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cbpuschmann/
klimacoder_protest_v0.1

Name: klimacoder_protest_v0.1
Availability: InStock

setfit

safetensors

xlm-roberta

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

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

finetune

model-index

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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
opposed	'In jüngster Zeit häufen sich die Proteste der Gruppierungen "Fridays for Future" und "Letzte Generation", deren drastische Aktionen mehr Fragen als Antworten hinterlassen. Während sie mit emotional aufgeladenen Szenarien von Klimakatastrophen für Aufmerksamkeit sorgen, scheint ihr Vorgehen oft wenig sachlich fundiert zu sein. Anstatt konstruktiver Lösungen bieten ihre teils chaotischen Demonstrationen eher eine Plattform für mediale Schlagzeilen als für nachhaltigen Fortschritt in der Klimapolitik.' 'Die radikalen Klima-Aktivisten von Gruppen wie Fridays for Future und Letzte Generation finanzieren ihre umstrittenen Aktionen durch Spenden und Unterstützung von internationalen Fonds, was Fragen zur Unabhängigkeit und den tatsächlichen Motiven dieser Bewegungen aufwirft. Während sie öffentlich für den Klimaschutz eintreten, bleibt unklar, wie viel Einfluss externe Geldgeber auf ihre Strategien und Entscheidungen haben.' 'In den letzten Jahren hat der Druck von Gruppen wie Fridays for Future und der Letzte Generation auf die öffentliche Meinung stetig zugenommen – oft ohne Rücksicht auf die Auswirkungen ihrer Methoden auf Alltagsleben und Wirtschaft. Während diese Aktivisten ihre Aktionen als nötige Maßnahmen für den Klimaschutz darstellen, bleibt unklar, inwiefern ihre radikalen Ansätze tatsächlich nachhaltige Lösungen oder bloße Aufmerksamkeitsstunts sind. Finanziert durch Spenden und Unterstützung aus dem Ausland wie dem ›Climate Emergency Fund‹, scheinen die Mittel oft mehr auf Kampagnen als auf konkrete Veränderungen gerichtet zu sein.\n\nDisclaimer: Dieser Beitrag reflektiert eine kritische Haltung und nicht unbedingt die Meinung des gesamten Lesepublikums.'
supportive	'Klima-Aktivismus-Gruppen wie Fridays for Future und Letzte Generation überschreiten mit blockierenden und teils störenden Aktionen oft die Grenze des Zumutbaren, doch sie zwingen dringend benötigte Debatten über Umweltpolitik auf die Agenda. Angesichts des stockenden Fortschritts beim Klimaschutz könnte ihr vehementes Auftreten jedoch der nötige Weckruf sein, um die Politik zu entschlossenem Handeln zu bewegen.' 'Inmitten wachsender Spannungen um die Klima-Agenda ziehen die Aktionen von Gruppen wie Fridays for Future und Letzte Generation sowohl Bewunderung als auch Kritik auf sich. Während ihre Methoden oft als disruptiv empfunden werden, lenken sie unbestreitbar den dringend benötigten Fokus auf die klimatischen Herausforderungen unserer Zeit.' 'Die jüngsten Aktionen der Klima-Aktivismus-Gruppen wie Fridays for Future und Letzte Generation haben zweifellos die öffentliche Wahrnehmung verschärft und Unternehmer dazu gezwungen, den Kopf aus dem Sand zu ziehen. Es bleibt jedoch fraglich, ob diese Druckmittel langfristig zu nachhaltigen Veränderungen führen oder nur kurzfristige Reaktionen hervorrufen. Trotzdem ist unverkennbar, dass der Anstieg der Energiepreise einen entscheidenden Katalysator darstellt, der Unternehmen schneller in die nachhaltige Energieversorgung treibt – ein Zeichen dafür, dass auch ökonomische Faktoren eine bedeutende Rolle spielen.'
neutral	'Klimaschutz-Aktivisten von Gruppen wie Fridays for Future und Letzte Generation haben erneut durch Protestaktionen auf die Dringlichkeit klimapolitischer Maßnahmen aufmerksam gemacht. Während Befürworter die Notwendigkeit solcher Aktionen zur Erreichung ehrgeiziger Klimaziele betonen, kritisieren Gegner die teils disruptiven Methoden und fordern einen Dialog innerhalb bestehender politischer Prozesse.' 'Bei Aktionen der Klima-Aktivismus-Gruppen wie Fridays for Future und der Letzten Generation haben am Wochenende in mehreren deutschen Städten zahlreiche Menschen für mehr Klimaschutz demonstriert. In Berlin versammelten sich laut Polizeiangaben rund 3000 Teilnehmer, während die Veranstalter von etwa 5000 Demonstrierenden sprachen. Die Gruppen fordern unter anderem eine schnellere Umsetzung von Maßnahmen zur Reduzierung der CO2-Emissionen.' 'Am Freitag haben in Baden-Württemberg mehrere Tausend Menschen bei Demonstrationen der Klimaschutzbewegung Fridays for Future (FFF) und der Letzten Generation für mehr Umweltschutz demonstriert. In Freiburg zählte die Polizei bis zu 5000 Teilnehmer, während FFF von etwa 8000 Beteiligten berichtete. Die Proteste standen unter dem Motto „People not Profit“ und richteten sich gegen wirtschaftliche Interessen zugunsten des Klimaschutzes.\n\nDie Demonstrationen spiegeln die wachsende Besorgnis junger Menschen über den Klimawandel wider, während sie auch Diskussionen über die Balance zwischen ökonomischen Entwicklungen und Umweltschutz anstoßen.'

