SetFit with T-Systems-onsite/cross-en-de-roberta-sentence-transformer

This is a SetFit model that can be used for Text Classification. This SetFit model uses T-Systems-onsite/cross-en-de-roberta-sentence-transformer 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: T-Systems-onsite/cross-en-de-roberta-sentence-transformer
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 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
supportive	'Die Debatte um ein nationales Tempolimit auf deutschen Autobahnen gewinnt an Fahrt, da eine wachsende Mehrheit der Bürger dies im Interesse des Umweltschutzes und der Verkehrssicherheit befürwortet. Trotz der historischen Skepsis gegenüber solchen Maßnahmen zeigt sich ein Umdenken in der Bevölkerung, wobei mehr als 60 Prozent für eine Geschwindigkeitsbegrenzung sind. Die Frage bleibt jedoch bestehen, wie lange jene, die sich vehement dagegen aussprechen, ihre Vorbehalte gegenüber einem umweltfreundlicheren und sichereren Verkehr in Deutschland aufrechterhalten können.' 'In jüngsten Umfragen zeigt sich eine bemerkenswerte Verschiebung in der öffentlichen Meinung: Mehr als 60 Prozent der Deutschen sprechen sich nun für ein nationales Tempolimit auf Autobahnen aus, getrieben von den Zielen des Umweltschutzes und der Verkehrssicherheit. Diese gesetzgeberischen Bestrebungen stoßen jedoch nach wie vor auf Widerstand bei jenen, die ihr Recht auf schnelle Mobilität über kollektive Belange stellen. Obwohl eine solche Maßnahme Herausforderungen in ihrer Umsetzung mit sich bringt, könnten sie einen wichtigen Schritt darstellen, um die ökologischen Fußabdrücke unserer Verkehrsinfrastruktur zu reduzieren und tödliche Unfälle zu verhindern.' 'Die Debatte um ein nationales Tempolimit auf deutschen Autobahnen findet zunehmend Anklang, wobei über 60 Prozent der Bevölkerung laut Umweltbundesamt dieses Maßnahme im Sinne des Klimaschutzes und der Verkehrssicherheit befürworten. Während sich Skepsis in manchen Kreisen hält, scheint die öffentliche Meinung einem Paradigmenwechsel entgegenzugehen, der den egozentrischen Widerstand gegen einen moderateren Fahrstil langsam schwinden lässt. Trotzdem bleibt abzuwarten, wie sich Gesetzesinitiativen konkret gestalten und welche Herausforderungen bei deren Umsetzung zu bewältigen sind.'
opposed	'Die Debatte um das nationale Tempolimit auf Autobahnen spaltet nicht nur die Länder, sondern auch die Union selbst. Während Niedersachsen einen Vorstoß unternimmt, rufen führende CDU-Vertreter aus Hessen und dem Saarland lautstark dagegen an: "Kein Tempolimit!" Ines Claus und Frank Wagner vertreten mit Nachdruck die Auffassung, dass das Fahren auf deutschen Autobahnen kein Spielball politischer Bevormundung sein sollte. Sie plädieren für Eigenverantwortung statt bürokratische Fahrverbote und sehen in der individuellen Verhaltensänderung der Autofahrerinnen und Autofahrern die bessere Lösung, um Treibstoff- und Emissionseinsparungen zu erreichen. Die Debatte bleibt hitzig – wer wird sich durchsetzen?' 'Ein nationaler Tempolimit-Wahnsinn auf deutschen Autobahnen? Immer mehr Politiker zeigen klare Kante gegen den Bremsklotz-Irrsinn und pochen auf Freiheit statt Fahrspaß-Verhüterli! Warum soll der mündige Bürger ausgebremst werden, wenn Treibstoffsparen auch anders geht?' 'Die Forderung nach einem generellen Tempolimit auf deutschen Autobahnen wirkt wie ein unnötiger Eingriff in die individuelle Freiheit der Autofahrer, zumal die Unfallstatistiken im europäischen Vergleich für sich sprechen. Während andere Länder mit Tempolimits kämpfen, zeigt sich Deutschland als Vorreiter in Sachen Verkehrssicherheit und moderner Fahrzeugtechnologie. Statt pauschaler Beschränkungen sollten wir auf innovative Lösungen setzen, die den Verkehrsfluss verbessern und gleichzeitig die Umwelt schonen.'
neutral	'Der Bundestag berät derzeit über Vorschläge zur Einführung eines nationalen Tempolimits auf deutschen Autobahnen, wobei sowohl Umweltauswirkungen als auch Sicherheitsaspekte eine Rolle spielen. Befürworter argumentieren, dass ein Tempolimit den CO2-Ausstoß reduzieren und die Verkehrssicherheit erhöhen könnte, während Gegner darauf hinweisen, dass die Freiheit der individuellen Mobilität eingeschränkt würde.' 'Im politischen Diskurs um ein nationales Tempolimit auf Autobahnen gibt es erneut Debatten, die sowohl Befürworter als auch Kritiker mobilisieren. Während die aktuelle Vereinbarung im Koalitionsvertrag ein Tempolimit ausschließt, bleiben die Forderungen nach einer neuen Gesetzesinitiative angesichts von Klima- und Sicherheitsüberlegungen weiter präsent. Bislang hat sich jedoch keine maßgebliche Veränderung des bestehenden Konsenses ergeben.' 'Der jüngste Vorschlag zur Einführung eines bundesweiten Tempolimits auf deutschen Autobahnen wurde im Bundestag intensiv debattiert, wobei Befürworter die Verbesserung der Verkehrssicherheit und die mögliche Reduzierung von CO2-Emissionen betonen. Gegner sehen hingegen eine Einschränkung individueller Freiheitsrechte und den potenziellen Einfluss auf den Verkehrsfluss als Hauptpunkte der Kritik.'

