SetFit with T-Systems-onsite/cross-en-de-roberta-sentence-transformer

This is a SetFit model that can be used for Text Classification. This SetFit model uses T-Systems-onsite/cross-en-de-roberta-sentence-transformer 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
  • 'In jüngster Zeit häufen sich die Proteste der Gruppierungen "Fridays for Future" und "Letzte Generation", deren drastische Aktionen mehr Fragen als Antworten hinterlassen. Während sie mit emotional aufgeladenen Szenarien von Klimakatastrophen für Aufmerksamkeit sorgen, scheint ihr Vorgehen oft wenig sachlich fundiert zu sein. Anstatt konstruktiver Lösungen bieten ihre teils chaotischen Demonstrationen eher eine Plattform für mediale Schlagzeilen als für nachhaltigen Fortschritt in der Klimapolitik.'
  • 'Die radikalen Klima-Aktivisten von Gruppen wie Fridays for Future und Letzte Generation finanzieren ihre umstrittenen Aktionen durch Spenden und Unterstützung von internationalen Fonds, was Fragen zur Unabhängigkeit und den tatsächlichen Motiven dieser Bewegungen aufwirft. Während sie öffentlich für den Klimaschutz eintreten, bleibt unklar, wie viel Einfluss externe Geldgeber auf ihre Strategien und Entscheidungen haben.'
  • 'In den letzten Jahren hat der Druck von Gruppen wie Fridays for Future und der Letzte Generation auf die öffentliche Meinung stetig zugenommen – oft ohne Rücksicht auf die Auswirkungen ihrer Methoden auf Alltagsleben und Wirtschaft. Während diese Aktivisten ihre Aktionen als nötige Maßnahmen für den Klimaschutz darstellen, bleibt unklar, inwiefern ihre radikalen Ansätze tatsächlich nachhaltige Lösungen oder bloße Aufmerksamkeitsstunts sind. Finanziert durch Spenden und Unterstützung aus dem Ausland wie dem ›Climate Emergency Fund‹, scheinen die Mittel oft mehr auf Kampagnen als auf konkrete Veränderungen gerichtet zu sein.\n\nDisclaimer: Dieser Beitrag reflektiert eine kritische Haltung und nicht unbedingt die Meinung des gesamten Lesepublikums.'
supportive
  • 'Klima-Aktivismus-Gruppen wie Fridays for Future und Letzte Generation überschreiten mit blockierenden und teils störenden Aktionen oft die Grenze des Zumutbaren, doch sie zwingen dringend benötigte Debatten über Umweltpolitik auf die Agenda. Angesichts des stockenden Fortschritts beim Klimaschutz könnte ihr vehementes Auftreten jedoch der nötige Weckruf sein, um die Politik zu entschlossenem Handeln zu bewegen.'
  • 'Inmitten wachsender Spannungen um die Klima-Agenda ziehen die Aktionen von Gruppen wie Fridays for Future und Letzte Generation sowohl Bewunderung als auch Kritik auf sich. Während ihre Methoden oft als disruptiv empfunden werden, lenken sie unbestreitbar den dringend benötigten Fokus auf die klimatischen Herausforderungen unserer Zeit.'
  • 'Die jüngsten Aktionen der Klima-Aktivismus-Gruppen wie Fridays for Future und Letzte Generation haben zweifellos die öffentliche Wahrnehmung verschärft und Unternehmer dazu gezwungen, den Kopf aus dem Sand zu ziehen. Es bleibt jedoch fraglich, ob diese Druckmittel langfristig zu nachhaltigen Veränderungen führen oder nur kurzfristige Reaktionen hervorrufen. Trotzdem ist unverkennbar, dass der Anstieg der Energiepreise einen entscheidenden Katalysator darstellt, der Unternehmen schneller in die nachhaltige Energieversorgung treibt – ein Zeichen dafür, dass auch ökonomische Faktoren eine bedeutende Rolle spielen.'
neutral
  • 'Klimaschutz-Aktivisten von Gruppen wie Fridays for Future und Letzte Generation haben erneut durch Protestaktionen auf die Dringlichkeit klimapolitischer Maßnahmen aufmerksam gemacht. Während Befürworter die Notwendigkeit solcher Aktionen zur Erreichung ehrgeiziger Klimaziele betonen, kritisieren Gegner die teils disruptiven Methoden und fordern einen Dialog innerhalb bestehender politischer Prozesse.'
