---
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: Quel est le principal litige dans les projets de construction, et quel droit
de la partie accusee
- text: Clarifier quels sont les facteurs déterminants dans le choix d'un emplacement
pour un nouveau magasin
- text: Compare ces deux documents
- text: Can you explain the process of wind energy generation and discuss its environmental
impacts compared to those of hydroelectric power?
- text: Could you restate the advantages of using project management software that
were mentioned earlier? Provide a linkedin post about it
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: 0.9333333333333333
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 | - 'Could you break down the main factors I should consider when researching market prices and how to effectively communicate our needs to the supplier during negotiations?'
- 'Comment faire pousser une plante et le mesurer ?'
- "Quel est le meilleur matériau pour l'isolation phonique et thermique?"
|
| simple_questions | - 'What are the key strategies for maintaining efficient communication in a remote work environment?'
- 'Could you summarize the ways a person can help in adapting to climate change ?'
- 'What are the current trends in construction?'
|
| exchange | - 'Could you please restate your last explanation using simpler terms?'
- 'Could you restate the impact of augmented reality on design practices?'
- 'Pourriez-vous me donner un résumé des principaux points abordés dans notre conversation précédente ?'
|
| compare | - 'How do the conclusions differ?'
- 'Contrast the main arguments presented in each paper'
- 'Quelles sont les principales différences dans les programmes éducatifs décrits dans ces documents ?'
|
| summary | - 'Que dois-je retenir de ce doc ?'
- 'What are the key assertions made within the text'
- 'What are the most important argument stated in the document?'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9333 |
## 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_l12")
# Run inference
preds = model("Compare ces deux documents")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 4 | 13.4389 | 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 | 0.4073 | - |
| 0.0151 | 50 | 0.3054 | - |
| 0.0303 | 100 | 0.2066 | - |
| 0.0454 | 150 | 0.2664 | - |
| 0.0606 | 200 | 0.2463 | - |
| 0.0757 | 250 | 0.214 | - |
| 0.0909 | 300 | 0.1892 | - |
| 0.1060 | 350 | 0.1402 | - |
| 0.1212 | 400 | 0.1804 | - |
| 0.1363 | 450 | 0.0571 | - |
| 0.1515 | 500 | 0.0979 | - |
| 0.1666 | 550 | 0.1775 | - |