Evaluation

Metrics

Label	Accuracy
all	0.35

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_speedlimit_v0.1")
# Run inference
preds = model("Die Straßen in Deutschland könnten künftig ähnlich überlastet sein wie die Schienen, befürchtet Verkehrsminister Volker Wissing. Der FDP-Politiker will das mit schnelleren Planungsverfahren verhindern - doch das grüne Umweltministerium ist dagegen. Ein Tempolimit auf Autobahnen lehnt er weiterhin ab.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	60.7587	127

Label	Training Sample Count
neutral	350
opposed	403
supportive	399

Training Hyperparameters

batch_size: (32, 32)
num_epochs: (3, 3)
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	0.2239	-
0.0018	50	0.2351	-
0.0036	100	0.2327	-
0.0054	150	0.2197	-
0.0072	200	0.2034	-
0.0091	250	0.1914	-
0.0109	300	0.1718	-
0.0127	350	0.1476	-
0.0145	400	0.1127	-
0.0163	450	0.0685	-
0.0181	500	0.039	-
0.0199	550	0.024	-
0.0217	600	0.0178	-
0.0236	650	0.01	-
0.0254	700	0.007	-
0.0272	750	0.0057	-
0.0290	800	0.0038	-
0.0308	850	0.0025	-
0.0326	900	0.0022	-
0.0344	950	0.0023	-
0.0362	1000	0.0014	-
0.0381	1050	0.0016	-
0.0399	1100	0.001	-
0.0417	1150	0.0009	-
0.0435	1200	0.0007	-
0.0453	1250	0.0007	-
0.0471	1300	0.0006	-
0.0489	1350	0.0004	-
0.0507	1400	0.0004	-
0.0525	1450	0.0003	-
0.0544	1500	0.0003	-
0.0562	1550	0.0003	-
0.0580	1600	0.0003	-
0.0598	1650	0.0002	-
0.0616	1700	0.0002	-
0.0634	1750	0.0002	-
0.0652	1800	0.0002	-
0.0670	1850	0.0002	-
0.0689	1900	0.0001	-
0.0707	1950	0.0001	-
0.0725	2000	0.0001	-
0.0743	2050	0.0001	-
0.0761	2100	0.0001	-
0.0779	2150	0.0001	-
0.0797	2200	0.0001	-
0.0815	2250	0.0001	-
0.0834	2300	0.0001	-
0.0852	2350	0.0001	-
0.0870	2400	0.0001	-
0.0888	2450	0.0001	-
0.0906	2500	0.0001	-
0.0924	2550	0.0001	-
0.0942	2600	0.0001	-
0.0960	2650	0.0001	-
0.0978	2700	0.0	-
0.0997	2750	0.0	-
0.1015	2800	0.0	-
0.1033	2850	0.0	-
0.1051	2900	0.0	-
0.1069	2950	0.0	-
0.1087	3000	0.0	-
0.1105	3050	0.0	-
0.1123	3100	0.0	-
0.1142	3150	0.0	-
0.1160	3200	0.0	-
0.1178	3250	0.0	-
0.1196	3300	0.0	-
0.1214	3350	0.0	-
0.1232	3400	0.0	-
0.1250	3450	0.0	-
0.1268	3500	0.0	-
0.1287	3550	0.0	-
0.1305	3600	0.0	-
0.1323	3650	0.0	-
0.1341	3700	0.0	-
0.1359	3750	0.0	-
0.1377	3800	0.0	-
0.1395	3850	0.0	-
0.1413	3900	0.0	-
