | - |
| 3.6029 | 11850 | 0.0 | - |
| 3.6181 | 11900 | 0.0 | - |
| 3.6333 | 11950 | 0.0 | - |
| 3.6485 | 12000 | 0.0 | - |
| 3.6637 | 12050 | 0.0 | - |
| 3.6789 | 12100 | 0.0 | - |
| 3.6941 | 12150 | 0.0 | - |
| 3.7093 | 12200 | 0.0 | - |
| 3.7245 | 12250 | 0.0 | - |
| 3.7397 | 12300 | 0.0 | - |
| 3.7549 | 12350 | 0.0 | - |
| 3.7701 | 12400 | 0.0 | - |
| 3.7853 | 12450 | 0.0 | - |
| 3.8005 | 12500 | 0.0 | - |
| 3.8157 | 12550 | 0.0 | - |
| 3.8310 | 12600 | 0.0 | - |
| 3.8462 | 12650 | 0.0 | - |
| 3.8614 | 12700 | 0.0 | - |
| 3.8766 | 12750 | 0.0 | - |
| 3.8918 | 12800 | 0.0 | - |
| 3.9070 | 12850 | 0.0 | - |
| 3.9222 | 12900 | 0.0 | - |
| 3.9374 | 12950 | 0.0 | - |
| 3.9526 | 13000 | 0.0 | - |
| 3.9678 | 13050 | 0.0 | - |
| 3.9830 | 13100 | 0.0 | - |
| 3.9982 | 13150 | 0.0 | - |
| 4.0 | 13156 | - | 0.0954 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.3.0+cu121
- Datasets: 2.19.2
- Tokenizers: 0.15.2
## 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}
}
```