metadata
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
"Während die Welt arbeitet, um Lösungen für die tatsächlichen Probleme zu
finden, verschwenden junge Aktivisten ihre Zeit mit Straßenblockaden und
Schütteln von Plakaten. Ihre Rhetorik ist einstudiert, ihre
Wirklichkeitserfahrung jedoch begrenzt."
- text: >-
Die jüngsten Gesetzesinitiativen zur flächendeckenden Einführung von
Wärmepumpen markieren einen bedeutenden Schritt in Richtung einer
nachhaltigeren Energiezukunft und könnten langfristig zur Reduzierung von
CO2-Emissionen im Gebäudesektor beitragen. Durch die Förderung dieser
umweltfreundlichen Technologie wird nicht nur der Klimaschutz gestärkt,
sondern auch die Abhängigkeit von fossilen Brennstoffen verringert.
- text: >-
Die Debatte über die Einführung eines nationalen Tempolimits auf deutschen
Autobahnen bleibt weiterhin ein umstrittenes Thema in der Politik.
Befürworter argumentieren mit positiven Auswirkungen auf die
Verkehrssicherheit und den Umweltschutz, während Gegner auf individuelle
Freiheitsrechte und wirtschaftliche Faktoren verweisen. Der Ausgang der
Gesetzesinitiativen ist bisher ungewiss.
- text: >-
Chaos auf den Straßen und genervte Pendler: Die Klima-Aktivisten von
Fridays for Future und der Letzten Generation sorgen erneut für Unmut in
der Bevölkerung. Während sie für ihre Sache kämpfen, wächst der Frust über
ihre umstrittenen Methoden.
- text: >-
Inmitten wachsender Besorgnis über den Klimawandel setzen
Klima-Aktivismus-Gruppen wie Fridays for Future und die Letzte Generation
mit ihren Aktionen ein starkes Zeichen für die Dringlichkeit des
Umweltschutzes. Ihre Entschlossenheit, auf die Notwendigkeit rascher
politischer und gesellschaftlicher Veränderungen hinzuweisen, findet bei
vielen Menschen Anklang und regt zum Nachdenken an.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: nomic-ai/modernbert-embed-base
model-index:
- name: SetFit with nomic-ai/modernbert-embed-base
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.9771428571428571
name: Accuracy
SetFit with nomic-ai/modernbert-embed-base
This is a SetFit model that can be used for Text Classification. This SetFit model uses nomic-ai/modernbert-embed-base 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: nomic-ai/modernbert-embed-base
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 8192 tokens
- Number of Classes: 3 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 |
---|---|
opposed |
|
neutral |
|
supportive |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.9771 |
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/klimacoder_modernbert_v0.1")
# Run inference
preds = model("Chaos auf den Straßen und genervte Pendler: Die Klima-Aktivisten von Fridays for Future und der Letzten Generation sorgen erneut für Unmut in der Bevölkerung. Während sie für ihre Sache kämpfen, wächst der Frust über ihre umstrittenen Methoden.")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 24 | 44.1632 | 73 |
