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cbpuschmann
/
klimacoder_modernbert_v0.1

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

SetFit with nomic-ai/modernbert-embed-base

This is a SetFit model that can be used for Text Classification. This SetFit model uses nomic-ai/modernbert-embed-base 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
opposed
  • 'Das umstrittene "Heizungsgesetz" sorgt für hitzige Debatten und lässt viele Hausbesitzer deutschlandweit zittern: Drohen uns jetzt hohe Kosten und ein bürokratisches Chaos rund um die verpflichtende Wärmepumpen-Installation? Kritiker warnen vor überstürzten Maßnahmen, die mehr Schaden als Nutzen bringen könnten.'
  • 'Die selbsternannten Retter der Welt von Fridays for Future und der Letzten Generation scheinen wieder einmal nichts Besseres zu tun zu haben, als den Alltag der hart arbeitenden Bürger mit ihren fragwürdigen Protestaktionen zu stören. Während sie sich in ihrer moralischen Überlegenheit sonnen, bleibt die Frage offen, wer die Rechnung für ihre chaotischen Stunts zahlen soll.'
  • 'Die neueste Gesetzesinitiative zur Einführung eines Tempolimits auf unseren Autobahnen ist ein weiterer Schritt in Richtung Bevormundung der Bürger durch den Staat. Anstatt auf Eigenverantwortung und Freiheit zu setzen, wird mit fragwürdigen Argumenten eine Tradition der deutschen Fahrkultur aufs Spiel gesetzt.'
neutral
  • 'Die Bundesregierung hat vorgestern einen Entwurf für ein nationales Tempolimit auf Autobahnen vorgelegt. Demnach entspricht der Vorschlag einem von den EU-Kommissionären geforderten Schritt, um die Verkehrssicherheit zu verbessern. Der Entwurf sieht vor, dass auf ausgewählten Strecken eine Höchstgeschwindigkeit von 130 km/h festgelegt wird.'
  • 'Die Bundesregierung hat ein Gesetz zur Förderung der flächendeckenden Einführung von Wärmepumpen verabschiedet, das den Übergang zu umweltfreundlicheren Heizungssystemen beschleunigen soll. Kritiker warnen vor möglichen finanziellen Belastungen für Hausbesitzer, während Befürworter die Maßnahme als notwendigen Schritt zur Erreichung der Klimaziele betrachten.'
  • 'Das Bundeskabinett hat sich auf seiner jüngsten Sitzung mit der Einführung eines nationalen Tempolimits auf Autobahnen auseinandergesetzt. Die Regierung will Gesetzesinitiativen erarbeiten, um die Geschwindigkeiten auf den Hauptverkehrsstrecken in Deutschland zu beschränken. \n\n Quelle: Bundesregierung'
supportive
  • 'Die Bundesregierung plant den Ausbau des nationalen Tempolimits auf Autobahnen. Nach Angaben von Verkehrsministerin Wilke soll dies die Verkehrssicherheit und -effizienz erhöhen, insbesondere in Kurven und Tunneln. Die Initiative wird von Umweltverbänden begrüßt, da sie den CO2-Ausstoß reduziert und die Belastung für Autofahrer vermindert.'
  • 'Die flächendeckende Einführung von Wärmepumpen durch das neue Heizungsgesetz könnte einen bedeutenden Schritt in Richtung einer nachhaltigeren Energiezukunft darstellen, indem sie den CO2-Ausstoß im Gebäudesektor erheblich reduziert. Zudem verspricht die Initiative, langfristig die Abhängigkeit von fossilen Brennstoffen zu verringern und die Energiekosten für Verbraucher zu stabilisieren.'
  • 'In den letzten Tagen haben Demonstranten erneut aufgerufen, um für stärkere Maßnahmen gegen den Klimawandel zu kämpfen. Viele von ihnen gehören zu Gruppen wie Fridays for Future oder Letzte Generation, die sich mit eindrucksvollen Aktionen für ihre Forderungen einsetzen. Ihre Überzeugung und Engagement verdienen Anerkennung.'