| 0.1818 | 600 | 0.0377 | - |
| 0.1969 | 650 | 0.0398 | - |
| 0.2121 | 700 | 0.0423 | - |
| 0.2272 | 750 | 0.0036 | - |
| 0.2424 | 800 | 0.0079 | - |
| 0.2575 | 850 | 0.0049 | - |
| 0.2726 | 900 | 0.0018 | - |
| 0.2878 | 950 | 0.0018 | - |
| 0.3029 | 1000 | 0.0032 | - |
| 0.3181 | 1050 | 0.0019 | - |
| 0.3332 | 1100 | 0.0008 | - |
| 0.3484 | 1150 | 0.0006 | - |
| 0.3635 | 1200 | 0.0006 | - |
| 0.3787 | 1250 | 0.0011 | - |
| 0.3938 | 1300 | 0.0005 | - |
| 0.4090 | 1350 | 0.001 | - |
| 0.4241 | 1400 | 0.0009 | - |
| 0.4393 | 1450 | 0.0004 | - |
| 0.4544 | 1500 | 0.0003 | - |
| 0.4696 | 1550 | 0.0003 | - |
| 0.4847 | 1600 | 0.0006 | - |
| 0.4998 | 1650 | 0.0003 | - |
| 0.5150 | 1700 | 0.0002 | - |
| 0.5301 | 1750 | 0.0002 | - |
| 0.5453 | 1800 | 0.0005 | - |
| 0.5604 | 1850 | 0.0003 | - |
| 0.5756 | 1900 | 0.0002 | - |
| 0.5907 | 1950 | 0.0002 | - |
| 0.6059 | 2000 | 0.0001 | - |
| 0.6210 | 2050 | 0.0002 | - |
| 0.6362 | 2100 | 0.0002 | - |
| 0.6513 | 2150 | 0.0001 | - |
| 0.6665 | 2200 | 0.0002 | - |
| 0.6816 | 2250 | 0.0002 | - |
| 0.6968 | 2300 | 0.0002 | - |
| 0.7119 | 2350 | 0.0002 | - |
| 0.7271 | 2400 | 0.0002 | - |
| 0.7422 | 2450 | 0.0002 | - |
| 0.7573 | 2500 | 0.0001 | - |
| 0.7725 | 2550 | 0.0001 | - |
| 0.7876 | 2600 | 0.0002 | - |
| 0.8028 | 2650 | 0.0001 | - |
| 0.8179 | 2700 | 0.0002 | - |
| 0.8331 | 2750 | 0.0007 | - |
| 0.8482 | 2800 | 0.0001 | - |
| 0.8634 | 2850 | 0.0001 | - |
| 0.8785 | 2900 | 0.0001 | - |
| 0.8937 | 2950 | 0.0001 | - |
| 0.9088 | 3000 | 0.0001 | - |
| 0.9240 | 3050 | 0.0002 | - |
| 0.9391 | 3100 | 0.0001 | - |
| 0.9543 | 3150 | 0.0001 | - |
| 0.9694 | 3200 | 0.0001 | - |
| 0.9846 | 3250 | 0.0001 | - |
| 0.9997 | 3300 | 0.0002 | - |
| 1.0 | 3301 | - | 0.0001 |
| 1.0148 | 3350 | 0.0003 | - |
| 1.0300 | 3400 | 0.0002 | - |
| 1.0451 | 3450 | 0.0001 | - |
| 1.0603 | 3500 | 0.0001 | - |
| 1.0754 | 3550 | 0.0001 | - |
| 1.0906 | 3600 | 0.0001 | - |
| 1.1057 | 3650 | 0.0001 | - |
| 1.1209 | 3700 | 0.0002 | - |
| 1.1360 | 3750 | 0.0001 | - |
| 1.1512 | 3800 | 0.0001 | - |
| 1.1663 | 3850 | 0.0001 | - |
| 1.1815 | 3900 | 0.0001 | - |
| 1.1966 | 3950 | 0.001 | - |
| 1.2118 | 4000 | 0.0001 | - |
| 1.2269 | 4050 | 0.0001 | - |
| 1.2420 | 4100 | 0.0001 | - |
| 1.2572 | 4150 | 0.0001 | - |
| 1.2723 | 4200 | 0.0001 | - |
| 1.2875 | 4250 | 0.0001 | - |
| 1.3026 | 4300 | 0.0001 | - |
| 1.3178 | 4350 | 0.0 | - |
| 1.3329 | 4400 | 0.0001 | - |
| 1.3481 | 4450 | 0.0001 | - |
| 1.3632 | 4500 | 0.0001 | - |
| 1.3784 | 4550 | 0.0001 | - |
| 1.3935 | 4600 | 0.0001 | - |
| 1.4087 | 4650 | 0.0001 | - |