Label | Training Sample Count |
---|---|
neutral | 503 |
opposed | 536 |
supportive | 536 |
Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- 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 |
---|---|---|---|
0.0000 | 1 | 0.343 | - |
0.0010 | 50 | 0.3042 | - |
0.0019 | 100 | 0.2881 | - |
0.0029 | 150 | 0.2698 | - |
0.0039 | 200 | 0.2463 | - |
0.0048 | 250 | 0.2377 | - |
0.0058 | 300 | 0.2319 | - |
0.0068 | 350 | 0.2074 | - |
0.0077 | 400 | 0.1729 | - |
0.0087 | 450 | 0.1458 | - |
0.0097 | 500 | 0.1004 | - |
0.0106 | 550 | 0.0714 | - |
0.0116 | 600 | 0.0452 | - |
0.0126 | 650 | 0.028 | - |
0.0136 | 700 | 0.0149 | - |
0.0145 | 750 | 0.0101 | - |
0.0155 | 800 | 0.0067 | - |
0.0165 | 850 | 0.0037 | - |
0.0174 | 900 | 0.0032 | - |
0.0184 | 950 | 0.0023 | - |
0.0194 | 1000 | 0.0017 | - |
0.0203 | 1050 | 0.0011 | - |
0.0213 | 1100 | 0.0006 | - |
0.0223 | 1150 | 0.0011 | - |
0.0232 | 1200 | 0.0016 | - |
0.0242 | 1250 | 0.0021 | - |
0.0252 | 1300 | 0.0004 | - |
0.0261 | 1350 | 0.0003 | - |
0.0271 | 1400 | 0.0009 | - |
0.0281 | 1450 | 0.002 | - |
0.0290 | 1500 | 0.0008 | - |
0.0300 | 1550 | 0.0012 | - |
0.0310 | 1600 | 0.0003 | - |
0.0319 | 1650 | 0.0002 | - |
0.0329 | 1700 | 0.0003 | - |
0.0339 | 1750 | 0.0002 | - |
0.0348 | 1800 | 0.0001 | - |
0.0358 | 1850 | 0.0001 | - |
0.0368 | 1900 | 0.0001 | - |
0.0377 | 1950 | 0.0001 | - |
0.0387 | 2000 | 0.0001 | - |
0.0397 | 2050 | 0.0001 | - |
0.0407 | 2100 | 0.0001 | - |
0.0416 | 2150 | 0.0001 | - |
0.0426 | 2200 | 0.0001 | - |
0.0436 | 2250 | 0.0001 | - |
0.0445 | 2300 | 0.0001 | - |
0.0455 | 2350 | 0.0001 | - |
0.0465 | 2400 | 0.0001 | - |
0.0474 | 2450 | 0.0001 | - |
0.0484 | 2500 | 0.0001 | - |
0.0494 | 2550 | 0.0 | - |
0.0503 | 2600 | 0.0 | - |
0.0513 | 2650 | 0.0 | - |
0.0523 | 2700 | 0.0 | - |
0.0532 | 2750 | 0.0 | - |
0.0542 | 2800 | 0.0 | - |
0.0552 | 2850 | 0.0 | - |
0.0561 | 2900 | 0.0 | - |
0.0571 | 2950 | 0.0 | - |
0.0581 | 3000 | 0.0 | - |
0.0590 | 3050 | 0.0 | - |
0.0600 | 3100 | 0.0 | - |
0.0610 | 3150 | 0.0 | - |
0.0619 | 3200 | 0.0 | - |
0.0629 | 3250 | 0.0 | - |
0.0639 | 3300 | 0.0 | - |
0.0649 | 3350 | 0.0 | - |
0.0658 | 3400 | 0.0 | - |
0.0668 | 3450 | 0.0 | - |
0.0678 | 3500 | 0.0 | - |
0.0687 | 3550 | 0.0 | - |
0.0697 | 3600 | 0.0 | - |
0.0707 | 3650 | 0.0 | - |
0.0716 | 3700 | 0.0 | - |
0.0726 | 3750 | 0.0 | - |
0.0736 | 3800 | 0.0 | - |
0.0745 | 3850 | 0.0 | - |
0.0755 | 3900 | 0.0 | - |
0.0765 | 3950 | 0.0 | - |
0.0774 | 4000 | 0.0 | - |
0.0784 | 4050 | 0.0 | - |
0.0794 | 4100 | 0.0 | - |
0.0803 | 4150 | 0.0 | - |
0.0813 | 4200 | 0.0 | - |
0.0823 | 4250 | 0.0 | - |
0.0832 | 4300 | 0.0 | - |
0.0842 | 4350 | 0.0 | - |
0.0852 | 4400 | 0.0 | - |
0.0861 | 4450 | 0.0 | - |
0.0871 | 4500 | 0.0 | - |
0.0881 | 4550 | 0.0 | - |
