cbpuschmann/klimacoder2_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
neutral	'Die Ampelkoalition diskutiert über eine flächendeckende Einführung von Wärmepumpen, bekannt als "Heizungsgesetz". Ein konkreter Zeitplan für die Verabschiedung vor der Sommerpause ist noch nicht bestätigt, während Verhandlungen zwischen SPD, Grünen und FDP andauern. Die Gesetzesinitiative zielt darauf ab, den Klimaschutz im Wohnungssektor zu fördern, stößt aber auch auf offene Fragen in Bezug auf Kosten und Umsetzbarkeit.' 'Nach dem jüngsten Urteil des Bundesverfassungsgerichts steht die Einführung eines Heizungsgesetzes zur flächendeckenden Förderung von Wärmepumpen weiterhin auf der Agenda der Bundesregierung. Eigenheimbesitzer können ab 2024 mit einer hohen Förderung rechnen, um den Austausch alter Heizsysteme gegen klimafreundliche Alternativen wie Wärmepumpen zu unterstützen. Die Verhandlungen über die genauen Förderbedingungen befinden sich in der Endphase.' 'Das Bundesregierung plant eine Gesetzesinitiative zur flächendeckenden Einführung von Wärmepumpen, um den Klimaschutz zu fördern. Demnach sollen Hausbesitzer mehr Zeit für den Austausch ihrer Heizungen erhalten und die Maßnahmen an ein Gesetz zur kommunalen Wärmeplanung gekoppelt werden. Ziel ist es, einen effizienteren und nachhaltigeren Energieverbrauch in Gebäuden zu gewährleisten.'
opposed	'"Heizungsgesetz: Zu wenig für den Klimaschutz, zu viel für die Bürger?"\n\nDas geplante Heizungsgesetz von Bundeswirtschaftsminister Robert Habeck stößt auf erhebliche Kritik. Experten und Bürger äußern Zweifel an der Effektivität der Maßnahmen zur Reduzierung von CO₂-Emissionen, insbesondere angesichts der aktuellen Energiekrise und der immensen Wertverluste bei Immobilien. "Die paar Milliarden Förderung reichen nicht ansatzweise, um die Vermögensverluste auszugleichen", so ein erfahrener Immobilienkenner. Viele sehen auch einen Widerspruch zwischen den Anstrengungen für den Klimaschutz und dem anhaltenden Hass der Grünen auf traditionelle Technologien wie das Auto und Kachelofenheizungen.' 'Wärmepumpen-Revolution in Frage gestellt: Wohnungswirtschaft warnt vor katastrophalen Folgen\n\nDie geplante flächendeckende Einführung von Wärmepumpen durch das sogenannte "Heizungsgesetz" stößt auf heftige Kritik. Die Wohnungswirtschaft warnt vor einem möglichen Zusammenbruch der Branche, da die strengen Klimaauflagen die finanziellen Reserven der Unternehmen erschöpfen könnten. GdW-Präsident Gedaschko befürchtet sogar einen Einbruch beim Wohnungsneubau.' 'Skepsis gegenüber dem Heizungsgesetz: Ist ein Verbot von Erdgasheizungen ab 2024 sinnvoll?\n\nDas geplante "Heizungsgesetz" stößt auf Kritik: Während die Absicht, den Einsatz erneuerbarer Energien zu fördern, lobenswert ist, fürchten Experten, dass eine flächendeckende Einführung von Wärmepumpen ohne angemessene Unterstützung unpraktisch und teuer sein könnte. Die schärferen Regelungen ab 2024 könnten de facto ein Verbot von Erdgasheizungen bedeuten, ohne realistische Alternativen zu bieten.'
