cbpuschmann/klimacoder_heatpumps_v0.1

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:

1. Fine-tuning a Sentence Transformer 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: 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	'Die geplante flächendeckende Einführung von Wärmepumpen durch das neue Heizungsgesetz stößt auf erhebliche Skepsis, da die Kosten für Hausbesitzer in die Höhe schnellen könnten und die technische Umsetzbarkeit in vielen Altbauten fraglich bleibt. Zudem wird die Frage aufgeworfen, ob der massive staatliche Eingriff in den Heizungsmarkt letztlich zu einer unnötigen Belastung der Bürger führt, ohne einen nennenswerten Effekt auf den globalen CO₂-Ausstoß zu haben.' 'Aufgewacht aus dem Heizungstraum: Trotz geschärfter Gesetzesinitiativen regt sich massiver Skeptizismus gegen die flächendeckende Einführung von Wärmepumpen! Koste es, was es wolle, bevor blind drauflos gepumpt wird, müssen realistische Lösungen her – alles andere heizt nur Ärger auf!' 'Wärmepumpen-Wahnsinn auf dem Vormarsch: Die geplante Gesetzesinitiative zur flächendeckenden Einführung von Wärmepumpen sorgt für erhitzte Gemüter und Skepsis in der Bevölkerung. Kritiker warnen vor explodierenden Kosten und unzureichender Infrastruktur, während die Regierung weiterhin auf stur schaltet.'
neutral	'Die Bundesregierung plant, den Einbau neuer Gas- und Ölheizungen künftig nur noch in Kombination mit Wärmepumpen zu genehmigen. Bevor das Heizungsverbot in Kraft tritt, müssen Städte und Kommunen zunächst einen Wärmeplan vorlegen, was in Berlin frühestens 2026 erwartet wird.' 'Die Bundesregierung plant die Einführung eines sogenannten "Heizungsgesetzes", das den Einbau neuer Gas- und Ölheizungen nur noch in Kombination mit Wärmepumpen erlauben soll. Die Umsetzung dieses Gesetzes soll jedoch erst nach Vorlage eines "Wärmeplans" durch die jeweiligen Städte und Kommunen erfolgen, wodurch eine aufschiebende Wirkung entsteht. In einigen Städten, wie Berlin, könnte das Verbot für den Einbau von Gas- und Ölheizungen ohne Wärmepumpen daher erst 2026 in Kraft treten.' 'In einer jüngsten Gesetzesinitiative wird der Einbau neuer Gas- und Ölheizungen künftig nur noch genehmigt, wenn sie mit Wärmepumpen kombiniert werden. Die Umsetzung des sogenannten "Heizungsgesetzes" soll eine aufschiebende Wirkung haben: Zunächst müssen Städte und Kommunen einen "Wärmeplan" vorlegen, bevor das Heizungsverbot in Kraft tritt. In Berlin könnte dies frühestens im Jahr 2026 der Fall sein.'
supportive	'Die jüngsten Gesetzesinitiativen zur Einführung von Wärmepumpen als Standardheizung haben die Gemüter erhitzt. Kritiker monieren hohe Anschaffungskosten und technische Herausforderungen, während Befürworter den positiven Beitrag dieser Technologie zur Reduzierung der CO2-Emissionen betonen. Angesichts des Klimawandels bleibt die Frage offen, ob diese Maßnahmen ausreichen oder sogar übertrieben sind; sie bilden jedoch einen entscheidenden Schritt in Richtung einer nachhaltigeren Energiepolitik.' 'Die geplante Einführung der Wärmepumpen als Teil des Heizungsgesetzes stößt auf Kritik, insbesondere hinsichtlich der finanziellen Belastung für Eigenheimbesitzer und der technischen Umsetzbarkeit in Altbauten. Dennoch setzt das Vorhaben ein ermutigendes Signal für den Klimaschutz und die notwendige Transformation des Energiesektors hin zu umweltfreundlicheren Technologien.' 'Die geplante flächendeckende Einführung von Wärmepumpen durch das neue Heizungsgesetz stößt auf gemischte Reaktionen: Während Kritiker die hohen Anfangsinvestitionen und die technische Umsetzbarkeit in Frage stellen, wird das Gesetz von vielen als notwendiger Schritt hin zu nachhaltigeren und langfristig kostengünstigeren Heizlösungen begrüßt. Besonders in Regionen, in denen Heizsysteme ohnehin erneuert werden müssen, könnte die Initiative einen wichtigen Anstoß für den Umstieg auf umweltfreundlichere Technologien geben.'