Evaluation

Metrics

Label	Accuracy
all	0.5616

Uses

Direct Use for Inference

First install the SetFit library:

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("cbpuschmann/klimacoder_protest_v0.1")
# Run inference
preds = model("Aber ratzfatz waren sie ab; Klima-Kleber blockieren Bundesstraße in Dortmund Dortmund - Klimaaktivist der Gruppierung ›DLG› haben am Mittwoch den Straßenverkehr in der Dortmunder Innenstadt vorübergehend gestört. Am Morgen blockierten fünf Menschen einen Teil der zentralen zweispurigen B54 stadteinwärts, einige klebten sich fest.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	35	69.3880	934

Label	Training Sample Count
neutral	364
opposed	398
supportive	367

Training Hyperparameters

batch_size: (16, 16)
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	0.1907	-
0.0009	50	0.2333	-
0.0019	100	0.2279	-
0.0028	150	0.2239	-
0.0038	200	0.2268	-
0.0047	250	0.2008	-
0.0057	300	0.1987	-
0.0066	350	0.1811	-
0.0075	400	0.176	-
0.0085	450	0.1651	-
0.0094	500	0.149	-
0.0104	550	0.1267	-
0.0113	600	0.1133	-
0.0122	650	0.0895	-
0.0132	700	0.0827	-
0.0141	750	0.0593	-
0.0151	800	0.0472	-
0.0160	850	0.0357	-
0.0170	900	0.0255	-
0.0179	950	0.0213	-
0.0188	1000	0.0149	-
0.0198	1050	0.0144	-
0.0207	1100	0.0124	-
0.0217	1150	0.0091	-
0.0226	1200	0.0072	-
0.0236	1250	0.0049	-
0.0245	1300	0.0044	-
0.0254	1350	0.003	-
0.0264	1400	0.0036	-
0.0273	1450	0.0028	-
0.0283	1500	0.0024	-
0.0292	1550	0.0017	-
0.0302	1600	0.0021	-
0.0311	1650	0.0013	-
0.0320	1700	0.0012	-
0.0330	1750	0.0013	-
0.0339	1800	0.001	-
0.0349	1850	0.0007	-
0.0358	1900	0.0006	-
0.0367	1950	0.0016	-
0.0377	2000	0.0007	-
0.0386	2050	0.0006	-
0.0396	2100	0.0005	-
0.0405	2150	0.0005	-
0.0415	2200	0.0003	-
0.0424	2250	0.0004	-
0.0433	2300	0.0003	-
0.0443	2350	0.0003	-
0.0452	2400	0.0002	-
0.0462	2450	0.0002	-
0.0471	2500	0.0002	-
0.0481	2550	0.0002	-
0.0490	2600	0.0002	-
0.0499	2650	0.0003	-
0.0509	2700	0.0002	-
0.0518	2750	0.0002	-
0.0528	2800	0.0002	-
0.0537	2850	0.0001	-
0.0546	2900	0.0001	-
0.0556	2950	0.0001	-
0.0565	3000	0.0001	-
0.0575	3050	0.0001	-
0.0584	3100	0.0001	-