supportive	'Politische Initiativen für Wärmepumpen: Ein Schritt in die richtige Richtung\n\nWährend steigende Energiekosten einen Wechsel dringend erforderlich machen, wirft die flächendeckende Einführung von Wärmepumpen durch das "Heizungsgesetz" wichtige Fragen auf. Kritiker monieren mögliche finanzielle Belastungen für Hausbesitzer und eine unzureichende Berücksichtigung regionaler Gegebenheiten. Dennoch begrüßt die Mehrheit der Experten den Vorstoß als notwendigen Schritt hin zu nachhaltigeren Heizsystemen, insbesondere vor dem Hintergrund des Klimaschutzes.' 'Heizungsgesetz: Ein Schritt in die richtige Richtung, aber nicht ohne Kritikpunkte\n\nWährend die flächendeckende Einführung von Wärmepumpen durch das neue Heizungsgesetz als umweltfreundliche Alternative gelobt wird, warnen Kritiker vor einer pauschalen Verpflichtung. Experten der Verbraucherzentrale betonen, dass individuelle Haus- und Systemanforderungen berücksichtigt werden müssen. Trotzdem sehen sie in den aktuellen Förderungen einen wichtigen Anreiz für Hausbesitzer, auf effizientere Heiztechnologien umzusteigen.' '"Heizungsgesetz: Ein Schritt in die richtige Richtung, aber Kritik an Bürgerbeteiligung"\n\nWährend die Bundesregierung ein Heizungsgesetz zur flächendeckenden Einführung von Wärmepumpen plant, wächst die Skepsis in der Bevölkerung. Kritiker fürchten, dass die Maßnahme ohne ausreichende Berücksichtigung individueller Bedürfnisse und Eigentumsrechte umgesetzt wird. Nach den jüngsten Landtagswahlen zeigt sich, dass das Thema Klimaschutz, insbesondere bei Heiztechnologien, zu einer politischen Mobilisierungskraft geworden ist, die sowohl von konservativen als auch von populistischen Parteien genutzt wird.'

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/klimacoder2_v0.2")
# Run inference
preds = model("Die Forderungen sind landesweit die gleichen. Es geht um die Wiedereinführung eines 9-Euro-Tickets und ein Tempolimit von 100 km/h auf den Autobahnen. Außerdem fordern wir die Einführung eines Gesellschaftsrats. Dieser soll Maßnahmen erarbeiten, wie Deutschland bis 2030 emissionsfrei wird. Die Lösungsansätze sollen von der Bundesregierung anerkannt und in der Politik umgesetzt werden.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	27	65.3585	750

Label	Training Sample Count
supportive	900
opposed	900
neutral	900

Training Hyperparameters

• batch_size: (32, 32)
• 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	0.311	-
0.0003	50	0.2648	-
0.0007	100	0.2651	-
0.0010	150	0.271	-
0.0013	200	0.2636	-
0.0016	250	0.2644	-
0.0020	300	0.263	-
0.0023	350	0.2664	-
0.0026	400	0.2641	-
0.0030	450	0.2595	-
0.0033	500	0.2678	-
0.0036	550	0.2604	-
0.0040	600	0.262	-
0.0043	650	0.2599	-
0.0046	700	0.2591	-
0.0049	750	0.2606	-
0.0053	800	0.2551	-
0.0056	850	0.2556	-
0.0059	900	0.25	-
0.0063	950	0.2582	-
0.0066	1000	0.2446	-
0.0069	1050	0.2517	-
0.0072	1100	0.2443	-
0.0076	1150	0.2419	-
0.0079	1200	0.2423	-
0.0082	1250	0.2352	-
0.0086	1300	0.2312	-
0.0089	1350	0.2253	-
0.0092	1400	0.229	-
0.0095	1450	0.2177	-
0.0099	1500	0.2113	-
0.0102	1550	0.2046	-
0.0105	1600	0.2033	-
0.0109	1650	0.1921	-
0.0112	1700	0.1852	-
0.0115	1750	0.1772	-
0.0119	1800	0.1822	-
0.0122	1850	0.1693	-
0.0125	1900	0.162	-
