SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-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 Type: SetFit
- Sentence Transformer body: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 128 tokens
- Number of Classes: 2 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
Label | Examples |
---|---|
supportive |
|
opposed |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.9851 |
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
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("cbpuschmann/MiniLM-klimacoder_v0.2")
# Run inference
preds = model("\"Ein Schritt in die richtige Richtung: Warum ein nationales Tempolimit sinnvoll ist\"
Die Diskussion um ein nationales Tempolimit auf deutschen Autobahnen ist wieder in aller Munde. Während einige das Thema als überflüssige Einschränkung der Freiheit ablehnen, sehe ich darin eine notwendige Maßnahme, um unsere Straßen sicherer und umweltfreundlicher zu machen.
Ein Tempolimit von 130 km/h auf Autobahnen würde nicht nur die Zahl der Unfälle und Todesopfer reduzieren, sondern auch den Kraftstoffverbrauch und damit die Treibhausgasemissionen senken. Laut einer Studie der Universität Kassel könnte ein Tempolimit bis zu 2,5 Millionen Tonnen CO2 pro Jahr einsparen – ein wichtiger Beitrag zum Klimaschutz.
Darüber hinaus würde ein Tempolimit die Verkehrssicherheit erhöhen. Denn je höher die Geschwindigkeit, desto länger sind die Bremswege und desto höher ist die Unfallgefahr. Ein Tempolimit würde auch die Lärmbelästigung entlang der Autobahnen reduzieren und die Lebensqualität der Anwohner verbessern.
Auch die Argumente der Tempolimit-Gegner halten einer näheren Betrachtung nicht stand. Die Befürchtung, dass ein Tempolimit zu einer Verkehrszunahme auf Landstraßen führen würde, ist nicht belegt. Im Gegenteil: In Ländern mit Tempolimit, wie beispielsweise in Österreich oder der Schweiz, ist die Verkehrssicherheit höher und die Zahl der Unfälle niedriger als in Deutschland.
Es ist Zeit, dass Deutschland endlich ein nationales Tempolimit einführt. Es wäre ein wichtiger Schritt in die richtige Richtung, um unsere Straßen sicherer, umweltfreundlicher und lebenswerter zu machen.")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 40 | 304.6601 | 551 |
Label | Training Sample Count |
---|---|
opposed | 550 |
supportive | 524 |
Training Hyperparameters
- batch_size: (64, 64)
- num_epochs: (10, 10)
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0001 | 1 | 0.2717 | - |
0.0055 | 50 | 0.3008 | - |
0.0111 | 100 | 0.2847 | - |
0.0166 | 150 | 0.2883 | - |
0.0221 | 200 | 0.2806 | - |
0.0277 | 250 | 0.266 | - |
0.0332 | 300 | 0.2439 | - |
0.0387 | 350 | 0.2332 | - |
0.0443 | 400 | 0.2164 | - |
0.0498 | 450 | 0.1868 | - |
0.0553 | 500 | 0.1462 | - |
0.0609 | 550 | 0.0979 | - |
0.0664 | 600 | 0.0659 | - |
0.0720 | 650 | 0.0452 | - |
0.0775 | 700 | 0.0364 | - |
0.0830 | 750 | 0.025 | - |
0.0886 | 800 | 0.018 | - |
0.0941 | 850 | 0.0144 | - |
0.0996 | 900 | 0.0105 | - |
0.1052 | 950 | 0.0087 | - |
0.1107 | 1000 | 0.0062 | - |
0.1162 | 1050 | 0.0043 | - |
0.1218 | 1100 | 0.0035 | - |
0.1273 | 1150 | 0.0031 | - |
0.1328 | 1200 | 0.0023 | - |
0.1384 | 1250 | 0.0018 | - |
0.1439 | 1300 | 0.0015 | - |
