---
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: au revoir
- text: quand auront lieu les matchs de Aston Villa
- text: any upcoming fixtures for Juventus
- text: qui êtes-vous
- text: what is the score of Brentford match
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 1.0
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-multilingual-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/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co./sentence-transformers/paraphrase-multilingual-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/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co./sentence-transformers/paraphrase-multilingual-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:** 6 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 |
|:------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| matches-match_time | - 'Norwich City vs Newcastle United'
- 'will Manchester United play with chelsea'
- 'est-ce que Manchester United jouera avec chelsea'
|
| matches-match_result | - 'Liverpool and West Ham result'
- 'what is the score of Wolverhampton match'
- 'who won in Liverpool vs Newcastle United match'
|
| greet-who_are_you | - 'how can you help me'
- "pourquoi j'ai besoin de toi"
- 'je ne te comprends pas'
|
| matches-team_next_match | - 'Real Madrid fixtures'
- 'quels sont les prochains matchs de Borussia Dortmund'
- 'próximos partidos de Atletico Madrid'
|
| greet-good_bye | - 'See you later'
- 'A plus tard'
- 'stop'
|
| greet-hi | - 'Hello buddy'
- 'Salut'
- 'Hey'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 1.0 |
## 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("fadyabdo/botpress_football_sft_model")
# Run inference
preds = model("au revoir")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 1 | 5.2 | 10 |
| Label | Training Sample Count |
|:------------------------|:----------------------|
| greet-hi | 5 |
| greet-who_are_you | 7 |
| greet-good_bye | 5 |
| matches-team_next_match | 21 |
| matches-match_time | 12 |
| matches-match_result | 15 |
### Training Hyperparameters
- batch_size: (4, 4)
- 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 |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0012 | 1 | 0.1544 | - |
| 0.0121 | 10 | 0.0658 | - |
| 0.0241 | 20 | 0.1235 | - |
| 0.0362 | 30 | 0.2422 | - |
| 0.0483 | 40 | 0.2876 | - |
| 0.0603 | 50 | 0.1208 | - |
| 0.0724 | 60 | 0.1358 | - |
| 0.0844 | 70 | 0.1494 | - |
| 0.0965 | 80 | 0.1284 | - |
| 0.1086 | 90 | 0.1107 | - |
| 0.1206 | 100 | 0.2395 | - |