0.0594	3150	0.0001	-
0.0603	3200	0.0001	-
0.0612	3250	0.0001	-
0.0622	3300	0.0001	-
0.0631	3350	0.0001	-
0.0641	3400	0.0001	-
0.0650	3450	0.0001	-
0.0660	3500	0.0001	-
0.0669	3550	0.0005	-
0.0678	3600	0.0018	-
0.0688	3650	0.0026	-
0.0697	3700	0.0001	-
0.0707	3750	0.0001	-
0.0716	3800	0.0001	-
0.0726	3850	0.0001	-
0.0735	3900	0.0001	-
0.0744	3950	0.0	-
0.0754	4000	0.0	-
0.0763	4050	0.0	-
0.0773	4100	0.0	-
0.0782	4150	0.0	-
0.0791	4200	0.0	-
0.0801	4250	0.0	-
0.0810	4300	0.0	-
0.0820	4350	0.0	-
0.0829	4400	0.0	-
0.0839	4450	0.0	-
0.0848	4500	0.0	-
0.0857	4550	0.0	-
0.0867	4600	0.0	-
0.0876	4650	0.0	-
0.0886	4700	0.0	-
0.0895	4750	0.0	-
0.0905	4800	0.0	-
0.0914	4850	0.0	-
0.0923	4900	0.0	-
0.0933	4950	0.0	-
0.0942	5000	0.0	-
0.0952	5050	0.0	-
0.0961	5100	0.0	-
0.0970	5150	0.0	-
0.0980	5200	0.0	-
0.0989	5250	0.0	-
0.0999	5300	0.0	-
0.1008	5350	0.0	-
0.1018	5400	0.0	-
0.1027	5450	0.0	-
0.1036	5500	0.0	-
0.1046	5550	0.0	-
0.1055	5600	0.0	-
0.1065	5650	0.0	-
0.1074	5700	0.0	-
0.1084	5750	0.0	-
0.1093	5800	0.0	-
0.1102	5850	0.0	-
0.1112	5900	0.0	-
0.1121	5950	0.0	-
0.1131	6000	0.0	-
0.1140	6050	0.0	-
0.1150	6100	0.0	-
0.1159	6150	0.0	-
0.1168	6200	0.0	-
0.1178	6250	0.0	-
0.1187	6300	0.0	-
0.1197	6350	0.0	-
0.1206	6400	0.0	-
0.1215	6450	0.0	-
0.1225	6500	0.0	-
0.1234	6550	0.0	-
0.1244	6600	0.0	-
0.1253	6650	0.0	-
0.1263	6700	0.0	-
0.1272	6750	0.0	-
0.1281	6800	0.0	-
0.1291	6850	0.0	-
0.1300	6900	0.0	-
0.1310	6950	0.0	-
0.1319	7000	0.0	-
0.1329	7050	0.0	-
0.1338	7100	0.0	-
0.1347	7150	0.0	-
0.1357	7200	0.0	-
0.1366	7250	0.0	-
0.1376	7300	0.0	-
0.1385	7350	0.0	-
0.1394	7400	0.0	-
0.1404	7450	0.0	-
0.1413	7500	0.0	-
0.1423	7550	0.0	-
0.1432	7600	0.0	-
0.1442	7650	0.0	-
0.1451	7700	0.0	-
0.1460	7750	0.0	-
0.1470	7800	0.0	-
0.1479	7850	0.1766	-
0.1489	7900	0.0139	-
0.1498	7950	0.0029	-
0.1508	8000	0.0029	-
0.1517	8050	0.0008	-
0.1526	8100	0.0001	-
0.1536	8150	0.0002	-
0.1545	8200	0.0001	-
0.1555	8250	0.0	-
0.1564	8300	0.0	-
0.1574	8350	0.0	-
0.1583	8400	0.0	-
0.1592	8450	0.0	-
0.1602	8500	0.0	-
0.1611	8550	0.0	-
0.1621	8600	0.0	-
0.1630	8650	0.0	-
0.1639	8700	0.0	-
0.1649	8750	0.0	-