0.0890 | 4600 | 0.0 | - |
0.0900 | 4650 | 0.0 | - |
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0.0958 | 4950 | 0.0 | - |
0.0968 | 5000 | 0.0 | - |
0.0978 | 5050 | 0.0 | - |
0.0987 | 5100 | 0.0 | - |
0.0997 | 5150 | 0.0 | - |
0.1007 | 5200 | 0.0 | - |
0.1016 | 5250 | 0.0 | - |
0.1026 | 5300 | 0.0 | - |
0.1036 | 5350 | 0.0 | - |
0.1045 | 5400 | 0.0 | - |
0.1055 | 5450 | 0.0 | - |
0.1065 | 5500 | 0.0 | - |
0.1074 | 5550 | 0.0 | - |
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0.1094 | 5650 | 0.0 | - |
0.1103 | 5700 | 0.0 | - |
0.1113 | 5750 | 0.0 | - |
0.1123 | 5800 | 0.0 | - |
0.1132 | 5850 | 0.0 | - |
0.1142 | 5900 | 0.0 | - |
0.1152 | 5950 | 0.0 | - |
0.1162 | 6000 | 0.0 | - |
0.1171 | 6050 | 0.0 | - |
0.1181 | 6100 | 0.0 | - |
0.1191 | 6150 | 0.0 | - |
0.1200 | 6200 | 0.0 | - |
0.1210 | 6250 | 0.0 | - |
0.1220 | 6300 | 0.0 | - |
0.1229 | 6350 | 0.0 | - |
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0.1471 | 7600 | 0.0 | - |
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0.1500 | 7750 | 0.0 | - |
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0.1549 | 8000 | 0.0 | - |
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0.1704 | 8800 | 0.0 | - |
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0.1742 | 9000 | 0.0 | - |
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0.1762 | 9100 | 0.0 | - |
0.1771 | 9150 | 0.0 | - |
0.1781 | 9200 | 0.0 | - |
0.1791 | 9250 | 0.0 | - |
0.1800 | 9300 | 0.0 | - |
0.1810 | 9350 | 0.0 | - |
0.1820 | 9400 | 0.0 | - |
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0.1926 | 9950 | 0.0 | - |
0.1936 | 10000 | 0.0 | - |
0.1946 | 10050 | 0.0 | - |
0.1955 | 10100 | 0.0 | - |
0.1965 | 10150 | 0.0 | - |
0.1975 | 10200 | 0.0 | - |
0.1984 | 10250 | 0.0 | - |
0.1994 | 10300 | 0.0 | - |
0.2004 | 10350 | 0.0 | - |
0.2013 | 10400 | 0.0 | - |
0.2023 | 10450 | 0.0 | - |
0.2033 | 10500 | 0.0 | - |
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0.2052 | 10600 | 0.0 | - |
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0.2071 | 10700 | 0.1864 | - |
0.2081 | 10750 | 0.0643 | - |
0.2091 | 10800 | 0.0257 | - |
0.2100 | 10850 | 0.0125 | - |
0.2110 | 10900 | 0.0097 | - |
0.2120 | 10950 | 0.0072 | - |
0.2129 | 11000 | 0.0032 | - |
0.2139 | 11050 | 0.001 | - |
0.2149 | 11100 | 0.0001 | - |
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0.4007 | 20700 | 0.0 | - |
0.4017 | 20750 | 0.0 | - |
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0.8934 | 46150 | 0.0 | - |
0.8944 | 46200 | 0.0 | - |
0.8953 | 46250 | 0.0 | - |
0.8963 | 46300 | 0.0 | - |
0.8973 | 46350 | 0.0 | - |
0.8982 | 46400 | 0.0 | - |
0.8992 | 46450 | 0.0 | - |
0.9002 | 46500 | 0.0 | - |
0.9011 | 46550 | 0.0 | - |
0.9021 | 46600 | 0.0 | - |
0.9031 | 46650 | 0.0 | - |
0.9040 | 46700 | 0.0 | - |
0.9050 | 46750 | 0.0 | - |
0.9060 | 46800 | 0.0 | - |