| 0.1327 | 110 | 0.0661 | - |
| 0.1448 | 120 | 0.1554 | - |
| 0.1568 | 130 | 0.0258 | - |
| 0.1689 | 140 | 0.0279 | - |
| 0.1809 | 150 | 0.1162 | - |
| 0.1930 | 160 | 0.0244 | - |
| 0.2051 | 170 | 0.0221 | - |
| 0.2171 | 180 | 0.0813 | - |
| 0.2292 | 190 | 0.0188 | - |
| 0.2413 | 200 | 0.03 | - |
| 0.2533 | 210 | 0.0019 | - |
| 0.2654 | 220 | 0.0076 | - |
| 0.2774 | 230 | 0.01 | - |
| 0.2895 | 240 | 0.0025 | - |
| 0.3016 | 250 | 0.0705 | - |
| 0.3136 | 260 | 0.0044 | - |
| 0.3257 | 270 | 0.0038 | - |
| 0.3378 | 280 | 0.006 | - |
| 0.3498 | 290 | 0.0018 | - |
| 0.3619 | 300 | 0.0003 | - |
| 0.3739 | 310 | 0.0007 | - |
| 0.3860 | 320 | 0.0128 | - |
| 0.3981 | 330 | 0.0022 | - |
| 0.4101 | 340 | 0.0008 | - |
| 0.4222 | 350 | 0.004 | - |
| 0.4343 | 360 | 0.0006 | - |
| 0.4463 | 370 | 0.0007 | - |
| 0.4584 | 380 | 0.0005 | - |
| 0.4704 | 390 | 0.0057 | - |
| 0.4825 | 400 | 0.0007 | - |
| 0.4946 | 410 | 0.0022 | - |
| 0.5066 | 420 | 0.0012 | - |
| 0.5187 | 430 | 0.0009 | - |
| 0.5308 | 440 | 0.0004 | - |
| 0.5428 | 450 | 0.0032 | - |
| 0.5549 | 460 | 0.0007 | - |
| 0.5669 | 470 | 0.0008 | - |
| 0.5790 | 480 | 0.0005 | - |
| 0.5911 | 490 | 0.0005 | - |
| 0.6031 | 500 | 0.0008 | - |
| 0.6152 | 510 | 0.0008 | - |
| 0.6273 | 520 | 0.0004 | - |
| 0.6393 | 530 | 0.0015 | - |
| 0.6514 | 540 | 0.0002 | - |
| 0.6634 | 550 | 0.0006 | - |
| 0.6755 | 560 | 0.0015 | - |
| 0.6876 | 570 | 0.0024 | - |
| 0.6996 | 580 | 0.0004 | - |
| 0.7117 | 590 | 0.0005 | - |
| 0.7238 | 600 | 0.0011 | - |
| 0.7358 | 610 | 0.0008 | - |
| 0.7479 | 620 | 0.0002 | - |
| 0.7600 | 630 | 0.0006 | - |
| 0.7720 | 640 | 0.0003 | - |
| 0.7841 | 650 | 0.0002 | - |
| 0.7961 | 660 | 0.0007 | - |
| 0.8082 | 670 | 0.0009 | - |
| 0.8203 | 680 | 0.0002 | - |
| 0.8323 | 690 | 0.0006 | - |
| 0.8444 | 700 | 0.0015 | - |
| 0.8565 | 710 | 0.0003 | - |
| 0.8685 | 720 | 0.0003 | - |
| 0.8806 | 730 | 0.0003 | - |
| 0.8926 | 740 | 0.0015 | - |
| 0.9047 | 750 | 0.0003 | - |
| 0.9168 | 760 | 0.0005 | - |
| 0.9288 | 770 | 0.0002 | - |
| 0.9409 | 780 | 0.0003 | - |
| 0.9530 | 790 | 0.0002 | - |
| 0.9650 | 800 | 0.0004 | - |
| 0.9771 | 810 | 0.0003 | - |
| 0.9891 | 820 | 0.001 | - |
| 1.0 | 829 | - | 0.0216 |
| 1.0012 | 830 | 0.0003 | - |
| 1.0133 | 840 | 0.0007 | - |
| 1.0253 | 850 | 0.0004 | - |
| 1.0374 | 860 | 0.0001 | - |
| 1.0495 | 870 | 0.0008 | - |
| 1.0615 | 880 | 0.0003 | - |
| 1.0736 | 890 | 0.0006 | - |
| 1.0856 | 900 | 0.0001 | - |
| 1.0977 | 910 | 0.0018 | - |
| 1.1098 | 920 | 0.0 | - |
| 1.1218 | 930 | 0.0001 | - |