0.1658	8800	0.0	-
0.1668	8850	0.0	-
0.1677	8900	0.0	-
0.1687	8950	0.0	-
0.1696	9000	0.0	-
0.1705	9050	0.0	-
0.1715	9100	0.0	-
0.1724	9150	0.0	-
0.1734	9200	0.0	-
0.1743	9250	0.0	-
0.1753	9300	0.0	-
0.1762	9350	0.0	-
0.1771	9400	0.0	-
0.1781	9450	0.0	-
0.1790	9500	0.0	-
0.1800	9550	0.0	-
0.1809	9600	0.0	-
0.1818	9650	0.0	-
0.1828	9700	0.0	-
0.1837	9750	0.0	-
0.1847	9800	0.0	-
0.1856	9850	0.0	-
0.1866	9900	0.0	-
0.1875	9950	0.0	-
0.1884	10000	0.0	-
0.1894	10050	0.0	-
0.1903	10100	0.0	-
0.1913	10150	0.0	-
0.1922	10200	0.0	-
0.1932	10250	0.0	-
0.1941	10300	0.0	-
0.1950	10350	0.0	-
0.1960	10400	0.0	-
0.1969	10450	0.0	-
0.1979	10500	0.0	-
0.1988	10550	0.0	-
0.1998	10600	0.0	-
0.2007	10650	0.0	-
0.2016	10700	0.0	-
0.2026	10750	0.0	-
0.2035	10800	0.0	-
0.2045	10850	0.0	-
0.2054	10900	0.0	-
0.2063	10950	0.0	-
0.2073	11000	0.0	-
0.2082	11050	0.0	-
0.2092	11100	0.0	-
0.2101	11150	0.0	-
0.2111	11200	0.0	-
0.2120	11250	0.0	-
0.2129	11300	0.0	-
0.2139	11350	0.0	-
0.2148	11400	0.0	-
0.2158	11450	0.0	-
0.2167	11500	0.0	-
0.2177	11550	0.0	-
0.2186	11600	0.0	-
0.2195	11650	0.0	-
0.2205	11700	0.0	-
0.2214	11750	0.0	-
0.2224	11800	0.0	-
0.2233	11850	0.0	-
0.2242	11900	0.0	-
0.2252	11950	0.0	-
0.2261	12000	0.0	-
0.2271	12050	0.0	-
0.2280	12100	0.0	-
0.2290	12150	0.0	-
0.2299	12200	0.0	-
0.2308	12250	0.0	-
0.2318	12300	0.0	-
0.2327	12350	0.0	-
0.2337	12400	0.0	-
0.2346	12450	0.0	-
0.2356	12500	0.0	-
0.2365	12550	0.0	-
0.2374	12600	0.0	-
0.2384	12650	0.0	-
0.2393	12700	0.0	-
0.2403	12750	0.0	-
0.2412	12800	0.0	-
0.2422	12850	0.0	-
0.2431	12900	0.0	-
0.2440	12950	0.0	-
0.2450	13000	0.0	-
0.2459	13050	0.0	-
0.2469	13100	0.0	-
0.2478	13150	0.0	-
0.2487	13200	0.0	-
0.2497	13250	0.0	-
0.2506	13300	0.0	-
0.2516	13350	0.0	-
0.2525	13400	0.0	-
0.2535	13450	0.0	-
0.2544	13500	0.0	-
0.2553	13550	0.0	-
0.2563	13600	0.0	-
0.2572	13650	0.0	-
0.2582	13700	0.0	-
0.2591	13750	0.0	-
0.2601	13800	0.0	-
0.2610	13850	0.0	-
0.2619	13900	0.0	-
0.2629	13950	0.0	-
0.2638	14000	0.0	-
0.2648	14050	0.0	-
0.2657	14100	0.0	-
0.2666	14150	0.0	-
0.2676	14200	0.0	-
0.2685	14250	0.0	-
0.2695	14300	0.0	-