0.0128	1950	0.1628	-
0.0132	2000	0.1544	-
0.0135	2050	0.1494	-
0.0138	2100	0.1537	-
0.0142	2150	0.1523	-
0.0145	2200	0.141	-
0.0148	2250	0.1315	-
0.0151	2300	0.1332	-
0.0155	2350	0.1301	-
0.0158	2400	0.1252	-
0.0161	2450	0.1241	-
0.0165	2500	0.1208	-
0.0168	2550	0.1141	-
0.0171	2600	0.1093	-
0.0174	2650	0.1066	-
0.0178	2700	0.0987	-
0.0181	2750	0.098	-
0.0184	2800	0.0934	-
0.0188	2850	0.0829	-
0.0191	2900	0.0751	-
0.0194	2950	0.0758	-
0.0198	3000	0.0686	-
0.0201	3050	0.0731	-
0.0204	3100	0.0696	-
0.0207	3150	0.0664	-
0.0211	3200	0.0622	-
0.0214	3250	0.0578	-
0.0217	3300	0.0533	-
0.0221	3350	0.055	-
0.0224	3400	0.049	-
0.0227	3450	0.0473	-
0.0230	3500	0.0458	-
0.0234	3550	0.0449	-
0.0237	3600	0.0373	-
0.0240	3650	0.0434	-
0.0244	3700	0.0381	-
0.0247	3750	0.0384	-
0.0250	3800	0.0393	-
0.0253	3850	0.0312	-
0.0257	3900	0.0332	-
0.0260	3950	0.0298	-
0.0263	4000	0.0277	-
0.0267	4050	0.0292	-
0.0270	4100	0.024	-
0.0273	4150	0.0209	-
0.0277	4200	0.0232	-
0.0280	4250	0.0215	-
0.0283	4300	0.0201	-
0.0286	4350	0.0201	-
0.0290	4400	0.0187	-
0.0293	4450	0.0182	-
0.0296	4500	0.0163	-
0.0300	4550	0.0138	-
0.0303	4600	0.0123	-
0.0306	4650	0.0108	-
0.0309	4700	0.0101	-
0.0313	4750	0.01	-
0.0316	4800	0.0081	-
0.0319	4850	0.008	-
0.0323	4900	0.0077	-
0.0326	4950	0.0072	-
0.0329	5000	0.0082	-
0.0333	5050	0.0066	-
0.0336	5100	0.007	-
0.0339	5150	0.0064	-
0.0342	5200	0.006	-
0.0346	5250	0.004	-
0.0349	5300	0.0068	-
0.0352	5350	0.0034	-
0.0356	5400	0.0051	-
0.0359	5450	0.0046	-
0.0362	5500	0.0043	-
0.0365	5550	0.0026	-
0.0369	5600	0.0027	-
0.0372	5650	0.0027	-
0.0375	5700	0.0021	-
0.0379	5750	0.0031	-
0.0382	5800	0.0022	-
0.0385	5850	0.0023	-
0.0388	5900	0.003	-
0.0392	5950	0.0016	-
0.0395	6000	0.0019	-
0.0398	6050	0.0012	-
0.0402	6100	0.0021	-
0.0405	6150	0.0011	-
0.0408	6200	0.0014	-
0.0412	6250	0.0014	-
0.0415	6300	0.001	-
0.0418	6350	0.0012	-
0.0421	6400	0.0009	-
0.0425	6450	0.0008	-
0.0428	6500	0.0006	-
0.0431	6550	0.0012	-
0.0435	6600	0.0008	-
0.0438	6650	0.0016	-
0.0441	6700	0.0016	-
0.0444	6750	0.0012	-
0.0448	6800	0.0006	-
0.0451	6850	0.0009	-
0.0454	6900	0.0007	-
0.0458	6950	0.001	-
0.0461	7000	0.0004	-
0.0464	7050	0.0008	-
0.0467	7100	0.001	-
0.0471	7150	0.0004	-
0.0474	7200	0.0005	-
0.0477	7250	0.0013	-
0.0481	7300	0.0005	-
0.0484	7350	0.0007	-
0.0487	7400	0.0007	-
0.0491	7450	0.0002	-
0.0494	7500	0.0004	-
0.0497	7550	0.001	-
0.0500	7600	0.0004	-
0.0504	7650	0.0009	-
0.0507	7700	0.0003	-
0.0510	7750	0.0002	-
0.0514	7800	0.0004	-
0.0517	7850	0.0002	-
0.0520	7900	0.0002	-
0.0523	7950	0.0001	-
0.0527	8000	0.0002	-
0.0530	8050	0.0002	-
0.0533	8100	0.0003	-
0.0537	8150	0.0004	-
0.0540	8200	0.0002	-
0.0543	8250	0.0002	-
0.0547	8300	0.0002	-
0.0550	8350	0.0001	-
0.0553	8400	0.0002	-
0.0556	8450	0.0001	-
0.0560	8500	0.0003	-
0.0563	8550	0.0009	-
0.0566	8600	0.0005	-
0.0570	8650	0.0001	-
0.0573	8700	0.0001	-
0.0576	8750	0.0001	-
0.0579	8800	0.001	-
0.0583	8850	0.0006	-
0.0586	8900	0.0012	-
0.0589	8950	0.0002	-
0.0593	9000	0.0001	-
0.0596	9050	0.0004	-
0.0599	9100	0.0002	-
0.0602	9150	0.0008	-
0.0606	9200	0.0001	-
0.0609	9250	0.0001	-
0.0612	9300	0.0	-
0.0616	9350	0.0003	-
0.0619	9400	0.0001	-
0.0622	9450	0.0004	-
0.0626	9500	0.0002	-
0.0629	9550	0.0001	-
0.0632	9600	0.0001	-