| 1.1339 | 940 | 0.0007 | - |
| 1.1460 | 950 | 0.0009 | - |
| 1.1580 | 960 | 0.0004 | - |
| 1.1701 | 970 | 0.0003 | - |
| 1.1821 | 980 | 0.0015 | - |
| 1.1942 | 990 | 0.0002 | - |
| 1.2063 | 1000 | 0.0005 | - |
| 1.2183 | 1010 | 0.0002 | - |
| 1.2304 | 1020 | 0.0003 | - |
| 1.2425 | 1030 | 0.0001 | - |
| 1.2545 | 1040 | 0.0002 | - |
| 1.2666 | 1050 | 0.0004 | - |
| 1.2786 | 1060 | 0.0001 | - |
| 1.2907 | 1070 | 0.0002 | - |
| 1.3028 | 1080 | 0.0001 | - |
| 1.3148 | 1090 | 0.0002 | - |
| 1.3269 | 1100 | 0.0001 | - |
| 1.3390 | 1110 | 0.0002 | - |
| 1.3510 | 1120 | 0.0003 | - |
| 1.3631 | 1130 | 0.0001 | - |
| 1.3752 | 1140 | 0.0001 | - |
| 1.3872 | 1150 | 0.0001 | - |
| 1.3993 | 1160 | 0.0002 | - |
| 1.4113 | 1170 | 0.0001 | - |
| 1.4234 | 1180 | 0.0005 | - |
| 1.4355 | 1190 | 0.0002 | - |
| 1.4475 | 1200 | 0.0002 | - |
| 1.4596 | 1210 | 0.0002 | - |
| 1.4717 | 1220 | 0.0001 | - |
| 1.4837 | 1230 | 0.0001 | - |
| 1.4958 | 1240 | 0.0001 | - |
| 1.5078 | 1250 | 0.0001 | - |
| 1.5199 | 1260 | 0.001 | - |
| 1.5320 | 1270 | 0.0001 | - |
| 1.5440 | 1280 | 0.0003 | - |
| 1.5561 | 1290 | 0.0001 | - |
| 1.5682 | 1300 | 0.0002 | - |
| 1.5802 | 1310 | 0.0005 | - |
| 1.5923 | 1320 | 0.0002 | - |
| 1.6043 | 1330 | 0.0001 | - |
| 1.6164 | 1340 | 0.0004 | - |
| 1.6285 | 1350 | 0.0002 | - |
| 1.6405 | 1360 | 0.0001 | - |
| 1.6526 | 1370 | 0.0004 | - |
| 1.6647 | 1380 | 0.0003 | - |
| 1.6767 | 1390 | 0.0002 | - |
| 1.6888 | 1400 | 0.0001 | - |
| 1.7008 | 1410 | 0.0008 | - |
| 1.7129 | 1420 | 0.0003 | - |
| 1.7250 | 1430 | 0.0005 | - |
| 1.7370 | 1440 | 0.0001 | - |
| 1.7491 | 1450 | 0.0001 | - |
| 1.7612 | 1460 | 0.0001 | - |
| 1.7732 | 1470 | 0.0007 | - |
| 1.7853 | 1480 | 0.0001 | - |
| 1.7973 | 1490 | 0.0002 | - |
| 1.8094 | 1500 | 0.0001 | - |
| 1.8215 | 1510 | 0.001 | - |
| 1.8335 | 1520 | 0.0002 | - |
| 1.8456 | 1530 | 0.0003 | - |
| 1.8577 | 1540 | 0.0004 | - |
| 1.8697 | 1550 | 0.0005 | - |
| 1.8818 | 1560 | 0.0001 | - |
| 1.8938 | 1570 | 0.0006 | - |
| 1.9059 | 1580 | 0.0005 | - |
| 1.9180 | 1590 | 0.0002 | - |
| 1.9300 | 1600 | 0.0002 | - |
| 1.9421 | 1610 | 0.0001 | - |
| 1.9542 | 1620 | 0.0003 | - |
| 1.9662 | 1630 | 0.0005 | - |
| 1.9783 | 1640 | 0.0007 | - |
| 1.9903 | 1650 | 0.0001 | - |
| 2.0 | 1658 | - | 0.0186 |
| 2.0024 | 1660 | 0.0 | - |
| 2.0145 | 1670 | 0.0001 | - |
| 2.0265 | 1680 | 0.0002 | - |
| 2.0386 | 1690 | 0.0001 | - |
| 2.0507 | 1700 | 0.0002 | - |
| 2.0627 | 1710 | 0.0001 | - |
| 2.0748 | 1720 | 0.0001 | - |
| 2.0869 | 1730 | 0.0002 | - |
| 2.0989 | 1740 | 0.0001 | - |