0.2704	14350	0.0	-
0.2714	14400	0.0	-
0.2723	14450	0.0	-
0.2732	14500	0.0	-
0.2742	14550	0.0	-
0.2751	14600	0.0	-
0.2761	14650	0.0	-
0.2770	14700	0.0001	-
0.2780	14750	0.0	-
0.2789	14800	0.0	-
0.2798	14850	0.0	-
0.2808	14900	0.0	-
0.2817	14950	0.0	-
0.2827	15000	0.0	-
0.2836	15050	0.0	-
0.2846	15100	0.0	-
0.2855	15150	0.0	-
0.2864	15200	0.0	-
0.2874	15250	0.0	-
0.2883	15300	0.0	-
0.2893	15350	0.0	-
0.2902	15400	0.0	-
0.2911	15450	0.0	-
0.2921	15500	0.0	-
0.2930	15550	0.0	-
0.2940	15600	0.0	-
0.2949	15650	0.0	-
0.2959	15700	0.0	-
0.2968	15750	0.0	-
0.2977	15800	0.0	-
0.2987	15850	0.0	-
0.2996	15900	0.0	-
0.3006	15950	0.0	-
0.3015	16000	0.0	-
0.3025	16050	0.0	-
0.3034	16100	0.0	-
0.3043	16150	0.0	-
0.3053	16200	0.0	-
0.3062	16250	0.0	-
0.3072	16300	0.0	-
0.3081	16350	0.0	-
0.3090	16400	0.0	-
0.3100	16450	0.0	-
0.3109	16500	0.0	-
0.3119	16550	0.0	-
0.3128	16600	0.0	-
0.3138	16650	0.0	-
0.3147	16700	0.0	-
0.3156	16750	0.0	-
0.3166	16800	0.0	-
0.3175	16850	0.0	-
0.3185	16900	0.0	-
0.3194	16950	0.0	-
0.3204	17000	0.0	-
0.3213	17050	0.0	-
0.3222	17100	0.0	-
0.3232	17150	0.0	-
0.3241	17200	0.0	-
0.3251	17250	0.0	-
0.3260	17300	0.0	-
0.3270	17350	0.0	-
0.3279	17400	0.0	-
0.3288	17450	0.0	-
0.3298	17500	0.0	-
0.3307	17550	0.0	-
0.3317	17600	0.0	-
0.3326	17650	0.0	-
0.3335	17700	0.0	-
0.3345	17750	0.0	-
0.3354	17800	0.0	-
0.3364	17850	0.0	-
0.3373	17900	0.0	-
0.3383	17950	0.0	-
0.3392	18000	0.0	-
0.3401	18050	0.0	-
0.3411	18100	0.0	-
0.3420	18150	0.0	-
0.3430	18200	0.0	-
0.3439	18250	0.0	-
0.3449	18300	0.0	-
0.3458	18350	0.0	-
0.3467	18400	0.0	-
0.3477	18450	0.0	-
0.3486	18500	0.0	-
0.3496	18550	0.0	-
0.3505	18600	0.0	-
0.3514	18650	0.0	-
0.3524	18700	0.0	-
0.3533	18750	0.0	-
0.3543	18800	0.0	-
0.3552	18850	0.0	-
0.3562	18900	0.0	-
0.3571	18950	0.0	-
0.3580	19000	0.0	-
0.3590	19050	0.0	-
0.3599	19100	0.0	-
0.3609	19150	0.0	-
0.3618	19200	0.0	-
0.3628	19250	0.0	-
0.3637	19300	0.0	-
0.3646	19350	0.0	-
0.3656	19400	0.0	-
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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

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}
}