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cbpuschmann/
paraphrase-multilingual-minilm-klimacoder_v0.10

Name: paraphrase-multilingual-minilm-klimacoder_v0.10
Availability: InStock

setfit

safetensors

bert

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

finetune

model-index

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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:

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: 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
supportive	"„Junge Menschen fordern Aktion, um den Klimawandel zu stoppen. Unter dem Motto 'Die Zukunft ist unsere' haben sie sich zu Aktivisten für eine nachhaltige Zukunft entwickelt und mit Massenprotesten auf die dringende Notwendigkeit von Klimaschutzmaßnahmen aufmerksam gemacht.“" 'Die flächendeckende Einführung von Wärmepumpen, wie sie im neuen Heizungsgesetz vorgesehen ist, könnte einen bedeutenden Schritt hin zu einer nachhaltigeren Energieversorgung darstellen. Durch die Förderung dieser umweltfreundlichen Technologie könnte nicht nur der CO2-Ausstoß erheblich reduziert werden, sondern auch die Abhängigkeit von fossilen Brennstoffen langfristig sinken.' 'In den letzten Jahren haben Klima-Aktivismus-Gruppen wie Fridays for Future und die Letzte Generation durch ihr Engagement und ihre Beharrlichkeit das Bewusstsein für die dringende Notwendigkeit von Klimaschutzmaßnahmen geschärft. Ihre Aktionen haben es geschafft, das Thema Klimawandel in den Mittelpunkt der gesellschaftlichen und politischen Debatte zu rücken, was langfristig zu einer stärkeren Auseinandersetzung mit umweltpolitischen Herausforderungen führen könnte.'
neutral	'Die Debatte um die Einführung eines nationalen Tempolimits auf Autobahnen bleibt ein kontroverses Thema in Deutschland. Befürworter argumentieren mit Vorteilen für die Verkehrssicherheit und den Umweltschutz, während Gegner mögliche Einschränkungen der individuellen Freiheit und wirtschaftliche Auswirkungen betonen. Der Gesetzgebungsprozess zu diesem Thema wird weiterhin aufmerksam verfolgt.' 'Das Bundeskabinett hat den Entwurf eines Heizungsgesetzes beschlossen, das die flächendeckende Einführung von Wärmepumpen in Deutschland vorsieht. Demnach soll der Einsatz erneuerbarer Wärmequellen in Gebäuden gefördert werden. Der Gesetzentwurf wird nun dem Bundestag vorgelegt, wo er behandelt und abgestimmt werden muss.' 'Die Bundesregierung hat ein Gesetz zur Förderung der flächendeckenden Einführung von Wärmepumpen verabschiedet, das den Einsatz erneuerbarer Energien im Heizungssektor vorantreiben soll. Kritiker bemängeln die Umsetzbarkeit und finanzielle Belastung für Hausbesitzer, während Befürworter die Maßnahme als wichtigen Schritt zur Erreichung der Klimaziele betrachten.'
opposed	'Die Straßen blockiert, der Alltag gestört – Klima-Aktivisten wie die Letzte Generation und Fridays for Future sorgen mit ihren Aktionen immer wieder für Chaos und Unmut. Während sie ihre Botschaft lautstark verkünden, fragen sich viele: Ist das der richtige Weg, um echte Veränderungen zu erreichen, oder treibt das nur einen Keil zwischen die Menschen?' 'Die grüne Verbotskultur schlägt wieder zu: Mit der Einführung eines nationalen Tempolimits auf Autobahnen wird einmal mehr die Freiheit der Autofahrer beschnitten. Statt auf Eigenverantwortung zu setzen, wird der mündige Bürger bevormundet und der deutschen Wirtschaft ein weiterer Stein in den Weg gelegt.' 'Die selbsternannten Klima-Retter von Fridays for Future und der Letzten Generation scheinen mehr daran interessiert zu sein, den Alltag der Bürger mit ihren fragwürdigen Aktionen zu stören, als tatsächlich sinnvolle Lösungen für den Klimawandel zu präsentieren. Während sie Straßen blockieren und Chaos verursachen, bleibt die Frage offen, ob ihre Methoden mehr Schaden anrichten als Nutzen bringen.'

