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cbpuschmann
/
paraphrase-multilingual-minilm-klimacoder_v0.10

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files
xet
Community

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 Sources

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.

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 -
0.4018 20750 0.0 -
0.4028 20800 0.0 -
0.4037 20850 0.0 -
0.4047 20900 0.0 -
0.4057 20950 0.0 -
0.4066 21000 0.0 -
0.4076 21050 0.0 -
0.4086 21100 0.0 -
0.4095 21150 0.0 -
0.4105 21200 0.0 -
0.4115 21250 0.0 -
0.4124 21300 0.0 -
0.4134 21350 0.0 -
0.4144 21400 0.0 -
0.4154 21450 0.0 -
0.4163 21500 0.0 -
0.4173 21550 0.0 -
0.4183 21600 0.0 -
0.4192 21650 0.0 -
0.4202 21700 0.0 -
0.4212 21750 0.0 -
0.4221 21800 0.0 -
0.4231 21850 0.0 -
0.4241 21900 0.0 -
0.4250 21950 0.0 -
0.4260 22000 0.0 -
0.4270 22050 0.0 -
0.4279 22100 0.0 -
0.4289 22150 0.0 -
0.4299 22200 0.0 -
0.4308 22250 0.0 -
0.4318 22300 0.0 -
0.4328 22350 0.0 -
0.4337 22400 0.0 -
0.4347 22450 0.0 -
0.4357 22500 0.0 -
0.4367 22550 0.0 -
0.4376 22600 0.0 -
0.4386 22650 0.0 -
0.4396 22700 0.0 -
0.4405 22750 0.0 -
0.4415 22800 0.0 -
0.4425 22850 0.0 -
0.4434 22900 0.0 -
0.4444 22950 0.0 -
0.4454 23000 0.0 -
0.4463 23050 0.0 -
0.4473 23100 0.0 -
0.4483 23150 0.0 -
0.4492 23200 0.0 -
0.4502 23250 0.0 -
0.4512 23300 0.0 -
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0.4647 24000 0.0 -
0.4657 24050 0.0 -
0.4667 24100 0.0 -
0.4676 24150 0.0 -
0.4686 24200 0.0 -
0.4696 24250 0.0 -
0.4705 24300 0.0 -
0.4715 24350 0.0 -
0.4725 24400 0.0 -
0.4734 24450 0.0 -
0.4744 24500 0.0 -
0.4754 24550 0.0 -
0.4763 24600 0.0 -
0.4773 24650 0.0 -
0.4783 24700 0.0 -
0.4793 24750 0.0 -
0.4802 24800 0.0 -
0.4812 24850 0.0 -
0.4822 24900 0.0 -
0.4831 24950 0.0 -
0.4841 25000 0.0 -
0.4851 25050 0.0 -
0.4860 25100 0.0 -
0.4870 25150 0.0 -
0.4880 25200 0.0 -
0.4889 25250 0.0 -
0.4899 25300 0.0 -
0.4909 25350 0.0 -
0.4918 25400 0.0 -
0.4928 25450 0.0 -
0.4938 25500 0.0 -
0.4947 25550 0.0 -
0.4957 25600 0.0 -
0.4967 25650 0.0 -
0.4976 25700 0.0 -
0.4986 25750 0.0 -
0.4996 25800 0.0 -
0.5006 25850 0.0 -
0.5015 25900 0.0 -
0.5025 25950 0.0 -
0.5035 26000 0.0 -
0.5044 26050 0.0 -
0.5054 26100 0.0 -
0.5064 26150 0.0 -
0.5073 26200 0.0 -
0.5083 26250 0.0 -
0.5093 26300 0.0 -
0.5102 26350 0.0 -
0.5112 26400 0.0 -
0.5122 26450 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 

@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}
}
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