| 2.1110 | 1750 | 0.0002 | - |
| 2.1230 | 1760 | 0.0001 | - |
| 2.1351 | 1770 | 0.0003 | - |
| 2.1472 | 1780 | 0.0006 | - |
| 2.1592 | 1790 | 0.0001 | - |
| 2.1713 | 1800 | 0.0002 | - |
| 2.1834 | 1810 | 0.0002 | - |
| 2.1954 | 1820 | 0.0001 | - |
| 2.2075 | 1830 | 0.0 | - |
| 2.2195 | 1840 | 0.0001 | - |
| 2.2316 | 1850 | 0.0002 | - |
| 2.2437 | 1860 | 0.0004 | - |
| 2.2557 | 1870 | 0.0003 | - |
| 2.2678 | 1880 | 0.0002 | - |
| 2.2799 | 1890 | 0.0002 | - |
| 2.2919 | 1900 | 0.0004 | - |
| 2.3040 | 1910 | 0.0002 | - |
| 2.3160 | 1920 | 0.0001 | - |
| 2.3281 | 1930 | 0.0 | - |
| 2.3402 | 1940 | 0.0002 | - |
| 2.3522 | 1950 | 0.0001 | - |
| 2.3643 | 1960 | 0.0 | - |
| 2.3764 | 1970 | 0.0003 | - |
| 2.3884 | 1980 | 0.0002 | - |
| 2.4005 | 1990 | 0.0001 | - |
| 2.4125 | 2000 | 0.0003 | - |
| 2.4246 | 2010 | 0.0003 | - |
| 2.4367 | 2020 | 0.0002 | - |
| 2.4487 | 2030 | 0.0002 | - |
| 2.4608 | 2040 | 0.0002 | - |
| 2.4729 | 2050 | 0.0001 | - |
| 2.4849 | 2060 | 0.0001 | - |
| 2.4970 | 2070 | 0.0002 | - |
| 2.5090 | 2080 | 0.0 | - |
| 2.5211 | 2090 | 0.0002 | - |
| 2.5332 | 2100 | 0.0004 | - |
| 2.5452 | 2110 | 0.0005 | - |
| 2.5573 | 2120 | 0.0003 | - |
| 2.5694 | 2130 | 0.0001 | - |
| 2.5814 | 2140 | 0.0002 | - |
| 2.5935 | 2150 | 0.0008 | - |
| 2.6055 | 2160 | 0.0002 | - |
| 2.6176 | 2170 | 0.0003 | - |
| 2.6297 | 2180 | 0.0001 | - |
| 2.6417 | 2190 | 0.0002 | - |
| 2.6538 | 2200 | 0.0001 | - |
| 2.6659 | 2210 | 0.0001 | - |
| 2.6779 | 2220 | 0.0 | - |
| 2.6900 | 2230 | 0.0002 | - |
| 2.7021 | 2240 | 0.0 | - |
| 2.7141 | 2250 | 0.0001 | - |
| 2.7262 | 2260 | 0.0001 | - |
| 2.7382 | 2270 | 0.0003 | - |
| 2.7503 | 2280 | 0.0001 | - |
| 2.7624 | 2290 | 0.0003 | - |
| 2.7744 | 2300 | 0.0001 | - |
| 2.7865 | 2310 | 0.0002 | - |
| 2.7986 | 2320 | 0.0001 | - |
| 2.8106 | 2330 | 0.0001 | - |
| 2.8227 | 2340 | 0.0001 | - |
| 2.8347 | 2350 | 0.0001 | - |
| 2.8468 | 2360 | 0.0002 | - |
| 2.8589 | 2370 | 0.0001 | - |
| 2.8709 | 2380 | 0.0001 | - |
| 2.8830 | 2390 | 0.0 | - |
| 2.8951 | 2400 | 0.0 | - |
| 2.9071 | 2410 | 0.0 | - |
| 2.9192 | 2420 | 0.0001 | - |
| 2.9312 | 2430 | 0.0002 | - |
| 2.9433 | 2440 | 0.0001 | - |
| 2.9554 | 2450 | 0.0001 | - |
| 2.9674 | 2460 | 0.0001 | - |
| 2.9795 | 2470 | 0.0003 | - |
| 2.9916 | 2480 | 0.0001 | - |
| **3.0** | **2487** | **-** | **0.0176** |
| 3.0036 | 2490 | 0.0001 | - |
| 3.0157 | 2500 | 0.0 | - |
| 3.0277 | 2510 | 0.0002 | - |
| 3.0398 | 2520 | 0.0 | - |
| 3.0519 | 2530 | 0.0002 | - |
| 3.0639 | 2540 | 0.0002 | - |
| 3.0760 | 2550 | 0.0 | - |