0.1431	3950	0.0	-
0.1450	4000	0.0	-
0.1468	4050	0.0	-
0.1486	4100	0.0	-
0.1504	4150	0.0	-
0.1522	4200	0.0	-
0.1540	4250	0.0	-
0.1558	4300	0.0	-
0.1576	4350	0.0	-
0.1595	4400	0.0	-
0.1613	4450	0.0	-
0.1631	4500	0.0	-
0.1649	4550	0.0	-
0.1667	4600	0.0	-
0.1685	4650	0.0	-
0.1703	4700	0.0	-
0.1721	4750	0.0	-
0.1740	4800	0.0	-
0.1758	4850	0.0	-
0.1776	4900	0.0	-
0.1794	4950	0.0	-
0.1812	5000	0.0	-
0.1830	5050	0.0	-
0.1848	5100	0.0	-
0.1866	5150	0.0	-
0.1884	5200	0.0	-
0.1903	5250	0.0	-
0.1921	5300	0.0081	-
0.1939	5350	0.0103	-
0.1957	5400	0.001	-
0.1975	5450	0.002	-
0.1993	5500	0.0001	-
0.2011	5550	0.0	-
0.2029	5600	0.0	-
0.2048	5650	0.0	-
0.2066	5700	0.0	-
0.2084	5750	0.0	-
0.2102	5800	0.0	-
0.2120	5850	0.0	-
0.2138	5900	0.0	-
0.2156	5950	0.0	-
0.2174	6000	0.0	-
0.2193	6050	0.0	-
0.2211	6100	0.0	-
0.2229	6150	0.0	-
0.2247	6200	0.0	-
0.2265	6250	0.0	-
0.2283	6300	0.0	-
0.2301	6350	0.0	-
0.2319	6400	0.0	-
0.2337	6450	0.0	-
0.2356	6500	0.0	-
0.2374	6550	0.0	-
0.2392	6600	0.0	-
0.2410	6650	0.0	-
0.2428	6700	0.0	-
0.2446	6750	0.0	-
0.2464	6800	0.0	-
0.2482	6850	0.0	-
0.2501	6900	0.0	-
0.2519	6950	0.0	-
0.2537	7000	0.0	-
0.2555	7050	0.0	-
0.2573	7100	0.0	-
0.2591	7150	0.0	-
0.2609	7200	0.0	-
0.2627	7250	0.0	-
0.2646	7300	0.0	-
0.2664	7350	0.0	-
0.2682	7400	0.0	-
0.2700	7450	0.0	-
0.2718	7500	0.0	-
0.2736	7550	0.0	-
0.2754	7600	0.0	-
0.2772	7650	0.0	-
0.2790	7700	0.0	-
0.2809	7750	0.0	-
0.2827	7800	0.0	-
0.2845	7850	0.0	-
0.2863	7900	0.0	-
0.2881	7950	0.0	-
0.2899	8000	0.0	-
0.2917	8050	0.0	-
0.2935	8100	0.0	-
0.2954	8150	0.0	-
0.2972	8200	0.0	-
0.2990	8250	0.0	-
0.3008	8300	0.0	-
0.3026	8350	0.0	-
0.3044	8400	0.0	-
0.3062	8450	0.0	-
0.3080	8500	0.0	-
0.3098	8550	0.0	-
0.3117	8600	0.0	-
0.3135	8650	0.0	-
0.3153	8700	0.0	-
0.3171	8750	0.0	-
0.3189	8800	0.0	-
0.3207	8850	0.0	-
0.3225	8900	0.0	-
0.3243	8950	0.0	-
0.3262	9000	0.0	-
0.3280	9050	0.0	-
0.3298	9100	0.0	-
0.3316	9150	0.0	-
0.3334	9200	0.0	-
0.3352	9250	0.0	-
0.3370	9300	0.0	-
0.3388	9350	0.0	-
0.3407	9400	0.0	-
0.3425	9450	0.0	-
0.3443	9500	0.0	-
0.3461	9550	0.0	-
0.3479	9600	0.0	-
0.3497	9650	0.0	-
0.3515	9700	0.0	-
0.3533	9750	0.0	-
0.3551	9800	0.0	-
0.3570	9850	0.0	-
0.3588	9900	0.0	-
0.3606	9950	0.0	-
0.3624	10000	0.0	-
0.3642	10050	0.0	-
0.3660	10100	0.0	-
0.3678	10150	0.0	-