0.0635	9650	0.0002	-
0.0639	9700	0.0001	-
0.0642	9750	0.0	-
0.0645	9800	0.0	-
0.0649	9850	0.0	-
0.0652	9900	0.0	-
0.0655	9950	0.0006	-
0.0658	10000	0.0015	-
0.0662	10050	0.0013	-
0.0665	10100	0.0008	-
0.0668	10150	0.0001	-
0.0672	10200	0.0001	-
0.0675	10250	0.0	-
0.0678	10300	0.0011	-
0.0681	10350	0.0008	-
0.0685	10400	0.0002	-
0.0688	10450	0.0	-
0.0691	10500	0.0	-
0.0695	10550	0.0009	-
0.0698	10600	0.0003	-
0.0701	10650	0.0008	-
0.0705	10700	0.0013	-
0.0708	10750	0.0002	-
0.0711	10800	0.0001	-
0.0714	10850	0.0004	-
0.0718	10900	0.0006	-
0.0721	10950	0.0004	-
0.0724	11000	0.0001	-
0.0728	11050	0.0006	-
0.0731	11100	0.0004	-
0.0734	11150	0.0004	-
0.0737	11200	0.0004	-
0.0741	11250	0.0	-
0.0744	11300	0.0	-
0.0747	11350	0.0	-
0.0751	11400	0.0001	-
0.0754	11450	0.0006	-
0.0757	11500	0.0001	-
0.0760	11550	0.0011	-
0.0764	11600	0.0	-
0.0767	11650	0.0007	-
0.0770	11700	0.0007	-
0.0774	11750	0.0006	-
0.0777	11800	0.0	-
0.0780	11850	0.0	-
0.0784	11900	0.0	-
0.0787	11950	0.0	-
0.0790	12000	0.0	-
0.0793	12050	0.0	-
0.0797	12100	0.0	-
0.08	12150	0.0	-
0.0803	12200	0.0	-
0.0807	12250	0.0007	-
0.0810	12300	0.0011	-
0.0813	12350	0.0001	-
0.0816	12400	0.0	-
0.0820	12450	0.0003	-
0.0823	12500	0.0	-
0.0826	12550	0.0	-
0.0830	12600	0.0	-
0.0833	12650	0.0	-
0.0836	12700	0.0	-
0.0840	12750	0.0	-
0.0843	12800	0.0	-
0.0846	12850	0.0004	-
0.0849	12900	0.0	-
0.0853	12950	0.0	-
0.0856	13000	0.0	-
0.0859	13050	0.0002	-
0.0863	13100	0.0023	-
0.0866	13150	0.0004	-
0.0869	13200	0.0	-
0.0872	13250	0.0	-
0.0876	13300	0.0006	-
0.0879	13350	0.0019	-
0.0882	13400	0.0	-
0.0886	13450	0.0	-
0.0889	13500	0.0	-
0.0892	13550	0.0	-
0.0895	13600	0.0	-
0.0899	13650	0.0005	-
0.0902	13700	0.0008	-
0.0905	13750	0.0022	-
0.0909	13800	0.0017	-
0.0912	13850	0.0002	-
0.0915	13900	0.0	-
0.0919	13950	0.0	-
0.0922	14000	0.0	-
0.0925	14050	0.0002	-
0.0928	14100	0.0006	-
0.0932	14150	0.0035	-
0.0935	14200	0.0	-
0.0938	14250	0.0015	-
0.0942	14300	0.0	-
0.0945	14350	0.0	-
0.0948	14400	0.0	-
0.0951	14450	0.0	-
0.0955	14500	0.0	-
0.0958	14550	0.0	-
0.0961	14600	0.0	-
0.0965	14650	0.0	-
0.0968	14700	0.0	-
0.0971	14750	0.0	-
0.0974	14800	0.0007	-
0.0978	14850	0.0	-
0.0981	14900	0.0	-
0.0984	14950	0.0	-
0.0988	15000	0.0	-
0.0991	15050	0.0006	-
0.0994	15100	0.0024	-
0.0998	15150	0.0007	-
0.1001	15200	0.0	-
0.1004	15250	0.001	-
0.1007	15300	0.0018	-
0.1011	15350	0.0009	-
0.1014	15400	0.0005	-
0.1017	15450	0.0	-
0.1021	15500	0.0007	-
0.1024	15550	0.0	-
0.1027	15600	0.0	-
0.1030	15650	0.0	-
0.1034	15700	0.0004	-
0.1037	15750	0.0002	-
0.1040	15800	0.0007	-
0.1044	15850	0.0018	-
0.1047	15900	0.0009	-
0.1050	15950	0.0003	-
0.1053	16000	0.0	-
0.1057	16050	0.0	-
0.1060	16100	0.0	-
0.1063	16150	0.0	-
0.1067	16200	0.0	-
0.1070	16250	0.0	-
0.1073	16300	0.0	-
0.1077	16350	0.0	-
0.1080	16400	0.0	-
0.1083	16450	0.0	-
0.1086	16500	0.0	-
0.1090	16550	0.0	-
0.1093	16600	0.0	-
0.1096	16650	0.0	-
0.1100	16700	0.0	-
0.1103	16750	0.0	-
0.1106	16800	0.0	-
0.1109	16850	0.0	-
0.1113	16900	0.0	-
0.1116	16950	0.0	-
0.1119	17000	0.0	-
0.1123	17050	0.0	-
0.1126	17100	0.0016	-
0.1129	17150	0.0013	-
0.1133	17200	0.0004	-
0.1136	17250	0.0003	-
0.1139	17300	0.0005	-
0.1142	17350	0.0006	-
0.1146	17400	0.0	-
0.1149	17450	0.0	-
0.1152	17500	0.0013	-
0.1156	17550	0.0006	-