  • 'Bei Aktionen der Klima-Aktivismus-Gruppen wie Fridays for Future und der Letzten Generation haben am Wochenende in mehreren deutschen Städten zahlreiche Menschen für mehr Klimaschutz demonstriert. In Berlin versammelten sich laut Polizeiangaben rund 3000 Teilnehmer, während die Veranstalter von etwa 5000 Demonstrierenden sprachen. Die Gruppen fordern unter anderem eine schnellere Umsetzung von Maßnahmen zur Reduzierung der CO2-Emissionen.'
  • 'Am Freitag haben in Baden-Württemberg mehrere Tausend Menschen bei Demonstrationen der Klimaschutzbewegung Fridays for Future (FFF) und der Letzten Generation für mehr Umweltschutz demonstriert. In Freiburg zählte die Polizei bis zu 5000 Teilnehmer, während FFF von etwa 8000 Beteiligten berichtete. Die Proteste standen unter dem Motto „People not Profit“ und richteten sich gegen wirtschaftliche Interessen zugunsten des Klimaschutzes.\n\nDie Demonstrationen spiegeln die wachsende Besorgnis junger Menschen über den Klimawandel wider, während sie auch Diskussionen über die Balance zwischen ökonomischen Entwicklungen und Umweltschutz anstoßen.'

Evaluation

Metrics

Label Accuracy
all 0.5616

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_protest_v0.1")
# Run inference
preds = model("Aber ratzfatz waren sie ab; Klima-Kleber blockieren Bundesstraße in Dortmund Dortmund - Klimaaktivist der Gruppierung ›DLG› haben am Mittwoch den Straßenverkehr in der Dortmunder Innenstadt vorübergehend gestört. Am Morgen blockierten fünf Menschen einen Teil der zentralen zweispurigen B54 stadteinwärts, einige klebten sich fest.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 35 69.3880 934
Label Training Sample Count
neutral 364
opposed 398
supportive 367

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (3, 3)
  • 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 0.1907 -
0.0009 50 0.2333 -
0.0019 100 0.2279 -
0.0028 150 0.2239 -
0.0038 200 0.2268 -
0.0047 250 0.2008 -
0.0057 300 0.1987 -
0.0066 350 0.1811 -
0.0075 400 0.176 -
0.0085 450 0.1651 -
0.0094 500 0.149 -
0.0104 550 0.1267 -
0.0113 600 0.1133 -
0.0122 650 0.0895 -
0.0132 700 0.0827 -
0.0141 750 0.0593 -
0.0151 800 0.0472 -
0.0160 850 0.0357 -
0.0170 900 0.0255 -
0.0179 950 0.0213 -
0.0188 1000 0.0149 -
0.0198 1050 0.0144 -
0.0207 1100 0.0124 -
0.0217 1150 0.0091 -
0.0226 1200 0.0072 -
0.0236 1250 0.0049 -