0.3696	10200	0.0	-
0.3715	10250	0.0	-
0.3733	10300	0.0	-
0.3751	10350	0.0	-
0.3769	10400	0.0	-
0.3787	10450	0.0	-
0.3805	10500	0.0	-
0.3823	10550	0.0	-
0.3841	10600	0.0	-
0.3860	10650	0.0	-
0.3878	10700	0.0	-
0.3896	10750	0.0	-
0.3914	10800	0.0	-
0.3932	10850	0.0	-
0.3950	10900	0.0	-
0.3968	10950	0.0	-
0.3986	11000	0.0	-
0.4004	11050	0.0	-
0.4023	11100	0.0	-
0.4041	11150	0.0	-
0.4059	11200	0.0	-
0.4077	11250	0.0	-
0.4095	11300	0.0	-
0.4113	11350	0.0	-
0.4131	11400	0.0	-
0.4149	11450	0.0	-
0.4168	11500	0.0	-
0.4186	11550	0.0	-
0.4204	11600	0.0	-
0.4222	11650	0.0	-
0.4240	11700	0.0	-
0.4258	11750	0.0	-
0.4276	11800	0.0	-
0.4294	11850	0.0	-
0.4313	11900	0.0	-
0.4331	11950	0.0	-
0.4349	12000	0.0	-
0.4367	12050	0.0	-
0.4385	12100	0.0	-
0.4403	12150	0.0	-
0.4421	12200	0.0	-
0.4439	12250	0.0	-
0.4457	12300	0.0	-
0.4476	12350	0.0	-
0.4494	12400	0.0	-
0.4512	12450	0.0	-
0.4530	12500	0.0	-
0.4548	12550	0.0	-
0.4566	12600	0.0	-
0.4584	12650	0.0	-
0.4602	12700	0.0	-
0.4621	12750	0.0	-
0.4639	12800	0.0	-
0.4657	12850	0.0	-
0.4675	12900	0.0001	-
0.4693	12950	0.0001	-
0.4711	13000	0.0	-
0.4729	13050	0.0	-
0.4747	13100	0.0	-
0.4766	13150	0.0	-
0.4784	13200	0.0	-
0.4802	13250	0.0	-
0.4820	13300	0.0	-
0.4838	13350	0.0	-
0.4856	13400	0.0	-
0.4874	13450	0.0	-
0.4892	13500	0.0	-
0.4910	13550	0.0	-
0.4929	13600	0.0	-
0.4947	13650	0.0	-
0.4965	13700	0.0	-
0.4983	13750	0.0	-
0.5001	13800	0.0	-
0.5019	13850	0.0	-
0.5037	13900	0.0	-
0.5055	13950	0.0	-
0.5074	14000	0.0	-
0.5092	14050	0.0	-
0.5110	14100	0.0	-
0.5128	14150	0.0	-
0.5146	14200	0.0	-
0.5164	14250	0.0	-
0.5182	14300	0.0	-
0.5200	14350	0.0	-
0.5219	14400	0.0	-
0.5237	14450	0.0	-
0.5255	14500	0.0	-
0.5273	14550	0.0	-
0.5291	14600	0.0	-
0.5309	14650	0.0	-
0.5327	14700	0.0	-
0.5345	14750	0.0	-
0.5363	14800	0.0	-
0.5382	14850	0.0	-
0.5400	14900	0.0	-
0.5418	14950	0.0	-
0.5436	15000	0.0	-
0.5454	15050	0.0	-
0.5472	15100	0.0	-
0.5490	15150	0.0	-
0.5508	15200	0.0	-
0.5527	15250	0.0	-
0.5545	15300	0.0	-
0.5563	15350	0.0	-
0.5581	15400	0.0	-
0.5599	15450	0.0	-
0.5617	15500	0.0	-
0.5635	15550	0.0	-
0.5653	15600	0.0	-
0.5672	15650	0.0	-
0.5690	15700	0.0	-
0.5708	15750	0.0	-
0.5726	15800	0.0	-
0.5744	15850	0.0	-
0.5762	15900	0.0	-
0.5780	15950	0.0	-
0.5798	16000	0.0	-
0.5816	16050	0.0	-
