cbpuschmann/klimacoder_heatpumps_v0.2

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:

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: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 128 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.'

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_v0.2")
# 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
supportive	371
opposed	352
neutral	363

Training Hyperparameters

• batch_size: (16, 16)
• num_epochs: (4, 4)
• 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: True

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0000	1	0.1692	-
0.0010	50	0.3092	-
0.0020	100	0.2943	-
0.0031	150	0.2981	-
0.0041	200	0.2829	-
0.0051	250	0.2572	-
0.0061	300	0.2392	-
0.0071	350	0.2174	-
0.0081	400	0.2034	-
0.0092	450	0.1872	-
0.0102	500	0.176	-
0.0112	550	0.1668	-
0.0122	600	0.1609	-
0.0132	650	0.141	-
0.0142	700	0.1303	-
0.0153	750	0.1272	-
0.0163	800	0.1176	-
0.0173	850	0.1114	-
0.0183	900	0.0987	-
0.0193	950	0.0965	-
0.0204	1000	0.0821	-
0.0214	1050	0.068	-
0.0224	1100	0.061	-
0.0234	1150	0.0507	-
0.0244	1200	0.0354	-
0.0254	1250	0.035	-
0.0265	1300	0.0274	-
0.0275	1350	0.024	-
0.0285	1400	0.0214	-
0.0295	1450	0.018	-
0.0305	1500	0.0137	-
0.0315	1550	0.0122	-
0.0326	1600	0.0105	-
0.0336	1650	0.0106	-
0.0346	1700	0.01	-
0.0356	1750	0.0076	-
0.0366	1800	0.0061	-
0.0377	1850	0.0061	-
0.0387	1900	0.0051	-
0.0397	1950	0.0038	-
0.0407	2000	0.004	-
0.0417	2050	0.0038	-
0.0427	2100	0.0034	-
0.0438	2150	0.0029	-
0.0448	2200	0.0025	-
0.0458	2250	0.0024	-
0.0468	2300	0.0021	-
0.0478	2350	0.0019	-
0.0488	2400	0.0017	-
0.0499	2450	0.0013	-
0.0509	2500	0.0011	-
0.0519	2550	0.0011	-
0.0529	2600	0.001	-
0.0539	2650	0.001	-
0.0550	2700	0.001	-
0.0560	2750	0.0007	-
0.0570	2800	0.0008	-
0.0580	2850	0.0006	-
0.0590	2900	0.0006	-
0.0600	2950	0.0006	-
0.0611	3000	0.0005	-
0.0621	3050	0.0006	-
0.0631	3100	0.0004	-
0.0641	3150	0.0003	-
0.0651	3200	0.0004	-
0.0661	3250	0.0005	-
0.0672	3300	0.0004	-
0.0682	3350	0.0003	-
0.0692	3400	0.0004	-
0.0702	3450	0.0002	-
0.0712	3500	0.0003	-
0.0723	3550	0.0002	-
0.0733	3600	0.0002	-
0.0743	3650	0.0002	-
0.0753	3700	0.0002	-
0.0763	3750	0.0003	-
0.0773	3800	0.0002	-
0.0784	3850	0.0002	-
0.0794	3900	0.0002	-
0.0804	3950	0.0001	-
0.0814	4000	0.0002	-
0.0824	4050	0.0001	-
0.0835	4100	0.0001	-
0.0845	4150	0.0001	-
0.0855	4200	0.0001	-
0.0865	4250	0.0001	-
0.0875	4300	0.0001	-
0.0885	4350	0.0001	-
0.0896	4400	0.0001	-
0.0906	4450	0.0001	-
0.0916	4500	0.0001	-
0.0926	4550	0.0001	-
0.0936	4600	0.0001	-
0.0946	4650	0.0001	-
0.0957	4700	0.0001	-
0.0967	4750	0.0001	-
0.0977	4800	0.0001	-
0.0987	4850	0.0001	-
0.0997	4900	0.0001	-
0.1008	4950	0.0001	-
0.1018	5000	0.0001	-
0.1028	5050	0.0001	-
0.1038	5100	0.0001	-
0.1048	5150	0.0001	-
0.1058	5200	0.0001	-
0.1069	5250	0.0001	-
0.1079	5300	0.0	-
0.1089	5350	0.0	-
0.1099	5400	0.0001	-
0.1109	5450	0.0	-
0.1119	5500	0.0001	-
0.1130	5550	0.0	-
0.1140	5600	0.0	-
0.1150	5650	0.0	-
0.1160	5700	0.0	-