0.0245 1300 0.0044 -
0.0254 1350 0.003 -
0.0264 1400 0.0036 -
0.0273 1450 0.0028 -
0.0283 1500 0.0024 -
0.0292 1550 0.0017 -
0.0302 1600 0.0021 -
0.0311 1650 0.0013 -
0.0320 1700 0.0012 -
0.0330 1750 0.0013 -
0.0339 1800 0.001 -
0.0349 1850 0.0007 -
0.0358 1900 0.0006 -
0.0367 1950 0.0016 -
0.0377 2000 0.0007 -
0.0386 2050 0.0006 -
0.0396 2100 0.0005 -
0.0405 2150 0.0005 -
0.0415 2200 0.0003 -
0.0424 2250 0.0004 -
0.0433 2300 0.0003 -
0.0443 2350 0.0003 -
0.0452 2400 0.0002 -
0.0462 2450 0.0002 -
0.0471 2500 0.0002 -
0.0481 2550 0.0002 -
0.0490 2600 0.0002 -
0.0499 2650 0.0003 -
0.0509 2700 0.0002 -
0.0518 2750 0.0002 -
0.0528 2800 0.0002 -
0.0537 2850 0.0001 -
0.0546 2900 0.0001 -
0.0556 2950 0.0001 -
0.0565 3000 0.0001 -
0.0575 3050 0.0001 -
0.0584 3100 0.0001 -
0.0594 3150 0.0001 -
0.0603 3200 0.0001 -
0.0612 3250 0.0001 -
0.0622 3300 0.0001 -
0.0631 3350 0.0001 -
0.0641 3400 0.0001 -
0.0650 3450 0.0001 -
0.0660 3500 0.0001 -
0.0669 3550 0.0005 -
0.0678 3600 0.0018 -
0.0688 3650 0.0026 -
0.0697 3700 0.0001 -
0.0707 3750 0.0001 -
0.0716 3800 0.0001 -
0.0726 3850 0.0001 -
0.0735 3900 0.0001 -
0.0744 3950 0.0 -
0.0754 4000 0.0 -
0.0763 4050 0.0 -
0.0773 4100 0.0 -
0.0782 4150 0.0 -
0.0791 4200 0.0 -
0.0801 4250 0.0 -
0.0810 4300 0.0 -
0.0820 4350 0.0 -
0.0829 4400 0.0 -
0.0839 4450 0.0 -
0.0848 4500 0.0 -
0.0857 4550 0.0 -
0.0867 4600 0.0 -
0.0876 4650 0.0 -
0.0886 4700 0.0 -
0.0895 4750 0.0 -
0.0905 4800 0.0 -
0.0914 4850 0.0 -
0.0923 4900 0.0 -
0.0933 4950 0.0 -
0.0942 5000 0.0 -
0.0952 5050 0.0 -
0.0961 5100 0.0 -
0.0970 5150 0.0 -
0.0980 5200 0.0 -
0.0989 5250 0.0 -
0.0999 5300 0.0 -
0.1008 5350 0.0 -
0.1018 5400 0.0 -
0.1027 5450 0.0 -
0.1036 5500 0.0 -
0.1046 5550 0.0 -
0.1055 5600 0.0 -
0.1065 5650 0.0 -
0.1074 5700 0.0 -
0.1084 5750 0.0 -
0.1093 5800 0.0 -
0.1102 5850 0.0 -
0.1112 5900 0.0 -
0.1121 5950 0.0 -
0.1131 6000 0.0 -
0.1140 6050 0.0 -
0.1150 6100 0.0 -
0.1159 6150 0.0 -
0.1168 6200 0.0 -
0.1178 6250 0.0 -
0.1187 6300 0.0 -
0.1197 6350 0.0 -
0.1206 6400 0.0 -
0.1215 6450 0.0 -
0.1225 6500 0.0 -
0.1234 6550 0.0 -
0.1244 6600 0.0 -
0.1253 6650 0.0 -
0.1263 6700 0.0 -
0.1272 6750 0.0 -
0.1281 6800 0.0 -
0.1291 6850 0.0 -
0.1300 6900 0.0 -
0.1310 6950 0.0 -
0.1319 7000 0.0 -
0.1329 7050 0.0 -
0.1338 7100 0.0 -
0.1347 7150 0.0 -
0.1357 7200 0.0 -
0.1366 7250 0.0 -
0.1376 7300 0.0 -
0.1385 7350 0.0 -