0.5835	16100	0.0	-
0.5853	16150	0.0	-
0.5871	16200	0.0	-
0.5889	16250	0.0	-
0.5907	16300	0.0	-
0.5925	16350	0.0	-
0.5943	16400	0.0	-
0.5961	16450	0.0	-
0.5980	16500	0.0	-
0.5998	16550	0.0	-
0.6016	16600	0.0	-
0.6034	16650	0.0	-
0.6052	16700	0.0	-
0.6070	16750	0.0	-
0.6088	16800	0.0	-
0.6106	16850	0.0	-
0.6125	16900	0.0	-
0.6143	16950	0.0	-
0.6161	17000	0.0	-
0.6179	17050	0.0	-
0.6197	17100	0.0	-
0.6215	17150	0.0	-
0.6233	17200	0.0	-
0.6251	17250	0.0	-
0.6269	17300	0.0	-
0.6288	17350	0.0	-
0.6306	17400	0.0	-
0.6324	17450	0.0	-
0.6342	17500	0.0	-
0.6360	17550	0.0	-
0.6378	17600	0.0	-
0.6396	17650	0.0	-
0.6414	17700	0.0	-
0.6433	17750	0.0	-
0.6451	17800	0.0	-
0.6469	17850	0.0	-
0.6487	17900	0.0	-
0.6505	17950	0.0	-
0.6523	18000	0.0	-
0.6541	18050	0.0	-
0.6559	18100	0.0	-
0.6578	18150	0.0	-
0.6596	18200	0.0	-
0.6614	18250	0.0	-
0.6632	18300	0.0	-
0.6650	18350	0.0	-
0.6668	18400	0.0	-
0.6686	18450	0.0	-
0.6704	18500	0.0	-
0.6722	18550	0.0	-
0.6741	18600	0.0	-
0.6759	18650	0.0	-
0.6777	18700	0.0	-
0.6795	18750	0.0	-
0.6813	18800	0.0	-
0.6831	18850	0.0	-
0.6849	18900	0.0	-
0.6867	18950	0.0	-
0.6886	19000	0.0	-
0.6904	19050	0.0	-
0.6922	19100	0.0	-
0.6940	19150	0.0	-
0.6958	19200	0.0	-
0.6976	19250	0.0	-
0.6994	19300	0.0	-
0.7012	19350	0.0	-
0.7031	19400	0.0	-
0.7049	19450	0.0	-
0.7067	19500	0.0	-
0.7085	19550	0.0	-
0.7103	19600	0.0	-
0.7121	19650	0.0	-
0.7139	19700	0.0	-
0.7157	19750	0.0	-
0.7175	19800	0.0	-
0.7194	19850	0.0	-
0.7212	19900	0.0	-
0.7230	19950	0.0	-
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2.9970	82700	0.0	-
2.9988	82750	0.0	-

Framework Versions

Python: 3.11.11
SetFit: 1.1.1
Sentence Transformers: 3.3.1
Transformers: 4.42.2
PyTorch: 2.5.1+cu121
Datasets: 3.2.0
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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278M params

Tensor type

F32

Model tree for cbpuschmann/klimacoder_speedlimit_v0.1

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T-Systems-onsite/cross-en-de-roberta-sentence-transformer

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(3)

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1 model

Evaluation results

Accuracy on Unknown
test set self-reported

0.350

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