0.1494 | 1350 | 0.0012 | - |
0.1550 | 1400 | 0.0009 | - |
0.1605 | 1450 | 0.0009 | - |
0.1660 | 1500 | 0.0008 | - |
0.1716 | 1550 | 0.0007 | - |
0.1771 | 1600 | 0.0006 | - |
0.1826 | 1650 | 0.0005 | - |
0.1882 | 1700 | 0.0004 | - |
0.1937 | 1750 | 0.0004 | - |
0.1992 | 1800 | 0.0003 | - |
0.2048 | 1850 | 0.0003 | - |
0.2103 | 1900 | 0.0003 | - |
0.2159 | 1950 | 0.0003 | - |
0.2214 | 2000 | 0.0003 | - |
0.2269 | 2050 | 0.0005 | - |
0.2325 | 2100 | 0.0002 | - |
0.2380 | 2150 | 0.0002 | - |
0.2435 | 2200 | 0.0005 | - |
0.2491 | 2250 | 0.0002 | - |
0.2546 | 2300 | 0.0002 | - |
0.2601 | 2350 | 0.0001 | - |
0.2657 | 2400 | 0.0001 | - |
0.2712 | 2450 | 0.0001 | - |
0.2767 | 2500 | 0.0001 | - |
0.2823 | 2550 | 0.0001 | - |
0.2878 | 2600 | 0.0001 | - |
0.2933 | 2650 | 0.0001 | - |
0.2989 | 2700 | 0.0001 | - |
0.3044 | 2750 | 0.0001 | - |
0.3099 | 2800 | 0.0001 | - |
0.3155 | 2850 | 0.0001 | - |
0.3210 | 2900 | 0.0001 | - |
0.3265 | 2950 | 0.0001 | - |
0.3321 | 3000 | 0.0001 | - |
0.3376 | 3050 | 0.0001 | - |
0.3431 | 3100 | 0.0001 | - |
0.3487 | 3150 | 0.0 | - |
0.3542 | 3200 | 0.0 | - |
0.3598 | 3250 | 0.0001 | - |
0.3653 | 3300 | 0.0 | - |
0.3708 | 3350 | 0.0002 | - |
0.3764 | 3400 | 0.0001 | - |
0.3819 | 3450 | 0.0 | - |
0.3874 | 3500 | 0.0 | - |
0.3930 | 3550 | 0.0 | - |
0.3985 | 3600 | 0.0 | - |
0.4040 | 3650 | 0.0 | - |
0.4096 | 3700 | 0.0 | - |
0.4151 | 3750 | 0.0 | - |
0.4206 | 3800 | 0.0001 | - |
0.4262 | 3850 | 0.0001 | - |
0.4317 | 3900 | 0.0 | - |
0.4372 | 3950 | 0.0 | - |
0.4428 | 4000 | 0.0 | - |
0.4483 | 4050 | 0.0 | - |
0.4538 | 4100 | 0.0 | - |
0.4594 | 4150 | 0.0 | - |
0.4649 | 4200 | 0.0 | - |
0.4704 | 4250 | 0.0 | - |
0.4760 | 4300 | 0.0 | - |
0.4815 | 4350 | 0.0 | - |
0.4870 | 4400 | 0.0 | - |
0.4926 | 4450 | 0.0 | - |
0.4981 | 4500 | 0.0 | - |
0.5037 | 4550 | 0.0 | - |
0.5092 | 4600 | 0.0 | - |
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0.5369 | 4850 | 0.0 | - |
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0.5535 | 5000 | 0.0 | - |
0.5590 | 5050 | 0.0 | - |
0.5645 | 5100 | 0.0 | - |
0.5701 | 5150 | 0.0 | - |
0.5756 | 5200 | 0.0 | - |
0.5811 | 5250 | 0.0 | - |
0.5867 | 5300 | 0.0 | - |
0.5922 | 5350 | 0.0 | - |
0.5977 | 5400 | 0.0 | - |
0.6033 | 5450 | 0.0 | - |
0.6088 | 5500 | 0.0 | - |
0.6143 | 5550 | 0.0 | - |
0.6199 | 5600 | 0.0 | - |
0.6254 | 5650 | 0.0 | - |
0.6309 | 5700 | 0.0 | - |
0.6365 | 5750 | 0.0 | - |
0.6420 | 5800 | 0.0 | - |
0.6476 | 5850 | 0.0 | - |
0.6531 | 5900 | 0.0 | - |
0.6586 | 5950 | 0.0 | - |
0.6642 | 6000 | 0.0 | - |
0.6697 | 6050 | 0.0 | - |
0.6752 | 6100 | 0.0 | - |
0.6808 | 6150 | 0.0 | - |
0.6863 | 6200 | 0.0 | - |
0.6918 | 6250 | 0.0 | - |
0.6974 | 6300 | 0.0 | - |
0.7029 | 6350 | 0.0 | - |
0.7084 | 6400 | 0.0 | - |
0.7140 | 6450 | 0.0 | - |
0.7195 | 6500 | 0.0 | - |
0.7250 | 6550 | 0.0 | - |
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0.7416 | 6700 | 0.0 | - |
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0.7527 | 6800 | 0.0 | - |
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0.7638 | 6900 | 0.0 | - |
0.7693 | 6950 | 0.0 | - |
0.7749 | 7000 | 0.0 | - |
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0.7859 | 7100 | 0.0 | - |
0.7915 | 7150 | 0.0 | - |