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/klimacoder_modernbert_v0.1")
# Run inference
preds = model("Chaos auf den Straßen und genervte Pendler: Die Klima-Aktivisten von Fridays for Future und der Letzten Generation sorgen erneut für Unmut in der Bevölkerung. Während sie für ihre Sache kämpfen, wächst der Frust über ihre umstrittenen Methoden.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 24 44.1632 73
Label Training Sample Count
neutral 503
opposed 536
supportive 536

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 0.343 -
0.0010 50 0.3042 -
0.0019 100 0.2881 -
0.0029 150 0.2698 -
0.0039 200 0.2463 -
0.0048 250 0.2377 -
0.0058 300 0.2319 -
0.0068 350 0.2074 -
0.0077 400 0.1729 -
0.0087 450 0.1458 -
0.0097 500 0.1004 -
0.0106 550 0.0714 -
0.0116 600 0.0452 -
0.0126 650 0.028 -
0.0136 700 0.0149 -
0.0145 750 0.0101 -
0.0155 800 0.0067 -
0.0165 850 0.0037 -
0.0174 900 0.0032 -
0.0184 950 0.0023 -
0.0194 1000 0.0017 -
0.0203 1050 0.0011 -
0.0213 1100 0.0006 -
0.0223 1150 0.0011 -
0.0232 1200 0.0016 -
0.0242 1250 0.0021 -
0.0252 1300 0.0004 -
0.0261 1350 0.0003 -
0.0271 1400 0.0009 -
0.0281 1450 0.002 -
0.0290 1500 0.0008 -
0.0300 1550 0.0012 -
0.0310 1600 0.0003 -
0.0319 1650 0.0002 -
0.0329 1700 0.0003 -
0.0339 1750 0.0002 -
0.0348 1800 0.0001 -
0.0358 1850 0.0001 -
0.0368 1900 0.0001 -
0.0377 1950 0.0001 -
0.0387 2000 0.0001 -
0.0397 2050 0.0001 -
0.0407 2100 0.0001 -
0.0416 2150 0.0001 -
0.0426 2200 0.0001 -
0.0436 2250 0.0001 -
0.0445 2300 0.0001 -
0.0455 2350 0.0001 -
0.0465 2400 0.0001 -
0.0474 2450 0.0001 -
0.0484 2500 0.0001 -
0.0494 2550 0.0 -
0.0503 2600 0.0 -
0.0513 2650 0.0 -
0.0523 2700 0.0 -
0.0532 2750 0.0 -
0.0542 2800 0.0 -
0.0552 2850 0.0 -
0.0561 2900 0.0 -
0.0571 2950 0.0 -
0.0581 3000 0.0 -
0.0590 3050 0.0 -
0.0600 3100 0.0 -
0.0610 3150 0.0 -
0.0619 3200 0.0 -
0.0629 3250 0.0 -
0.0639 3300 0.0 -
0.0649 3350 0.0 -
0.0658 3400 0.0 -
0.0668 3450 0.0 -
0.0678 3500 0.0 -
0.0687 3550 0.0 -
0.0697 3600 0.0 -
0.0707 3650 0.0 -
0.0716 3700 0.0 -
0.0726 3750 0.0 -
0.0736 3800 0.0 -
0.0745 3850 0.0 -
0.0755 3900 0.0 -
0.0765 3950 0.0 -
0.0774 4000 0.0 -
0.0784 4050 0.0 -
0.0794 4100 0.0 -
0.0803 4150 0.0 -
0.0813 4200 0.0 -
0.0823 4250 0.0 -
0.0832 4300 0.0 -
0.0842 4350 0.0 -
0.0852 4400 0.0 -
0.0861 4450 0.0 -
0.0871 4500 0.0 -
0.0881 4550 0.0 -
0.0890 4600 0.0 -
0.0900 4650 0.0 -
0.0910 4700 0.0 -
0.0920 4750 0.0 -
0.0929 4800 0.0 -
0.0939 4850 0.0 -
0.0949 4900 0.0 -
0.0958 4950 0.0 -
0.0968 5000 0.0 -
0.0978 5050 0.0 -
0.0987 5100 0.0 -
0.0997 5150 0.0 -
0.1007 5200 0.0 -
0.1016 5250 0.0 -
0.1026 5300 0.0 -
0.1036 5350 0.0 -
0.1045 5400 0.0 -
0.1055 5450 0.0 -
0.1065 5500 0.0 -
0.1074 5550 0.0 -
0.1084 5600 0.0 -
0.1094 5650 0.0 -
0.1103 5700 0.0 -
0.1113 5750 0.0 -
0.1123 5800 0.0 -