Evaluation

Metrics

Label	Accuracy
all	0.6119

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_heatpumps_v0.1")
# Run inference
preds = model("14. Juli 2022: Ein Dialogversuch Mitten in der Sommerpause veröffentlichen die beiden für das Heizungsgesetz zuständigen Ministerien, das  Bundeswirtschaftsministerium")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	27	60.7919	195

Label	Training Sample Count
neutral	363
opposed	352
supportive	371

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	0.1865	-
0.0020	50	0.2414	-
0.0041	100	0.2266	-
0.0061	150	0.2097	-
0.0081	200	0.1931	-
0.0102	250	0.1684	-
0.0122	300	0.1417	-
0.0142	350	0.0991	-
0.0163	400	0.0684	-
0.0183	450	0.0349	-
0.0204	500	0.023	-
0.0224	550	0.0137	-
0.0244	600	0.0091	-
0.0265	650	0.0066	-
0.0285	700	0.0046	-
0.0305	750	0.0031	-
0.0326	800	0.0024	-
0.0346	850	0.002	-
0.0366	900	0.0014	-
0.0387	950	0.0013	-
0.0407	1000	0.001	-
0.0427	1050	0.0008	-
0.0448	1100	0.0008	-
0.0468	1150	0.0006	-
0.0488	1200	0.0005	-
0.0509	1250	0.0005	-
0.0529	1300	0.0004	-
0.0550	1350	0.0005	-
0.0570	1400	0.0003	-
0.0590	1450	0.0003	-
0.0611	1500	0.0003	-
0.0631	1550	0.0002	-
0.0651	1600	0.0002	-
0.0672	1650	0.0002	-
0.0692	1700	0.0002	-
0.0712	1750	0.0001	-
0.0733	1800	0.0001	-
0.0753	1850	0.0002	-
0.0773	1900	0.0003	-
0.0794	1950	0.0001	-
0.0814	2000	0.0001	-
0.0834	2050	0.0001	-
0.0855	2100	0.0001	-
0.0875	2150	0.0001	-
0.0896	2200	0.0001	-
0.0916	2250	0.0001	-
0.0936	2300	0.0001	-
0.0957	2350	0.0001	-
0.0977	2400	0.0001	-
0.0997	2450	0.0001	-
0.1018	2500	0.0	-
0.1038	2550	0.0	-
0.1058	2600	0.0	-
0.1079	2650	0.0	-
0.1099	2700	0.0	-
0.1119	2750	0.0	-
0.1140	2800	0.0	-
0.1160	2850	0.0	-
0.1180	2900	0.0	-
0.1201	2950	0.0	-
0.1221	3000	0.0	-
0.1242	3050	0.0	-
0.1262	3100	0.0	-
0.1282	3150	0.0	-
0.1303	3200	0.0	-
0.1323	3250	0.0	-
0.1343	3300	0.0	-
0.1364	3350	0.0	-
0.1384	3400	0.0	-
0.1404	3450	0.0	-
0.1425	3500	0.0	-
0.1445	3550	0.0	-
0.1465	3600	0.0	-
0.1486	3650	0.0	-
0.1506	3700	0.0	-
0.1527	3750	0.0	-
0.1547	3800	0.0	-
0.1567	3850	0.0	-
0.1588	3900	0.0	-
0.1608	3950	0.0	-
0.1628	4000	0.0	-
0.1649	4050	0.0	-
0.1669	4100	0.0	-