0.1170	5750	0.0	-
0.1181	5800	0.0003	-
0.1191	5850	0.0006	-
0.1201	5900	0.0001	-
0.1211	5950	0.0	-
0.1221	6000	0.0	-
0.1231	6050	0.0	-
0.1242	6100	0.0	-
0.1252	6150	0.0	-
0.1262	6200	0.0	-
0.1272	6250	0.0	-
0.1282	6300	0.0	-
0.1292	6350	0.0	-
0.1303	6400	0.0	-
0.1313	6450	0.0	-
0.1323	6500	0.0	-
0.1333	6550	0.0	-
0.1343	6600	0.0	-
0.1354	6650	0.0	-
0.1364	6700	0.0	-
0.1374	6750	0.0	-
0.1384	6800	0.0	-
0.1394	6850	0.0	-
0.1404	6900	0.0	-
0.1415	6950	0.0	-
0.1425	7000	0.0003	-
0.1435	7050	0.0017	-
0.1445	7100	0.0011	-
0.1455	7150	0.0	-
0.1465	7200	0.0	-
0.1476	7250	0.0	-
0.1486	7300	0.0	-
0.1496	7350	0.0	-
0.1506	7400	0.0019	-
0.1516	7450	0.0001	-
0.1527	7500	0.0	-
0.1537	7550	0.0	-
0.1547	7600	0.0	-
0.1557	7650	0.0	-
0.1567	7700	0.0	-
0.1577	7750	0.0	-
0.1588	7800	0.0	-
0.1598	7850	0.0	-
0.1608	7900	0.0	-
0.1618	7950	0.0	-
0.1628	8000	0.0	-
0.1638	8050	0.0	-
0.1649	8100	0.0	-
0.1659	8150	0.0	-
0.1669	8200	0.0	-
0.1679	8250	0.0	-
0.1689	8300	0.0	-
0.1700	8350	0.0	-
0.1710	8400	0.0	-
0.1720	8450	0.0	-
0.1730	8500	0.0	-
0.1740	8550	0.0	-
0.1750	8600	0.0	-
0.1761	8650	0.0	-
0.1771	8700	0.0	-
0.1781	8750	0.0	-
0.1791	8800	0.0	-
0.1801	8850	0.0	-
0.1811	8900	0.0	-
0.1822	8950	0.0	-
0.1832	9000	0.0	-
0.1842	9050	0.0	-
0.1852	9100	0.0	-
0.1862	9150	0.0	-
0.1873	9200	0.0	-
0.1883	9250	0.0	-
0.1893	9300	0.0	-
0.1903	9350	0.0	-
0.1913	9400	0.0	-
0.1923	9450	0.0	-
0.1934	9500	0.0	-
0.1944	9550	0.0	-
0.1954	9600	0.0	-
0.1964	9650	0.0	-
0.1974	9700	0.0	-
0.1984	9750	0.0	-
0.1995	9800	0.0	-
0.2005	9850	0.0	-
0.2015	9900	0.0	-
0.2025	9950	0.0	-
0.2035	10000	0.0	-
0.2046	10050	0.0	-
0.2056	10100	0.0	-
0.2066	10150	0.0	-
0.2076	10200	0.0	-
0.2086	10250	0.0	-
0.2096	10300	0.0	-
0.2107	10350	0.0	-
0.2117	10400	0.0	-
0.2127	10450	0.0	-
0.2137	10500	0.0	-
0.2147	10550	0.0	-
0.2157	10600	0.0	-
0.2168	10650	0.0	-
0.2178	10700	0.0	-
0.2188	10750	0.0	-
0.2198	10800	0.0	-
0.2208	10850	0.0	-
0.2219	10900	0.0	-
0.2229	10950	0.0	-
0.2239	11000	0.0	-
0.2249	11050	0.0	-
0.2259	11100	0.0	-
0.2269	11150	0.0	-
0.2280	11200	0.0	-
0.2290	11250	0.0	-
0.2300	11300	0.0	-
0.2310	11350	0.0	-
0.2320	11400	0.0	-
0.2331	11450	0.0	-
0.2341	11500	0.0	-
0.2351	11550	0.0	-
0.2361	11600	0.0	-
0.2371	11650	0.0	-
0.2381	11700	0.0	-
0.2392	11750	0.0	-
0.2402	11800	0.0	-
0.2412	11850	0.0	-
0.2422	11900	0.0	-
0.2432	11950	0.0	-
0.2442	12000	0.0	-
0.2453	12050	0.0	-
0.2463	12100	0.0	-
0.2473	12150	0.0057	-
0.2483	12200	0.0035	-
0.2493	12250	0.0001	-
0.2504	12300	0.0	-
0.2514	12350	0.0	-
0.2524	12400	0.0	-
0.2534	12450	0.0	-
0.2544	12500	0.0	-
0.2554	12550	0.0	-
0.2565	12600	0.0	-
0.2575	12650	0.0	-
0.2585	12700	0.0	-
0.2595	12750	0.0	-
0.2605	12800	0.0	-
0.2615	12850	0.0	-
0.2626	12900	0.0	-
0.2636	12950	0.0	-
0.2646	13000	0.0	-
0.2656	13050	0.0	-
0.2666	13100	0.0	-
0.2677	13150	0.0	-
0.2687	13200	0.0	-
0.2697	13250	0.0	-
0.2707	13300	0.0	-
0.2717	13350	0.0	-
0.2727	13400	0.0	-
0.2738	13450	0.0	-
0.2748	13500	0.0	-
0.2758	13550	0.0	-
0.2768	13600	0.0	-