0.7970 | 7200 | 0.0 | - |
0.8025 | 7250 | 0.0 | - |
0.8081 | 7300 | 0.0 | - |
0.8136 | 7350 | 0.0 | - |
0.8191 | 7400 | 0.0 | - |
0.8247 | 7450 | 0.0 | - |
0.8302 | 7500 | 0.0 | - |
0.8357 | 7550 | 0.0 | - |
0.8413 | 7600 | 0.0 | - |
0.8468 | 7650 | 0.0 | - |
0.8523 | 7700 | 0.0 | - |
0.8579 | 7750 | 0.0 | - |
0.8634 | 7800 | 0.0 | - |
0.8689 | 7850 | 0.0 | - |
0.8745 | 7900 | 0.0 | - |
0.8800 | 7950 | 0.0 | - |
0.8855 | 8000 | 0.0 | - |
0.8911 | 8050 | 0.0 | - |
0.8966 | 8100 | 0.0 | - |
0.9021 | 8150 | 0.0 | - |
0.9077 | 8200 | 0.0 | - |
0.9132 | 8250 | 0.0 | - |
0.9188 | 8300 | 0.0 | - |
0.9243 | 8350 | 0.0 | - |
0.9298 | 8400 | 0.0 | - |
0.9354 | 8450 | 0.0 | - |
0.9409 | 8500 | 0.0 | - |
0.9464 | 8550 | 0.0 | - |
0.9520 | 8600 | 0.0 | - |
0.9575 | 8650 | 0.0 | - |
0.9630 | 8700 | 0.0 | - |
0.9686 | 8750 | 0.0 | - |
0.9741 | 8800 | 0.0 | - |
0.9796 | 8850 | 0.0 | - |
0.9852 | 8900 | 0.0 | - |
0.9907 | 8950 | 0.0 | - |
0.9962 | 9000 | 0.0076 | - |
1.0018 | 9050 | 0.0041 | - |
1.0073 | 9100 | 0.0018 | - |
1.0128 | 9150 | 0.0009 | - |
1.0184 | 9200 | 0.0002 | - |
1.0239 | 9250 | 0.0 | - |
1.0294 | 9300 | 0.0 | - |
1.0350 | 9350 | 0.0 | - |
1.0405 | 9400 | 0.0 | - |
1.0460 | 9450 | 0.0 | - |
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1.0627 | 9600 | 0.0 | - |
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1.0793 | 9750 | 0.0 | - |
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1.0903 | 9850 | 0.0 | - |
1.0959 | 9900 | 0.0 | - |
1.1014 | 9950 | 0.0 | - |
1.1069 | 10000 | 0.0 | - |
1.1125 | 10050 | 0.0 | - |
1.1180 | 10100 | 0.0 | - |
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1.1291 | 10200 | 0.0 | - |
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1.1955 | 10800 | 0.0 | - |
1.2010 | 10850 | 0.0 | - |
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1.2176 | 11000 | 0.0 | - |
1.2232 | 11050 | 0.0 | - |
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1.3117 | 11850 | 0.0 | - |
1.3172 | 11900 | 0.0 | - |
1.3228 | 11950 | 0.0 | - |
1.3283 | 12000 | 0.0 | - |
1.3338 | 12050 | 0.0 | - |
1.3394 | 12100 | 0.0 | - |
1.3449 | 12150 | 0.0 | - |
1.3505 | 12200 | 0.0 | - |
1.3560 | 12250 | 0.0 | - |
1.3615 | 12300 | 0.0 | - |
1.3671 | 12350 | 0.0 | - |
1.3726 | 12400 | 0.0 | - |
1.3781 | 12450 | 0.0 | - |
1.3837 | 12500 | 0.0 | - |
1.3892 | 12550 | 0.0 | - |
1.3947 | 12600 | 0.0 | - |
1.4003 | 12650 | 0.0 | - |
1.4058 | 12700 | 0.0 | - |
1.4113 | 12750 | 0.0 | - |
1.4169 | 12800 | 0.0 | - |
1.4224 | 12850 | 0.0 | - |
1.4279 | 12900 | 0.0 | - |
1.4335 | 12950 | 0.0 | - |
1.4390 | 13000 | 0.0 | - |
1.4445 | 13050 | 0.0 | - |
1.4501 | 13100 | 0.0 | - |
1.4556 | 13150 | 0.0 | - |
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1.4667 | 13250 | 0.0 | - |
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1.8043 | 16300 | 0.0 | - |
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2.0035 | 18100 | 0.0 | - |
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Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.2.1
- Transformers: 4.42.2
- PyTorch: 2.5.1+cu121
- Datasets: 3.1.0
- Tokenizers: 0.19.1
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}
}
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