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.

python

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("cbpuschmann/paraphrase-multilingual-minilm-klimacoder_v0.10")
# Run inference
preds = model("\"Das Heizungsgesetz: Eine teure, ineffiziente und überbordete Lösung für unsere Energieprobleme? Die geplanten Wärmepumpen in jedem Haus wirken sich negativ auf die Umwelt aus und werden wahrscheinlich Millionen von Steuergeldern verschlingen. Wir brauchen eine realistische Energiewende, nicht ein teures Experiment.\"")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	24	44.1537	73

Label	Training Sample Count
neutral	500
opposed	549
supportive	526

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	0.2419	-
0.0010	50	0.2541	-
0.0019	100	0.2489	-
0.0029	150	0.2404	-
0.0039	200	0.2281	-
0.0048	250	0.2168	-
0.0058	300	0.193	-
0.0068	350	0.1604	-
0.0077	400	0.1304	-
0.0087	450	0.1218	-
0.0097	500	0.1046	-
0.0107	550	0.0978	-
0.0116	600	0.0733	-
0.0126	650	0.061	-
0.0136	700	0.0496	-
0.0145	750	0.0397	-
0.0155	800	0.0331	-
0.0165	850	0.0329	-
0.0174	900	0.0254	-
0.0184	950	0.0194	-
0.0194	1000	0.0154	-
0.0203	1050	0.0111	-
0.0213	1100	0.0112	-
0.0223	1150	0.0107	-
0.0232	1200	0.0065	-
0.0242	1250	0.0046	-
0.0252	1300	0.0059	-
0.0261	1350	0.0033	-
0.0271	1400	0.003	-
0.0281	1450	0.0024	-
0.0290	1500	0.0018	-
0.0300	1550	0.001	-
0.0310	1600	0.0011	-
0.0320	1650	0.0012	-
0.0329	1700	0.0007	-
0.0339	1750	0.0007	-
0.0349	1800	0.0005	-
0.0358	1850	0.0004	-
0.0368	1900	0.0003	-
0.0378	1950	0.0006	-
0.0387	2000	0.0004	-
0.0397	2050	0.0003	-
0.0407	2100	0.0002	-
0.0416	2150	0.0003	-
0.0426	2200	0.0005	-
0.0436	2250	0.0005	-
0.0445	2300	0.0001	-
0.0455	2350	0.0003	-
0.0465	2400	0.0003	-
0.0474	2450	0.0002	-
0.0484	2500	0.0003	-
0.0494	2550	0.0001	-
0.0503	2600	0.0002	-
0.0513	2650	0.0003	-
0.0523	2700	0.0004	-
0.0533	2750	0.0007	-
0.0542	2800	0.0001	-
0.0552	2850	0.0002	-
0.0562	2900	0.0001	-
0.0571	2950	0.0001	-
0.0581	3000	0.0001	-
0.0591	3050	0.0001	-
0.0600	3100	0.0001	-
0.0610	3150	0.0	-
0.0620	3200	0.0	-
0.0629	3250	0.0	-
0.0639	3300	0.0001	-
0.0649	3350	0.0006	-
0.0658	3400	0.0	-
0.0668	3450	0.0	-
0.0678	3500	0.0001	-
0.0687	3550	0.0	-
0.0697	3600	0.0	-