0.1159	17600	0.0	-
0.1162	17650	0.0	-
0.1165	17700	0.0	-
0.1169	17750	0.0	-
0.1172	17800	0.0	-
0.1175	17850	0.0008	-
0.1179	17900	0.0	-
0.1182	17950	0.0015	-
0.1185	18000	0.0006	-
0.1188	18050	0.0002	-
0.1192	18100	0.0	-
0.1195	18150	0.0	-
0.1198	18200	0.0	-
0.1202	18250	0.0049	-
0.1205	18300	0.0031	-
0.1208	18350	0.0008	-
0.1212	18400	0.0005	-
0.1215	18450	0.0004	-
0.1218	18500	0.0	-
0.1221	18550	0.0	-
0.1225	18600	0.0018	-
0.1228	18650	0.0001	-
0.1231	18700	0.0	-
0.1235	18750	0.0	-
0.1238	18800	0.0	-
0.1241	18850	0.0001	-
0.1244	18900	0.0	-
0.1248	18950	0.0	-
0.1251	19000	0.0	-
0.1254	19050	0.0	-
0.1258	19100	0.0	-
0.1261	19150	0.0009	-
0.1264	19200	0.0001	-
0.1267	19250	0.0	-
0.1271	19300	0.0	-
0.1274	19350	0.0	-
0.1277	19400	0.0	-
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0.1975	30000	0.0	-
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1.0	151875	-	0.3320
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1.0272	156000	0.0007	-
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1.0278	156100	0.0072	-
1.0281	156150	0.0031	-
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1.0291	156300	0.0004	-
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1.0308	156550	0.0003	-
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1.0318	156700	0.0004	-
1.0321	156750	0.0011	-
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1.0403	158000	0.0	-
1.0407	158050	0.0001	-
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1.0420	158250	0.0	-
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1.0677	162150	0.0	-
1.0680	162200	0.0051	-
1.0683	162250	0.0013	-
1.0686	162300	0.0001	-
1.0690	162350	0.0	-
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1.0749	163250	0.0002	-
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1.0805	164100	0.0007	-
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1.0812	164200	0.0	-
1.0815	164250	0.0	-
1.0818	164300	0.0006	-
1.0821	164350	0.0013	-
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1.1144	169250	0.0	-
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1.1193	170000	0.0	-
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1.1210	170250	0.0	-
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1.1342	172250	0.0	-
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1.1391	173000	0.0	-
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1.1588	176000	0.0	-
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1.1602	176200	0.0	-
1.1605	176250	0.0	-
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1.1654	177000	0.0	-
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1.1661	177100	0.0	-
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1.1667	177200	0.0	-
1.1671	177250	0.0	-
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1.1720	178000	0.0	-
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1.1727	178100	0.0	-
1.1730	178150	0.0	-
1.1733	178200	0.0	-
1.1737	178250	0.0	-
1.1740	178300	0.0	-
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1.2	182250	0.0	-
1.2003	182300	0.0	-
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Framework Versions

• Python: 3.11.10
• SetFit: 1.1.1
• Sentence Transformers: 3.4.1
• 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}
}