0.1689	4150	0.0	-
0.1710	4200	0.0	-
0.1730	4250	0.0	-
0.1750	4300	0.0	-
0.1771	4350	0.0	-
0.1791	4400	0.0	-
0.1811	4450	0.0	-
0.1832	4500	0.0	-
0.1852	4550	0.0	-
0.1873	4600	0.0	-
0.1893	4650	0.0	-
0.1913	4700	0.0	-
0.1934	4750	0.0	-
0.1954	4800	0.0	-
0.1974	4850	0.0	-
0.1995	4900	0.0	-
0.2015	4950	0.0	-
0.2035	5000	0.0	-
0.2056	5050	0.0	-
0.2076	5100	0.0	-
0.2096	5150	0.0	-
0.2117	5200	0.0	-
0.2137	5250	0.0	-
0.2157	5300	0.0	-
0.2178	5350	0.0	-
0.2198	5400	0.0	-
0.2219	5450	0.0	-
0.2239	5500	0.0	-
0.2259	5550	0.0	-
0.2280	5600	0.0	-
0.2300	5650	0.0	-
0.2320	5700	0.0	-
0.2341	5750	0.0	-
0.2361	5800	0.0	-
0.2381	5850	0.0	-
0.2402	5900	0.0	-
0.2422	5950	0.0	-
0.2442	6000	0.0	-
0.2463	6050	0.0	-
0.2483	6100	0.0	-
0.2503	6150	0.0	-
0.2524	6200	0.0	-
0.2544	6250	0.0	-
0.2565	6300	0.0	-
0.2585	6350	0.0	-
0.2605	6400	0.0	-
0.2626	6450	0.0	-
0.2646	6500	0.0	-
0.2666	6550	0.0	-
0.2687	6600	0.0	-
0.2707	6650	0.0	-
0.2727	6700	0.0	-
0.2748	6750	0.0	-
0.2768	6800	0.0	-
0.2788	6850	0.0	-
0.2809	6900	0.0	-
0.2829	6950	0.0	-
0.2849	7000	0.0	-
0.2870	7050	0.0	-
0.2890	7100	0.0	-
0.2911	7150	0.0	-
0.2931	7200	0.0	-
0.2951	7250	0.0001	-
0.2972	7300	0.0	-
0.2992	7350	0.0	-
0.3012	7400	0.0	-
0.3033	7450	0.0	-
0.3053	7500	0.0	-
0.3073	7550	0.0	-
0.3094	7600	0.0	-
0.3114	7650	0.0	-
0.3134	7700	0.0	-
0.3155	7750	0.0	-
0.3175	7800	0.0	-
0.3195	7850	0.0	-
0.3216	7900	0.0	-
0.3236	7950	0.0	-
0.3257	8000	0.0	-
0.3277	8050	0.0	-
0.3297	8100	0.0	-
0.3318	8150	0.0	-
0.3338	8200	0.0	-
0.3358	8250	0.0	-
0.3379	8300	0.0	-
0.3399	8350	0.0	-
0.3419	8400	0.0	-
0.3440	8450	0.0	-
0.3460	8500	0.0	-
0.3480	8550	0.0	-
0.3501	8600	0.0	-
0.3521	8650	0.0	-
0.3541	8700	0.0	-
0.3562	8750	0.0	-
0.3582	8800	0.0	-
0.3603	8850	0.0	-
0.3623	8900	0.0	-
0.3643	8950	0.0	-
0.3664	9000	0.0	-
0.3684	9050	0.0	-
0.3704	9100	0.0	-
0.3725	9150	0.0	-
0.3745	9200	0.0	-
0.3765	9250	0.0	-
0.3786	9300	0.0	-
0.3806	9350	0.0	-
0.3826	9400	0.0	-
0.3847	9450	0.0	-
0.3867	9500	0.0	-
0.3887	9550	0.0	-
0.3908	9600	0.0	-
0.3928	9650	0.0	-
0.3949	9700	0.0	-
0.3969	9750	0.0	-
0.3989	9800	0.0	-
0.4010	9850	0.0	-
0.4030	9900	0.0	-
0.4050	9950	0.0	-