0.1132 5850 0.0 -
0.1142 5900 0.0 -
0.1152 5950 0.0 -
0.1162 6000 0.0 -
0.1171 6050 0.0 -
0.1181 6100 0.0 -
0.1191 6150 0.0 -
0.1200 6200 0.0 -
0.1210 6250 0.0 -
0.1220 6300 0.0 -
0.1229 6350 0.0 -
0.1239 6400 0.0 -
0.1249 6450 0.0 -
0.1258 6500 0.0 -
0.1268 6550 0.0 -
0.1278 6600 0.0 -
0.1287 6650 0.0 -
0.1297 6700 0.0 -
0.1307 6750 0.0 -
0.1316 6800 0.0 -
0.1326 6850 0.0 -
0.1336 6900 0.0 -
0.1345 6950 0.0 -
0.1355 7000 0.0 -
0.1365 7050 0.0 -
0.1374 7100 0.0 -
0.1384 7150 0.0 -
0.1394 7200 0.0 -
0.1403 7250 0.0 -
0.1413 7300 0.0 -
0.1423 7350 0.0 -
0.1433 7400 0.0 -
0.1442 7450 0.0 -
0.1452 7500 0.0 -
0.1462 7550 0.0 -
0.1471 7600 0.0 -
0.1481 7650 0.0 -
0.1491 7700 0.0 -
0.1500 7750 0.0 -
0.1510 7800 0.0 -
0.1520 7850 0.0 -
0.1529 7900 0.0 -
0.1539 7950 0.0 -
0.1549 8000 0.0 -
0.1558 8050 0.0 -
0.1568 8100 0.0 -
0.1578 8150 0.0 -
0.1587 8200 0.0 -
0.1597 8250 0.0 -
0.1607 8300 0.0 -
0.1616 8350 0.0 -
0.1626 8400 0.0 -
0.1636 8450 0.0 -
0.1645 8500 0.0 -
0.1655 8550 0.0 -
0.1665 8600 0.0 -
0.1675 8650 0.0 -
0.1684 8700 0.0 -
0.1694 8750 0.0 -
0.1704 8800 0.0 -
0.1713 8850 0.0 -
0.1723 8900 0.0 -
0.1733 8950 0.0 -
0.1742 9000 0.0 -
0.1752 9050 0.0 -
0.1762 9100 0.0 -
0.1771 9150 0.0 -
0.1781 9200 0.0 -
0.1791 9250 0.0 -
0.1800 9300 0.0 -
0.1810 9350 0.0 -
0.1820 9400 0.0 -
0.1829 9450 0.0 -
0.1839 9500 0.0 -
0.1849 9550 0.0 -
0.1858 9600 0.0 -
0.1868 9650 0.0 -
0.1878 9700 0.0 -
0.1887 9750 0.0 -
0.1897 9800 0.0 -
0.1907 9850 0.0 -
0.1916 9900 0.0 -
0.1926 9950 0.0 -
0.1936 10000 0.0 -
0.1946 10050 0.0 -
0.1955 10100 0.0 -
0.1965 10150 0.0 -
0.1975 10200 0.0 -
0.1984 10250 0.0 -
0.1994 10300 0.0 -
0.2004 10350 0.0 -
0.2013 10400 0.0 -
0.2023 10450 0.0 -
0.2033 10500 0.0 -
0.2042 10550 0.0 -
0.2052 10600 0.0 -
0.2062 10650 0.0 -
0.2071 10700 0.1864 -
0.2081 10750 0.0643 -
0.2091 10800 0.0257 -
0.2100 10850 0.0125 -
0.2110 10900 0.0097 -
0.2120 10950 0.0072 -
0.2129 11000 0.0032 -
0.2139 11050 0.001 -
0.2149 11100 0.0001 -
0.2158 11150 0.0001 -
0.2168 11200 0.0001 -
0.2178 11250 0.0 -
0.2188 11300 0.0001 -
0.2197 11350 0.0 -
0.2207 11400 0.0 -
0.2217 11450 0.0 -
0.2226 11500 0.0 -
0.2236 11550 0.0 -
0.2246 11600 0.0 -
0.2255 11650 0.0 -
0.2265 11700 0.0 -
0.2275 11750 0.0 -
0.2284 11800 0.0 -
0.2294 11850 0.0 -
0.2304 11900 0.0 -
0.2313 11950 0.0 -
0.2323 12000 0.0 -
0.2333 12050 0.0 -
0.2342 12100 0.0 -
0.2352 12150 0.0 -
0.2362 12200 0.0 -
0.2371 12250 0.0 -
0.2381 12300 0.0 -
0.2391 12350 0.0 -
0.2400 12400 0.0 -
0.2410 12450 0.0 -
0.2420 12500 0.0 -
0.2429 12550 0.0 -
0.2439 12600 0.0 -
0.2449 12650 0.0 -
0.2459 12700 0.0 -
0.2468 12750 0.0 -
0.2478 12800 0.0 -
0.2488 12850 0.0 -
0.2497 12900 0.0 -
0.2507 12950 0.0 -
0.2517 13000 0.0 -
0.2526 13050 0.0 -
0.2536 13100 0.0 -
0.2546 13150 0.0 -
0.2555 13200 0.0 -