0.2778	13650	0.0	-
0.2788	13700	0.0	-
0.2799	13750	0.0	-
0.2809	13800	0.0	-
0.2819	13850	0.0	-
0.2829	13900	0.0	-
0.2839	13950	0.0	-
0.2850	14000	0.0	-
0.2860	14050	0.0	-
0.2870	14100	0.0	-
0.2880	14150	0.0	-
0.2890	14200	0.0	-
0.2900	14250	0.0	-
0.2911	14300	0.0	-
0.2921	14350	0.0	-
0.2931	14400	0.0	-
0.2941	14450	0.0	-
0.2951	14500	0.0	-
0.2961	14550	0.0	-
0.2972	14600	0.0	-
0.2982	14650	0.0	-
0.2992	14700	0.0	-
0.3002	14750	0.0	-
0.3012	14800	0.0	-
0.3023	14850	0.0	-
0.3033	14900	0.0	-
0.3043	14950	0.0	-
0.3053	15000	0.0	-
0.3063	15050	0.0	-
0.3073	15100	0.0	-
0.3084	15150	0.0	-
0.3094	15200	0.0	-
0.3104	15250	0.0	-
0.3114	15300	0.0	-
0.3124	15350	0.0	-
0.3134	15400	0.0	-
0.3145	15450	0.0	-
0.3155	15500	0.0	-
0.3165	15550	0.0	-
0.3175	15600	0.0	-
0.3185	15650	0.0	-
0.3196	15700	0.0	-
0.3206	15750	0.0	-
0.3216	15800	0.0	-
0.3226	15850	0.0	-
0.3236	15900	0.0	-
0.3246	15950	0.0	-
0.3257	16000	0.0	-
0.3267	16050	0.0	-
0.3277	16100	0.0	-
0.3287	16150	0.0	-
0.3297	16200	0.0	-
0.3307	16250	0.0	-
0.3318	16300	0.0	-
0.3328	16350	0.0	-
0.3338	16400	0.0	-
0.3348	16450	0.0	-
0.3358	16500	0.0	-
0.3369	16550	0.0	-
0.3379	16600	0.0	-
0.3389	16650	0.0	-
0.3399	16700	0.0	-
0.3409	16750	0.0	-
0.3419	16800	0.0	-
0.3430	16850	0.0	-
0.3440	16900	0.0	-
0.3450	16950	0.0	-
0.3460	17000	0.0	-
0.3470	17050	0.0	-
0.3480	17100	0.0	-
0.3491	17150	0.0	-
0.3501	17200	0.0	-
0.3511	17250	0.0	-
0.3521	17300	0.0	-
0.3531	17350	0.0	-
0.3542	17400	0.0	-
0.3552	17450	0.0	-
0.3562	17500	0.0	-
0.3572	17550	0.0	-
0.3582	17600	0.0	-
0.3592	17650	0.0	-
0.3603	17700	0.0	-
0.3613	17750	0.0	-
0.3623	17800	0.0	-
0.3633	17850	0.0	-
0.3643	17900	0.0	-
0.3653	17950	0.0	-
0.3664	18000	0.0	-
0.3674	18050	0.0	-
0.3684	18100	0.0	-
0.3694	18150	0.0	-
0.3704	18200	0.0	-
0.3715	18250	0.0	-
0.3725	18300	0.0	-
0.3735	18350	0.0	-
0.3745	18400	0.0	-
0.3755	18450	0.0	-
0.3765	18500	0.0	-
0.3776	18550	0.0	-
0.3786	18600	0.0	-
0.3796	18650	0.0	-
0.3806	18700	0.0	-
0.3816	18750	0.0	-
0.3827	18800	0.0	-
0.3837	18850	0.0	-
0.3847	18900	0.0	-
0.3857	18950	0.0	-
0.3867	19000	0.0	-
0.3877	19050	0.0	-
0.3888	19100	0.0	-
0.3898	19150	0.0	-
0.3908	19200	0.0	-
0.3918	19250	0.0	-
0.3928	19300	0.0	-
0.3938	19350	0.0	-
0.3949	19400	0.0	-
0.3959	19450	0.0	-
0.3969	19500	0.0001	-
0.3979	19550	0.0	-
0.3989	19600	0.0012	-
0.4000	19650	0.0055	-
0.4010	19700	0.0069	-
0.4020	19750	0.0064	-
0.4030	19800	0.0043	-
0.4040	19850	0.0001	-
0.4050	19900	0.0	-
0.4061	19950	0.0	-
0.4071	20000	0.0	-
0.4081	20050	0.0	-
0.4091	20100	0.0	-
0.4101	20150	0.0	-
0.4111	20200	0.0	-
0.4122	20250	0.0	-
0.4132	20300	0.0	-
0.4142	20350	0.0	-
0.4152	20400	0.0	-
0.4162	20450	0.0	-
0.4173	20500	0.0	-
0.4183	20550	0.0	-
0.4193	20600	0.0	-
0.4203	20650	0.0	-
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4.0	196524	-	0.3929

Framework Versions

• Python: 3.11.10
• SetFit: 1.1.1
• Sentence Transformers: 3.4.0
• Transformers: 4.47.1
• PyTorch: 2.3.1.post300
• 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}
}