0.4071	10000	0.0	-
0.4091	10050	0.0	-
0.4111	10100	0.0	-
0.4132	10150	0.0	-
0.4152	10200	0.0	-
0.4172	10250	0.0	-
0.4193	10300	0.0	-
0.4213	10350	0.0	-
0.4233	10400	0.0	-
0.4254	10450	0.0	-
0.4274	10500	0.0	-
0.4295	10550	0.0	-
0.4315	10600	0.0	-
0.4335	10650	0.0	-
0.4356	10700	0.0	-
0.4376	10750	0.0	-
0.4396	10800	0.0	-
0.4417	10850	0.0	-
0.4437	10900	0.0	-
0.4457	10950	0.0	-
0.4478	11000	0.0	-
0.4498	11050	0.0	-
0.4518	11100	0.0	-
0.4539	11150	0.0	-
0.4559	11200	0.0	-
0.4580	11250	0.0	-
0.4600	11300	0.0	-
0.4620	11350	0.0	-
0.4641	11400	0.0	-
0.4661	11450	0.0	-
0.4681	11500	0.0	-
0.4702	11550	0.0	-
0.4722	11600	0.0	-
0.4742	11650	0.0	-
0.4763	11700	0.0	-
0.4783	11750	0.0	-
0.4803	11800	0.0	-
0.4824	11850	0.0	-
0.4844	11900	0.0	-
0.4864	11950	0.0	-
0.4885	12000	0.0	-
0.4905	12050	0.0	-
0.4926	12100	0.0	-
0.4946	12150	0.0	-
0.4966	12200	0.0	-
0.4987	12250	0.0	-
0.5007	12300	0.0	-
0.5027	12350	0.0	-
0.5048	12400	0.0	-
0.5068	12450	0.0	-
0.5088	12500	0.0	-
0.5109	12550	0.0	-
0.5129	12600	0.0	-
0.5149	12650	0.0	-
0.5170	12700	0.0	-
0.5190	12750	0.0	-
0.5210	12800	0.0	-
0.5231	12850	0.0	-
0.5251	12900	0.0	-
0.5272	12950	0.0	-
0.5292	13000	0.0	-
0.5312	13050	0.0	-
0.5333	13100	0.0	-
0.5353	13150	0.0006	-
0.5373	13200	0.211	-
0.5394	13250	0.0774	-
0.5414	13300	0.0171	-
0.5434	13350	0.0052	-
0.5455	13400	0.0036	-
0.5475	13450	0.0006	-
0.5495	13500	0.0003	-
0.5516	13550	0.0007	-
0.5536	13600	0.0002	-
0.5556	13650	0.0002	-
0.5577	13700	0.0001	-
0.5597	13750	0.0001	-
0.5618	13800	0.0001	-
0.5638	13850	0.0001	-
0.5658	13900	0.0001	-
0.5679	13950	0.0	-
0.5699	14000	0.0	-
0.5719	14050	0.0	-
0.5740	14100	0.0	-
0.5760	14150	0.0	-
0.5780	14200	0.0	-
0.5801	14250	0.0	-
0.5821	14300	0.0	-
0.5841	14350	0.0	-
0.5862	14400	0.0	-
0.5882	14450	0.0	-
0.5902	14500	0.0	-
0.5923	14550	0.0	-
0.5943	14600	0.0	-
0.5964	14650	0.0	-
0.5984	14700	0.0	-
0.6004	14750	0.0	-
0.6025	14800	0.0	-
0.6045	14850	0.0	-
0.6065	14900	0.0002	-
0.6086	14950	0.0004	-
0.6106	15000	0.0	-
0.6126	15050	0.0	-
0.6147	15100	0.0	-
0.6167	15150	0.0	-
0.6187	15200	0.0	-
0.6208	15250	0.0	-
0.6228	15300	0.0	-
0.6248	15350	0.0	-
0.6269	15400	0.0	-