0.2565 13250 0.0 -
0.2575 13300 0.0 -
0.2584 13350 0.0 -
0.2594 13400 0.0 -
0.2604 13450 0.0 -
0.2613 13500 0.0 -
0.2623 13550 0.0 -
0.2633 13600 0.0 -
0.2642 13650 0.0 -
0.2652 13700 0.0 -
0.2662 13750 0.0 -
0.2671 13800 0.0 -
0.2681 13850 0.0 -
0.2691 13900 0.0 -
0.2701 13950 0.0 -
0.2710 14000 0.0 -
0.2720 14050 0.0 -
0.2730 14100 0.0 -
0.2739 14150 0.0 -
0.2749 14200 0.0 -
0.2759 14250 0.0 -
0.2768 14300 0.0 -
0.2778 14350 0.0 -
0.2788 14400 0.0 -
0.2797 14450 0.0 -
0.2807 14500 0.0 -
0.2817 14550 0.0 -
0.2826 14600 0.0 -
0.2836 14650 0.0 -
0.2846 14700 0.0 -
0.2855 14750 0.0 -
0.2865 14800 0.0 -
0.2875 14850 0.0 -
0.2884 14900 0.0 -
0.2894 14950 0.0 -
0.2904 15000 0.0 -
0.2913 15050 0.0 -
0.2923 15100 0.0 -
0.2933 15150 0.0 -
0.2942 15200 0.0 -
0.2952 15250 0.0 -
0.2962 15300 0.0 -
0.2972 15350 0.0 -
0.2981 15400 0.0 -
0.2991 15450 0.0 -
0.3001 15500 0.0 -
0.3010 15550 0.0 -
0.3020 15600 0.0 -
0.3030 15650 0.0 -
0.3039 15700 0.0 -
0.3049 15750 0.0 -
0.3059 15800 0.0 -
0.3068 15850 0.0 -
0.3078 15900 0.0 -
0.3088 15950 0.0 -
0.3097 16000 0.0 -
0.3107 16050 0.0 -
0.3117 16100 0.0 -
0.3126 16150 0.0 -
0.3136 16200 0.0 -
0.3146 16250 0.0 -
0.3155 16300 0.0 -
0.3165 16350 0.0 -
0.3175 16400 0.0 -
0.3184 16450 0.0 -
0.3194 16500 0.0 -
0.3204 16550 0.0 -
0.3214 16600 0.0 -
0.3223 16650 0.0 -
0.3233 16700 0.0 -
0.3243 16750 0.0 -
0.3252 16800 0.0 -
0.3262 16850 0.0 -
0.3272 16900 0.0 -
0.3281 16950 0.0 -
0.3291 17000 0.0 -
0.3301 17050 0.0 -
0.3310 17100 0.0 -
0.3320 17150 0.0 -
0.3330 17200 0.0 -
0.3339 17250 0.0 -
0.3349 17300 0.0 -
0.3359 17350 0.0 -
0.3368 17400 0.0 -
0.3378 17450 0.0 -
0.3388 17500 0.0 -
0.3397 17550 0.0 -
0.3407 17600 0.0 -
0.3417 17650 0.0 -
0.3426 17700 0.0 -
0.3436 17750 0.0 -
0.3446 17800 0.0 -
0.3455 17850 0.0 -
0.3465 17900 0.0 -
0.3475 17950 0.0 -
0.3485 18000 0.0 -
0.3494 18050 0.0 -
0.3504 18100 0.0 -
0.3514 18150 0.0 -
0.3523 18200 0.0 -
0.3533 18250 0.0 -
0.3543 18300 0.0 -
0.3552 18350 0.0 -
0.3562 18400 0.0 -
0.3572 18450 0.0 -
0.3581 18500 0.0 -
0.3591 18550 0.0 -
0.3601 18600 0.0 -
0.3610 18650 0.0 -
0.3620 18700 0.0 -
0.3630 18750 0.0 -
0.3639 18800 0.0 -
0.3649 18850 0.0 -
0.3659 18900 0.0 -
0.3668 18950 0.0 -
0.3678 19000 0.0 -
0.3688 19050 0.0 -
0.3697 19100 0.0 -
0.3707 19150 0.0 -
0.3717 19200 0.0 -
0.3727 19250 0.0 -
0.3736 19300 0.0 -
0.3746 19350 0.0 -
0.3756 19400 0.0 -
0.3765 19450 0.0 -
0.3775 19500 0.0 -
0.3785 19550 0.0 -
0.3794 19600 0.0 -
0.3804 19650 0.0 -
0.3814 19700 0.0 -
0.3823 19750 0.0 -
0.3833 19800 0.0 -
0.3843 19850 0.0 -
0.3852 19900 0.0 -
0.3862 19950 0.0 -
0.3872 20000 0.0 -
0.3881 20050 0.0 -
0.3891 20100 0.0 -
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0.3910 20200 0.0 -
0.3920 20250 0.0 -
0.3930 20300 0.0 -
0.3939 20350 0.0 -
0.3949 20400 0.0 -