0.0707	3650	0.0001	-
0.0716	3700	0.0001	-
0.0726	3750	0.0	-
0.0736	3800	0.0	-
0.0746	3850	0.0	-
0.0755	3900	0.0	-
0.0765	3950	0.0	-
0.0775	4000	0.0	-
0.0784	4050	0.0	-
0.0794	4100	0.0	-
0.0804	4150	0.0	-
0.0813	4200	0.0	-
0.0823	4250	0.0	-
0.0833	4300	0.0	-
0.0842	4350	0.0027	-
0.0852	4400	0.0021	-
0.0862	4450	0.0013	-
0.0871	4500	0.0022	-
0.0881	4550	0.004	-
0.0891	4600	0.0017	-
0.0900	4650	0.0054	-
0.0910	4700	0.0019	-
0.0920	4750	0.0009	-
0.0929	4800	0.0001	-
0.0939	4850	0.0	-
0.0949	4900	0.0	-
0.0959	4950	0.0	-
0.0968	5000	0.0	-
0.0978	5050	0.0	-
0.0988	5100	0.0	-
0.0997	5150	0.0	-
0.1007	5200	0.0	-
0.1017	5250	0.0	-
0.1026	5300	0.0	-
0.1036	5350	0.0	-
0.1046	5400	0.0	-
0.1055	5450	0.0	-
0.1065	5500	0.0	-
0.1075	5550	0.0	-
0.1084	5600	0.0	-
0.1094	5650	0.0	-
0.1104	5700	0.0	-
0.1113	5750	0.0	-
0.1123	5800	0.0	-
0.1133	5850	0.0	-
0.1142	5900	0.0	-
0.1152	5950	0.0	-
0.1162	6000	0.0	-
0.1172	6050	0.0	-
0.1181	6100	0.0	-
0.1191	6150	0.0	-
0.1201	6200	0.0	-
0.1210	6250	0.0	-
0.1220	6300	0.0	-
0.1230	6350	0.0	-
0.1239	6400	0.0	-
0.1249	6450	0.0	-
0.1259	6500	0.0	-
0.1268	6550	0.0	-
0.1278	6600	0.0	-
0.1288	6650	0.0	-
0.1297	6700	0.0	-
0.1307	6750	0.0	-
0.1317	6800	0.0	-
0.1326	6850	0.0	-
0.1336	6900	0.0	-
0.1346	6950	0.0	-
0.1355	7000	0.0	-
0.1365	7050	0.0	-
0.1375	7100	0.0	-
0.1385	7150	0.0	-
0.1394	7200	0.0	-
0.1404	7250	0.0	-
0.1414	7300	0.0	-
0.1423	7350	0.0	-
0.1433	7400	0.0	-
0.1443	7450	0.0	-
0.1452	7500	0.0	-
0.1462	7550	0.0	-
0.1472	7600	0.0	-
0.1481	7650	0.0	-
0.1491	7700	0.0	-
0.1501	7750	0.0	-
0.1510	7800	0.0	-
0.1520	7850	0.0	-
0.1530	7900	0.0	-
0.1539	7950	0.0	-
0.1549	8000	0.0	-
0.1559	8050	0.0	-
0.1568	8100	0.0	-
0.1578	8150	0.0	-
0.1588	8200	0.0	-
0.1598	8250	0.0	-
0.1607	8300	0.0	-
0.1617	8350	0.0	-
0.1627	8400	0.0	-
0.1636	8450	0.0	-
0.1646	8500	0.0	-
0.1656	8550	0.0	-
0.1665	8600	0.0	-
0.1675	8650	0.0	-
0.1685	8700	0.0	-
0.1694	8750	0.0	-
0.1704	8800	0.0	-
0.1714	8850	0.0	-
0.1723	8900	0.0	-
0.1733	8950	0.0	-
0.1743	9000	0.0	-
0.1752	9050	0.0	-
0.1762	9100	0.0	-
0.1772	9150	0.0	-
0.1781	9200	0.0	-
0.1791	9250	0.0	-
0.1801	9300	0.0	-
0.1811	9350	0.0	-
0.1820	9400	0.0	-
0.1830	9450	0.0	-
0.1840	9500	0.0	-
0.1849	9550	0.0	-
0.1859	9600	0.0	-