| 1.4238 | 4700 | 0.0001 | - |
| 1.4390 | 4750 | 0.0001 | - |
| 1.4541 | 4800 | 0.0 | - |
| 1.4693 | 4850 | 0.0 | - |
| 1.4844 | 4900 | 0.0001 | - |
| 1.4995 | 4950 | 0.0001 | - |
| 1.5147 | 5000 | 0.0001 | - |
| 1.5298 | 5050 | 0.0001 | - |
| 1.5450 | 5100 | 0.0 | - |
| 1.5601 | 5150 | 0.0001 | - |
| 1.5753 | 5200 | 0.0 | - |
| 1.5904 | 5250 | 0.0 | - |
| 1.6056 | 5300 | 0.0001 | - |
| 1.6207 | 5350 | 0.0 | - |
| 1.6359 | 5400 | 0.0001 | - |
| 1.6510 | 5450 | 0.0 | - |
| 1.6662 | 5500 | 0.0001 | - |
| 1.6813 | 5550 | 0.0001 | - |
| 1.6965 | 5600 | 0.0 | - |
| 1.7116 | 5650 | 0.0 | - |
| 1.7267 | 5700 | 0.0 | - |
| 1.7419 | 5750 | 0.0001 | - |
| 1.7570 | 5800 | 0.0001 | - |
| 1.7722 | 5850 | 0.0 | - |
| 1.7873 | 5900 | 0.0 | - |
| 1.8025 | 5950 | 0.0001 | - |
| 1.8176 | 6000 | 0.0002 | - |
| 1.8328 | 6050 | 0.0 | - |
| 1.8479 | 6100 | 0.0001 | - |
| 1.8631 | 6150 | 0.0001 | - |
| 1.8782 | 6200 | 0.0001 | - |
| 1.8934 | 6250 | 0.0 | - |
| 1.9085 | 6300 | 0.0001 | - |
| 1.9237 | 6350 | 0.0 | - |
| 1.9388 | 6400 | 0.0001 | - |
| 1.9540 | 6450 | 0.0001 | - |
| 1.9691 | 6500 | 0.0 | - |
| 1.9842 | 6550 | 0.0 | - |
| 1.9994 | 6600 | 0.0 | - |
| 2.0 | 6602 | - | 0.0 |
| 2.0145 | 6650 | 0.0 | - |
| 2.0297 | 6700 | 0.0 | - |
| 2.0448 | 6750 | 0.0 | - |
| 2.0600 | 6800 | 0.0 | - |
| 2.0751 | 6850 | 0.0 | - |
| 2.0903 | 6900 | 0.0001 | - |
| 2.1054 | 6950 | 0.0 | - |
| 2.1206 | 7000 | 0.0 | - |
| 2.1357 | 7050 | 0.0 | - |
| 2.1509 | 7100 | 0.0001 | - |
| 2.1660 | 7150 | 0.0 | - |
| 2.1812 | 7200 | 0.0 | - |
| 2.1963 | 7250 | 0.0 | - |
| 2.2115 | 7300 | 0.0 | - |
| 2.2266 | 7350 | 0.0001 | - |
| 2.2417 | 7400 | 0.0 | - |
| 2.2569 | 7450 | 0.0 | - |
| 2.2720 | 7500 | 0.0001 | - |
| 2.2872 | 7550 | 0.0001 | - |
| 2.3023 | 7600 | 0.0 | - |
| 2.3175 | 7650 | 0.0 | - |
| 2.3326 | 7700 | 0.0 | - |
| 2.3478 | 7750 | 0.0 | - |
| 2.3629 | 7800 | 0.0 | - |
| 2.3781 | 7850 | 0.0 | - |
| 2.3932 | 7900 | 0.0 | - |
| 2.4084 | 7950 | 0.0 | - |
| 2.4235 | 8000 | 0.0 | - |
| 2.4387 | 8050 | 0.0 | - |
| 2.4538 | 8100 | 0.0001 | - |
| 2.4689 | 8150 | 0.0 | - |
| 2.4841 | 8200 | 0.0001 | - |
| 2.4992 | 8250 | 0.0 | - |
| 2.5144 | 8300 | 0.0 | - |
| 2.5295 | 8350 | 0.0001 | - |
| 2.5447 | 8400 | 0.0 | - |
| 2.5598 | 8450 | 0.0 | - |
| 2.5750 | 8500 | 0.0 | - |
| 2.5901 | 8550 | 0.0001 | - |
| 2.6053 | 8600 | 0.0001 | - |
| 2.6204 | 8650 | 0.0 | - |
| 2.6356 | 8700 | 0.0 | - |
| 2.6507 | 8750 | 0.0 | - |
| 2.6659 | 8800 | 0.0 | - |
| 2.6810 | 8850 | 0.0 | - |
| 2.6962 | 8900 | 0.0 | - |
| 2.7113 | 8950 | 0.0 | - |
| 2.7264 | 9000 | 0.0 | - |