| 3.0881 | 2560 | 0.0001 | - |
| 3.1001 | 2570 | 0.0001 | - |
| 3.1122 | 2580 | 0.0003 | - |
| 3.1242 | 2590 | 0.0003 | - |
| 3.1363 | 2600 | 0.0001 | - |
| 3.1484 | 2610 | 0.0 | - |
| 3.1604 | 2620 | 0.0002 | - |
| 3.1725 | 2630 | 0.0001 | - |
| 3.1846 | 2640 | 0.0001 | - |
| 3.1966 | 2650 | 0.0001 | - |
| 3.2087 | 2660 | 0.0003 | - |
| 3.2207 | 2670 | 0.0001 | - |
| 3.2328 | 2680 | 0.0001 | - |
| 3.2449 | 2690 | 0.0001 | - |
| 3.2569 | 2700 | 0.0001 | - |
| 3.2690 | 2710 | 0.0002 | - |
| 3.2811 | 2720 | 0.0001 | - |
| 3.2931 | 2730 | 0.0005 | - |
| 3.3052 | 2740 | 0.0 | - |
| 3.3172 | 2750 | 0.0001 | - |
| 3.3293 | 2760 | 0.0002 | - |
| 3.3414 | 2770 | 0.0003 | - |
| 3.3534 | 2780 | 0.0001 | - |
| 3.3655 | 2790 | 0.0001 | - |
| 3.3776 | 2800 | 0.0001 | - |
| 3.3896 | 2810 | 0.0004 | - |
| 3.4017 | 2820 | 0.0001 | - |
| 3.4138 | 2830 | 0.0002 | - |
| 3.4258 | 2840 | 0.0001 | - |
| 3.4379 | 2850 | 0.0003 | - |
| 3.4499 | 2860 | 0.0001 | - |
| 3.4620 | 2870 | 0.0002 | - |
| 3.4741 | 2880 | 0.0001 | - |
| 3.4861 | 2890 | 0.0003 | - |
| 3.4982 | 2900 | 0.0003 | - |
| 3.5103 | 2910 | 0.0001 | - |
| 3.5223 | 2920 | 0.0 | - |
| 3.5344 | 2930 | 0.0 | - |
| 3.5464 | 2940 | 0.0001 | - |
| 3.5585 | 2950 | 0.0002 | - |
| 3.5706 | 2960 | 0.0002 | - |
| 3.5826 | 2970 | 0.0001 | - |
| 3.5947 | 2980 | 0.0 | - |
| 3.6068 | 2990 | 0.0001 | - |
| 3.6188 | 3000 | 0.0003 | - |
| 3.6309 | 3010 | 0.0001 | - |
| 3.6429 | 3020 | 0.0 | - |
| 3.6550 | 3030 | 0.0002 | - |
| 3.6671 | 3040 | 0.0003 | - |
| 3.6791 | 3050 | 0.0005 | - |
| 3.6912 | 3060 | 0.0001 | - |
| 3.7033 | 3070 | 0.0 | - |
| 3.7153 | 3080 | 0.0001 | - |
| 3.7274 | 3090 | 0.0002 | - |
| 3.7394 | 3100 | 0.0001 | - |
| 3.7515 | 3110 | 0.0001 | - |
| 3.7636 | 3120 | 0.0002 | - |
| 3.7756 | 3130 | 0.0001 | - |
| 3.7877 | 3140 | 0.0 | - |
| 3.7998 | 3150 | 0.0001 | - |
| 3.8118 | 3160 | 0.0001 | - |
| 3.8239 | 3170 | 0.0001 | - |
| 3.8359 | 3180 | 0.0001 | - |
| 3.8480 | 3190 | 0.0005 | - |
| 3.8601 | 3200 | 0.0 | - |
| 3.8721 | 3210 | 0.0001 | - |
| 3.8842 | 3220 | 0.0001 | - |
| 3.8963 | 3230 | 0.0001 | - |
| 3.9083 | 3240 | 0.0001 | - |
| 3.9204 | 3250 | 0.0001 | - |
| 3.9324 | 3260 | 0.0 | - |
| 3.9445 | 3270 | 0.0001 | - |
| 3.9566 | 3280 | 0.0001 | - |
| 3.9686 | 3290 | 0.0002 | - |
| 3.9807 | 3300 | 0.0002 | - |
| 3.9928 | 3310 | 0.0001 | - |
| 4.0 | 3316 | - | 0.0187 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.37.0
- PyTorch: 2.3.0+cu121
- Datasets: 2.20.0
- 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}
}
```