0.1869	9650	0.0	-
0.1878	9700	0.0	-
0.1888	9750	0.0	-
0.1898	9800	0.0	-
0.1907	9850	0.0	-
0.1917	9900	0.0	-
0.1927	9950	0.0	-
0.1936	10000	0.0	-
0.1946	10050	0.0	-
0.1956	10100	0.0	-
0.1965	10150	0.0	-
0.1975	10200	0.0	-
0.1985	10250	0.0	-
0.1994	10300	0.0	-
0.2004	10350	0.0	-
0.2014	10400	0.0	-
0.2024	10450	0.0	-
0.2033	10500	0.0	-
0.2043	10550	0.0	-
0.2053	10600	0.0	-
0.2062	10650	0.0	-
0.2072	10700	0.0	-
0.2082	10750	0.0	-
0.2091	10800	0.0	-
0.2101	10850	0.0	-
0.2111	10900	0.0	-
0.2120	10950	0.0	-
0.2130	11000	0.0	-
0.2140	11050	0.0	-
0.2149	11100	0.0	-
0.2159	11150	0.0	-
0.2169	11200	0.0	-
0.2178	11250	0.0	-
0.2188	11300	0.0	-
0.2198	11350	0.0	-
0.2207	11400	0.0	-
0.2217	11450	0.0	-
0.2227	11500	0.0	-
0.2237	11550	0.0	-
0.2246	11600	0.0	-
0.2256	11650	0.0	-
0.2266	11700	0.0	-
0.2275	11750	0.0	-
0.2285	11800	0.0	-
0.2295	11850	0.0	-
0.2304	11900	0.0	-
0.2314	11950	0.0	-
0.2324	12000	0.0	-
0.2333	12050	0.0	-
0.2343	12100	0.0	-
0.2353	12150	0.0	-
0.2362	12200	0.0	-
0.2372	12250	0.0	-
0.2382	12300	0.0	-
0.2391	12350	0.0	-
0.2401	12400	0.0	-
0.2411	12450	0.0	-
0.2420	12500	0.0	-
0.2430	12550	0.0	-
0.2440	12600	0.0	-
0.2450	12650	0.0	-
0.2459	12700	0.0	-
0.2469	12750	0.0	-
0.2479	12800	0.0	-
0.2488	12850	0.0	-
0.2498	12900	0.0	-
0.2508	12950	0.0001	-
0.2517	13000	0.0	-
0.2527	13050	0.0	-
0.2537	13100	0.0	-
0.2546	13150	0.0	-
0.2556	13200	0.0	-
0.2566	13250	0.0	-
0.2575	13300	0.0	-
0.2585	13350	0.0	-
0.2595	13400	0.0	-
0.2604	13450	0.0	-
0.2614	13500	0.0	-
0.2624	13550	0.0	-
0.2633	13600	0.0	-
0.2643	13650	0.0	-
0.2653	13700	0.0	-
0.2663	13750	0.0	-
0.2672	13800	0.0	-
0.2682	13850	0.0	-
0.2692	13900	0.0	-
0.2701	13950	0.0	-
0.2711	14000	0.0	-
0.2721	14050	0.0	-
0.2730	14100	0.0	-
0.2740	14150	0.0	-
0.2750	14200	0.0	-
0.2759	14250	0.0	-
0.2769	14300	0.0	-
0.2779	14350	0.0	-
0.2788	14400	0.0	-
0.2798	14450	0.0	-
0.2808	14500	0.0	-
0.2817	14550	0.0	-
0.2827	14600	0.0	-
0.2837	14650	0.0	-
0.2846	14700	0.0	-
0.2856	14750	0.0	-
0.2866	14800	0.0	-
0.2876	14850	0.0	-
0.2885	14900	0.0	-
0.2895	14950	0.0	-
0.2905	15000	0.0	-
0.2914	15050	0.0	-
0.2924	15100	0.0	-
0.2934	15150	0.0	-
0.2943	15200	0.0	-
0.2953	15250	0.0	-
0.2963	15300	0.0	-
0.2972	15350	0.0	-
0.2982	15400	0.0	-
