SetFit with sentence-transformers/all-MiniLM-L12-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-MiniLM-L12-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
Model Sources
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:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
model = SetFitModel.from_pretrained("egis-group/router_mini_lm_l12")
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
@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}
}