0.6289	15450	0.0	-
0.6310	15500	0.0	-
0.6330	15550	0.0	-
0.6350	15600	0.0	-
0.6371	15650	0.0	-
0.6391	15700	0.0	-
0.6411	15750	0.0	-
0.6432	15800	0.0	-
0.6452	15850	0.0	-
0.6472	15900	0.0	-
0.6493	15950	0.0	-
0.6513	16000	0.0	-
0.6533	16050	0.0	-
0.6554	16100	0.0	-
0.6574	16150	0.0	-
0.6594	16200	0.0	-
0.6615	16250	0.0	-
0.6635	16300	0.0	-
0.6656	16350	0.0	-
0.6676	16400	0.0	-
0.6696	16450	0.0	-
0.6717	16500	0.0	-
0.6737	16550	0.0	-
0.6757	16600	0.0	-
0.6778	16650	0.0	-
0.6798	16700	0.0	-
0.6818	16750	0.0	-
0.6839	16800	0.0	-
0.6859	16850	0.0	-
0.6879	16900	0.0	-
0.6900	16950	0.0	-
0.6920	17000	0.0	-
0.6940	17050	0.0	-
0.6961	17100	0.0	-
0.6981	17150	0.0	-
0.7002	17200	0.0	-
0.7022	17250	0.0	-
0.7042	17300	0.0	-
0.7063	17350	0.0	-
0.7083	17400	0.0	-
0.7103	17450	0.0	-
0.7124	17500	0.0	-
0.7144	17550	0.0	-
0.7164	17600	0.0	-
0.7185	17650	0.0	-
0.7205	17700	0.0	-
0.7225	17750	0.0	-
0.7246	17800	0.0	-
0.7266	17850	0.0	-
0.7286	17900	0.0	-
0.7307	17950	0.0	-
0.7327	18000	0.0	-
0.7348	18050	0.0	-
0.7368	18100	0.0	-
0.7388	18150	0.0	-
0.7409	18200	0.0	-
0.7429	18250	0.0	-
0.7449	18300	0.0	-
0.7470	18350	0.0	-
0.7490	18400	0.0	-
0.7510	18450	0.0	-
0.7531	18500	0.0	-
0.7551	18550	0.0	-
0.7571	18600	0.0	-
0.7592	18650	0.0	-
0.7612	18700	0.0	-
0.7633	18750	0.0	-
0.7653	18800	0.0	-
0.7673	18850	0.0	-
0.7694	18900	0.0	-
0.7714	18950	0.0	-
0.7734	19000	0.0	-
0.7755	19050	0.0	-
0.7775	19100	0.0	-
0.7795	19150	0.0	-
0.7816	19200	0.0	-
0.7836	19250	0.0	-
0.7856	19300	0.0	-
0.7877	19350	0.0	-
0.7897	19400	0.0	-
0.7917	19450	0.0	-
0.7938	19500	0.0	-
0.7958	19550	0.0	-
0.7979	19600	0.0	-
0.7999	19650	0.0	-
0.8019	19700	0.0	-
0.8040	19750	0.0	-
0.8060	19800	0.0	-
0.8080	19850	0.0	-
0.8101	19900	0.0	-
0.8121	19950	0.0	-
0.8141	20000	0.0	-
0.8162	20050	0.0	-
0.8182	20100	0.0	-
0.8202	20150	0.0	-
0.8223	20200	0.0	-
0.8243	20250	0.0	-
0.8263	20300	0.0	-
0.8284	20350	0.0	-
0.8304	20400	0.0	-
0.8325	20450	0.0	-
0.8345	20500	0.0	-
0.8365	20550	0.0	-
0.8386	20600	0.0	-
0.8406	20650	0.0	-
0.8426	20700	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

@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}
}