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0.3968 20500 0.0 -
0.3978 20550 0.0 -
0.3988 20600 0.0 -
0.3998 20650 0.0 -
0.4007 20700 0.0 -
0.4017 20750 0.0 -
0.4027 20800 0.0 -
0.4036 20850 0.0 -
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0.4065 21000 0.0 -
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0.4104 21200 0.0 -
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0.4123 21300 0.0 -
0.4133 21350 0.0 -
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0.4162 21500 0.0 -
0.4172 21550 0.0 -
0.4181 21600 0.0 -
0.4191 21650 0.0 -
0.4201 21700 0.0 -
0.4210 21750 0.0 -
0.4220 21800 0.0 -
0.4230 21850 0.0 -
0.4240 21900 0.0 -
0.4249 21950 0.0 -
0.4259 22000 0.0 -
0.4269 22050 0.0 -
0.4278 22100 0.0 -
0.4288 22150 0.0 -
0.4298 22200 0.0 -
0.4307 22250 0.0 -
0.4317 22300 0.0 -
0.4327 22350 0.0 -
0.4336 22400 0.0 -
0.4346 22450 0.0 -
0.4356 22500 0.0 -
0.4365 22550 0.0 -
0.4375 22600 0.0 -
0.4385 22650 0.0 -
0.4394 22700 0.0 -
0.4404 22750 0.0 -
0.4414 22800 0.0 -
0.4423 22850 0.0 -
0.4433 22900 0.0 -
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0.4452 23000 0.0 -
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0.4472 23100 0.0 -
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0.4491 23200 0.0 -
0.4501 23250 0.0 -
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0.4675 24150 0.0 -
0.4685 24200 0.0 -
0.4694 24250 0.0 -
0.4704 24300 0.0 -
0.4714 24350 0.0 -
0.4723 24400 0.0 -
0.4733 24450 0.0 -
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0.4753 24550 0.0 -
0.4762 24600 0.0 -
0.4772 24650 0.0 -
0.4782 24700 0.0 -
0.4791 24750 0.0 -
0.4801 24800 0.0 -
0.4811 24850 0.0 -
0.4820 24900 0.0 -
0.4830 24950 0.0 -
0.4840 25000 0.0 -
0.4849 25050 0.0 -
0.4859 25100 0.0 -
0.4869 25150 0.0 -
0.4878 25200 0.0 -
0.4888 25250 0.0 -
0.4898 25300 0.0 -
0.4907 25350 0.0 -
0.4917 25400 0.0 -
0.4927 25450 0.0 -
0.4936 25500 0.0 -
0.4946 25550 0.0 -
0.4956 25600 0.0 -
0.4965 25650 0.0 -
0.4975 25700 0.0 -
0.4985 25750 0.0 -
0.4994 25800 0.0 -
0.5004 25850 0.0 -
0.5014 25900 0.0 -
0.5024 25950 0.0 -
0.5033 26000 0.0 -
0.5043 26050 0.0 -
0.5053 26100 0.0 -
0.5062 26150 0.0 -
0.5072 26200 0.0 -
0.5082 26250 0.0 -
0.5091 26300 0.0 -
0.5101 26350 0.0 -
0.5111 26400 0.0 -
0.5120 26450 0.0 -
0.5130 26500 0.0 -
0.5140 26550 0.0 -
0.5149 26600 0.0 -
0.5159 26650 0.0 -
0.5169 26700 0.0 -
0.5178 26750 0.0 -
0.5188 26800 0.0 -
0.5198 26850 0.0 -
0.5207 26900 0.0 -
0.5217 26950 0.0 -
0.5227 27000 0.0 -
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Framework Versions

  • Python: 3.10.12
  • SetFit: 1.2.0.dev0
  • Sentence Transformers: 3.3.1
  • Transformers: 4.48.0.dev0
  • PyTorch: 2.5.1+cu121
  • 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}
}
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