0.9069 | 46850 | 0.0 | - |
0.9079 | 46900 | 0.0 | - |
0.9089 | 46950 | 0.0 | - |
0.9098 | 47000 | 0.0 | - |
0.9108 | 47050 | 0.0 | - |
0.9118 | 47100 | 0.0 | - |
0.9128 | 47150 | 0.0 | - |
0.9137 | 47200 | 0.0 | - |
0.9147 | 47250 | 0.0 | - |
0.9157 | 47300 | 0.0 | - |
0.9166 | 47350 | 0.0 | - |
0.9176 | 47400 | 0.0 | - |
0.9186 | 47450 | 0.0 | - |
0.9195 | 47500 | 0.0 | - |
0.9205 | 47550 | 0.0 | - |
0.9215 | 47600 | 0.0 | - |
0.9224 | 47650 | 0.0 | - |
0.9234 | 47700 | 0.0 | - |
0.9244 | 47750 | 0.0 | - |
0.9253 | 47800 | 0.0 | - |
0.9263 | 47850 | 0.0 | - |
0.9273 | 47900 | 0.0 | - |
0.9282 | 47950 | 0.0 | - |
0.9292 | 48000 | 0.0 | - |
0.9302 | 48050 | 0.0 | - |
0.9311 | 48100 | 0.0 | - |
0.9321 | 48150 | 0.0 | - |
0.9331 | 48200 | 0.0 | - |
0.9340 | 48250 | 0.0 | - |
0.9350 | 48300 | 0.0 | - |
0.9360 | 48350 | 0.0 | - |
0.9369 | 48400 | 0.0 | - |
0.9379 | 48450 | 0.0 | - |
0.9389 | 48500 | 0.0 | - |
0.9399 | 48550 | 0.0 | - |
0.9408 | 48600 | 0.0 | - |
0.9418 | 48650 | 0.0 | - |
0.9428 | 48700 | 0.0 | - |
0.9437 | 48750 | 0.0 | - |
0.9447 | 48800 | 0.0 | - |
0.9457 | 48850 | 0.0 | - |
0.9466 | 48900 | 0.0 | - |
0.9476 | 48950 | 0.0 | - |
0.9486 | 49000 | 0.0 | - |
0.9495 | 49050 | 0.0 | - |
0.9505 | 49100 | 0.0 | - |
0.9515 | 49150 | 0.0 | - |
0.9524 | 49200 | 0.0 | - |
0.9534 | 49250 | 0.0 | - |
0.9544 | 49300 | 0.0 | - |
0.9553 | 49350 | 0.0 | - |
0.9563 | 49400 | 0.0 | - |
0.9573 | 49450 | 0.0 | - |
0.9582 | 49500 | 0.0 | - |
0.9592 | 49550 | 0.0 | - |
0.9602 | 49600 | 0.0 | - |
0.9611 | 49650 | 0.0 | - |
0.9621 | 49700 | 0.0 | - |
0.9631 | 49750 | 0.0 | - |
0.9641 | 49800 | 0.0 | - |
0.9650 | 49850 | 0.0 | - |
0.9660 | 49900 | 0.0 | - |
0.9670 | 49950 | 0.0 | - |
0.9679 | 50000 | 0.0 | - |
0.9689 | 50050 | 0.0 | - |
0.9699 | 50100 | 0.0 | - |
0.9708 | 50150 | 0.0 | - |
0.9718 | 50200 | 0.0 | - |
0.9728 | 50250 | 0.0 | - |
0.9737 | 50300 | 0.0 | - |
0.9747 | 50350 | 0.0 | - |
0.9757 | 50400 | 0.0 | - |
0.9766 | 50450 | 0.0 | - |
0.9776 | 50500 | 0.0 | - |
0.9786 | 50550 | 0.0 | - |
0.9795 | 50600 | 0.0 | - |
0.9805 | 50650 | 0.0 | - |
0.9815 | 50700 | 0.0 | - |
0.9824 | 50750 | 0.0 | - |
0.9834 | 50800 | 0.0 | - |
0.9844 | 50850 | 0.0 | - |
0.9853 | 50900 | 0.0 | - |
0.9863 | 50950 | 0.0 | - |
0.9873 | 51000 | 0.0 | - |
0.9882 | 51050 | 0.0 | - |
0.9892 | 51100 | 0.0 | - |
0.9902 | 51150 | 0.0 | - |
0.9912 | 51200 | 0.0 | - |
0.9921 | 51250 | 0.0 | - |
0.9931 | 51300 | 0.0 | - |
0.9941 | 51350 | 0.0 | - |
0.9950 | 51400 | 0.0 | - |
0.9960 | 51450 | 0.0 | - |
0.9970 | 51500 | 0.0 | - |
0.9979 | 51550 | 0.0 | - |
0.9989 | 51600 | 0.0 | - |
0.9999 | 51650 | 0.0 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.2.0.dev0
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0.dev0
- PyTorch: 2.5.1+cu121
- Datasets: 3.2.0
- Tokenizers: 0.21.0
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}
}