| 2.7416 | 9050 | 0.0001 | - |
| 2.7567 | 9100 | 0.0001 | - |
| 2.7719 | 9150 | 0.0 | - |
| 2.7870 | 9200 | 0.0001 | - |
| 2.8022 | 9250 | 0.0 | - |
| 2.8173 | 9300 | 0.0 | - |
| 2.8325 | 9350 | 0.0 | - |
| 2.8476 | 9400 | 0.0 | - |
| 2.8628 | 9450 | 0.0 | - |
| 2.8779 | 9500 | 0.0 | - |
| 2.8931 | 9550 | 0.0 | - |
| 2.9082 | 9600 | 0.0 | - |
| 2.9234 | 9650 | 0.0 | - |
| 2.9385 | 9700 | 0.0 | - |
| 2.9537 | 9750 | 0.0 | - |
| 2.9688 | 9800 | 0.0 | - |
| 2.9839 | 9850 | 0.0 | - |
| 2.9991 | 9900 | 0.0 | - |
| 3.0 | 9903 | - | 0.0 |
| 3.0142 | 9950 | 0.0 | - |
| 3.0294 | 10000 | 0.0 | - |
| 3.0445 | 10050 | 0.0 | - |
| 3.0597 | 10100 | 0.0 | - |
| 3.0748 | 10150 | 0.0 | - |
| 3.0900 | 10200 | 0.0 | - |
| 3.1051 | 10250 | 0.0001 | - |
| 3.1203 | 10300 | 0.0001 | - |
| 3.1354 | 10350 | 0.0 | - |
| 3.1506 | 10400 | 0.0 | - |
| 3.1657 | 10450 | 0.0 | - |
| 3.1809 | 10500 | 0.0 | - |
| 3.1960 | 10550 | 0.0 | - |
| 3.2111 | 10600 | 0.0 | - |
| 3.2263 | 10650 | 0.0 | - |
| 3.2414 | 10700 | 0.0 | - |
| 3.2566 | 10750 | 0.0 | - |
| 3.2717 | 10800 | 0.0 | - |
| 3.2869 | 10850 | 0.0 | - |
| 3.3020 | 10900 | 0.0 | - |
| 3.3172 | 10950 | 0.0 | - |
| 3.3323 | 11000 | 0.0 | - |
| 3.3475 | 11050 | 0.0 | - |
| 3.3626 | 11100 | 0.0 | - |
| 3.3778 | 11150 | 0.0 | - |
| 3.3929 | 11200 | 0.0 | - |
| 3.4081 | 11250 | 0.0001 | - |
| 3.4232 | 11300 | 0.0 | - |
| 3.4384 | 11350 | 0.0 | - |
| 3.4535 | 11400 | 0.0 | - |
| 3.4686 | 11450 | 0.0 | - |
| 3.4838 | 11500 | 0.0 | - |
| 3.4989 | 11550 | 0.0 | - |
| 3.5141 | 11600 | 0.0 | - |
| 3.5292 | 11650 | 0.0 | - |
| 3.5444 | 11700 | 0.0 | - |
| 3.5595 | 11750 | 0.0 | - |
| 3.5747 | 11800 | 0.0 | - |
| 3.5898 | 11850 | 0.0 | - |
| 3.6050 | 11900 | 0.0 | - |
| 3.6201 | 11950 | 0.0 | - |
| 3.6353 | 12000 | 0.0 | - |
| 3.6504 | 12050 | 0.0 | - |
| 3.6656 | 12100 | 0.0001 | - |
| 3.6807 | 12150 | 0.0 | - |
| 3.6958 | 12200 | 0.0 | - |
| 3.7110 | 12250 | 0.0 | - |
| 3.7261 | 12300 | 0.0 | - |
| 3.7413 | 12350 | 0.0 | - |
| 3.7564 | 12400 | 0.0 | - |
| 3.7716 | 12450 | 0.0 | - |
| 3.7867 | 12500 | 0.0 | - |
| 3.8019 | 12550 | 0.0 | - |
| 3.8170 | 12600 | 0.0 | - |
| 3.8322 | 12650 | 0.0 | - |
| 3.8473 | 12700 | 0.0 | - |
| 3.8625 | 12750 | 0.0 | - |
| 3.8776 | 12800 | 0.0 | - |
| 3.8928 | 12850 | 0.0 | - |
| 3.9079 | 12900 | 0.0 | - |
| 3.9231 | 12950 | 0.0 | - |
| 3.9382 | 13000 | 0.0 | - |
| 3.9533 | 13050 | 0.0 | - |
| 3.9685 | 13100 | 0.0 | - |
| 3.9836 | 13150 | 0.0 | - |
| 3.9988 | 13200 | 0.0 | - |
| **4.0** | **13204** | **-** | **0.0** |
* 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}
}
```