0.1394 7400 0.0 -
0.1404 7450 0.0 -
0.1413 7500 0.0 -
0.1423 7550 0.0 -
0.1432 7600 0.0 -
0.1442 7650 0.0 -
0.1451 7700 0.0 -
0.1460 7750 0.0 -
0.1470 7800 0.0 -
0.1479 7850 0.1766 -
0.1489 7900 0.0139 -
0.1498 7950 0.0029 -
0.1508 8000 0.0029 -
0.1517 8050 0.0008 -
0.1526 8100 0.0001 -
0.1536 8150 0.0002 -
0.1545 8200 0.0001 -
0.1555 8250 0.0 -
0.1564 8300 0.0 -
0.1574 8350 0.0 -
0.1583 8400 0.0 -
0.1592 8450 0.0 -
0.1602 8500 0.0 -
0.1611 8550 0.0 -
0.1621 8600 0.0 -
0.1630 8650 0.0 -
0.1639 8700 0.0 -
0.1649 8750 0.0 -
0.1658 8800 0.0 -
0.1668 8850 0.0 -
0.1677 8900 0.0 -
0.1687 8950 0.0 -
0.1696 9000 0.0 -
0.1705 9050 0.0 -
0.1715 9100 0.0 -
0.1724 9150 0.0 -
0.1734 9200 0.0 -
0.1743 9250 0.0 -
0.1753 9300 0.0 -
0.1762 9350 0.0 -
0.1771 9400 0.0 -
0.1781 9450 0.0 -
0.1790 9500 0.0 -
0.1800 9550 0.0 -
0.1809 9600 0.0 -
0.1818 9650 0.0 -
0.1828 9700 0.0 -
0.1837 9750 0.0 -
0.1847 9800 0.0 -
0.1856 9850 0.0 -
0.1866 9900 0.0 -
0.1875 9950 0.0 -
0.1884 10000 0.0 -
0.1894 10050 0.0 -
0.1903 10100 0.0 -
0.1913 10150 0.0 -
0.1922 10200 0.0 -
0.1932 10250 0.0 -
0.1941 10300 0.0 -
0.1950 10350 0.0 -
0.1960 10400 0.0 -
0.1969 10450 0.0 -
0.1979 10500 0.0 -
0.1988 10550 0.0 -
0.1998 10600 0.0 -
0.2007 10650 0.0 -
0.2016 10700 0.0 -
0.2026 10750 0.0 -
0.2035 10800 0.0 -
0.2045 10850 0.0 -
0.2054 10900 0.0 -
0.2063 10950 0.0 -
0.2073 11000 0.0 -
0.2082 11050 0.0 -
0.2092 11100 0.0 -
0.2101 11150 0.0 -
0.2111 11200 0.0 -
0.2120 11250 0.0 -
0.2129 11300 0.0 -
0.2139 11350 0.0 -
0.2148 11400 0.0 -
0.2158 11450 0.0 -
0.2167 11500 0.0 -
0.2177 11550 0.0 -
0.2186 11600 0.0 -
0.2195 11650 0.0 -
0.2205 11700 0.0 -
0.2214 11750 0.0 -
0.2224 11800 0.0 -
0.2233 11850 0.0 -
0.2242 11900 0.0 -
0.2252 11950 0.0 -
0.2261 12000 0.0 -
0.2271 12050 0.0 -
0.2280 12100 0.0 -
0.2290 12150 0.0 -
0.2299 12200 0.0 -
0.2308 12250 0.0 -
0.2318 12300 0.0 -
0.2327 12350 0.0 -
0.2337 12400 0.0 -
0.2346 12450 0.0 -
0.2356 12500 0.0 -
0.2365 12550 0.0 -
0.2374 12600 0.0 -
0.2384 12650 0.0 -
0.2393 12700 0.0 -
0.2403 12750 0.0 -
0.2412 12800 0.0 -
0.2422 12850 0.0 -
0.2431 12900 0.0 -
0.2440 12950 0.0 -
0.2450 13000 0.0 -
0.2459 13050 0.0 -
0.2469 13100 0.0 -
0.2478 13150 0.0 -
0.2487 13200 0.0 -
0.2497 13250 0.0 -
0.2506 13300 0.0 -
0.2516 13350 0.0 -
0.2525 13400 0.0 -
0.2535 13450 0.0 -
0.2544 13500 0.0 -
0.2553 13550 0.0 -
0.2563 13600 0.0 -
0.2572 13650 0.0 -