0.2992	15450	0.0	-
0.3001	15500	0.0	-
0.3011	15550	0.0	-
0.3021	15600	0.0	-
0.3030	15650	0.0	-
0.3040	15700	0.0	-
0.3050	15750	0.0	-
0.3059	15800	0.0	-
0.3069	15850	0.0	-
0.3079	15900	0.0	-
0.3089	15950	0.0	-
0.3098	16000	0.0	-
0.3108	16050	0.0	-
0.3118	16100	0.0	-
0.3127	16150	0.0	-
0.3137	16200	0.0	-
0.3147	16250	0.0	-
0.3156	16300	0.0	-
0.3166	16350	0.0	-
0.3176	16400	0.0	-
0.3185	16450	0.0	-
0.3195	16500	0.0	-
0.3205	16550	0.0	-
0.3214	16600	0.0	-
0.3224	16650	0.0	-
0.3234	16700	0.0	-
0.3243	16750	0.0	-
0.3253	16800	0.0	-
0.3263	16850	0.0	-
0.3272	16900	0.0	-
0.3282	16950	0.0	-
0.3292	17000	0.0	-
0.3302	17050	0.0	-
0.3311	17100	0.0	-
0.3321	17150	0.0	-
0.3331	17200	0.0	-
0.3340	17250	0.0	-
0.3350	17300	0.0	-
0.3360	17350	0.0	-
0.3369	17400	0.0	-
0.3379	17450	0.0	-
0.3389	17500	0.0	-
0.3398	17550	0.0	-
0.3408	17600	0.0	-
0.3418	17650	0.0	-
0.3427	17700	0.0	-
0.3437	17750	0.0	-
0.3447	17800	0.0	-
0.3456	17850	0.0	-
0.3466	17900	0.0	-
0.3476	17950	0.0	-
0.3485	18000	0.0	-
0.3495	18050	0.0	-
0.3505	18100	0.0	-
0.3515	18150	0.0	-
0.3524	18200	0.0	-
0.3534	18250	0.0	-
0.3544	18300	0.0	-
0.3553	18350	0.0	-
0.3563	18400	0.0	-
0.3573	18450	0.0	-
0.3582	18500	0.0	-
0.3592	18550	0.0	-
0.3602	18600	0.0	-
0.3611	18650	0.0	-
0.3621	18700	0.0	-
0.3631	18750	0.0	-
0.3640	18800	0.0	-
0.3650	18850	0.0	-
0.3660	18900	0.0	-
0.3669	18950	0.0	-
0.3679	19000	0.0	-
0.3689	19050	0.0	-
0.3698	19100	0.0	-
0.3708	19150	0.0	-
0.3718	19200	0.0	-
0.3728	19250	0.0	-
0.3737	19300	0.0	-
0.3747	19350	0.0	-
0.3757	19400	0.0	-
0.3766	19450	0.0	-
0.3776	19500	0.0	-
0.3786	19550	0.0	-
0.3795	19600	0.0	-
0.3805	19650	0.0	-
0.3815	19700	0.0	-
0.3824	19750	0.0	-
0.3834	19800	0.0	-
0.3844	19850	0.0	-
0.3853	19900	0.0	-
0.3863	19950	0.0	-
0.3873	20000	0.0165	-
0.3882	20050	0.0065	-
0.3892	20100	0.0014	-
0.3902	20150	0.002	-
0.3911	20200	0.0011	-
0.3921	20250	0.0	-
0.3931	20300	0.0014	-
0.3941	20350	0.0	-
0.3950	20400	0.0008	-
0.3960	20450	0.0	-
0.3970	20500	0.0	-
0.3979	20550	0.0	-
0.3989	20600	0.0	-
0.3999	20650	0.0	-
0.4008	20700	0.0	-
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Framework Versions

Python: 3.10.12
SetFit: 1.1.0
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}
}