0.2582 13700 0.0 -
0.2591 13750 0.0 -
0.2601 13800 0.0 -
0.2610 13850 0.0 -
0.2619 13900 0.0 -
0.2629 13950 0.0 -
0.2638 14000 0.0 -
0.2648 14050 0.0 -
0.2657 14100 0.0 -
0.2666 14150 0.0 -
0.2676 14200 0.0 -
0.2685 14250 0.0 -
0.2695 14300 0.0 -
0.2704 14350 0.0 -
0.2714 14400 0.0 -
0.2723 14450 0.0 -
0.2732 14500 0.0 -
0.2742 14550 0.0 -
0.2751 14600 0.0 -
0.2761 14650 0.0 -
0.2770 14700 0.0001 -
0.2780 14750 0.0 -
0.2789 14800 0.0 -
0.2798 14850 0.0 -
0.2808 14900 0.0 -
0.2817 14950 0.0 -
0.2827 15000 0.0 -
0.2836 15050 0.0 -
0.2846 15100 0.0 -
0.2855 15150 0.0 -
0.2864 15200 0.0 -
0.2874 15250 0.0 -
0.2883 15300 0.0 -
0.2893 15350 0.0 -
0.2902 15400 0.0 -
0.2911 15450 0.0 -
0.2921 15500 0.0 -
0.2930 15550 0.0 -
0.2940 15600 0.0 -
0.2949 15650 0.0 -
0.2959 15700 0.0 -
0.2968 15750 0.0 -
0.2977 15800 0.0 -
0.2987 15850 0.0 -
0.2996 15900 0.0 -
0.3006 15950 0.0 -
0.3015 16000 0.0 -
0.3025 16050 0.0 -
0.3034 16100 0.0 -
0.3043 16150 0.0 -
0.3053 16200 0.0 -
0.3062 16250 0.0 -
0.3072 16300 0.0 -
0.3081 16350 0.0 -
0.3090 16400 0.0 -
0.3100 16450 0.0 -
0.3109 16500 0.0 -
0.3119 16550 0.0 -
0.3128 16600 0.0 -
0.3138 16650 0.0 -
0.3147 16700 0.0 -
0.3156 16750 0.0 -
0.3166 16800 0.0 -
0.3175 16850 0.0 -
0.3185 16900 0.0 -
0.3194 16950 0.0 -
0.3204 17000 0.0 -
0.3213 17050 0.0 -
0.3222 17100 0.0 -
0.3232 17150 0.0 -
0.3241 17200 0.0 -
0.3251 17250 0.0 -
0.3260 17300 0.0 -
0.3270 17350 0.0 -
0.3279 17400 0.0 -
0.3288 17450 0.0 -
0.3298 17500 0.0 -
0.3307 17550 0.0 -
0.3317 17600 0.0 -
0.3326 17650 0.0 -
0.3335 17700 0.0 -
0.3345 17750 0.0 -
0.3354 17800 0.0 -
0.3364 17850 0.0 -
0.3373 17900 0.0 -
0.3383 17950 0.0 -
0.3392 18000 0.0 -
0.3401 18050 0.0 -
0.3411 18100 0.0 -
0.3420 18150 0.0 -
0.3430 18200 0.0 -
0.3439 18250 0.0 -
0.3449 18300 0.0 -
0.3458 18350 0.0 -
0.3467 18400 0.0 -
0.3477 18450 0.0 -
0.3486 18500 0.0 -
0.3496 18550 0.0 -
0.3505 18600 0.0 -
0.3514 18650 0.0 -
0.3524 18700 0.0 -
0.3533 18750 0.0 -
0.3543 18800 0.0 -
0.3552 18850 0.0 -
0.3562 18900 0.0 -
0.3571 18950 0.0 -
0.3580 19000 0.0 -
0.3590 19050 0.0 -
0.3599 19100 0.0 -
0.3609 19150 0.0 -
0.3618 19200 0.0 -
0.3628 19250 0.0 -
0.3637 19300 0.0 -
0.3646 19350 0.0 -
0.3656 19400 0.0 -
0.3665 19450 0.0 -
0.3675 19500 0.0 -
0.3684 19550 0.0001 -
0.3694 19600 0.0001 -
0.3703 19650 0.0 -
0.3712 19700 0.0 -
0.3722 19750 0.0 -
0.3731 19800 0.0 -
0.3741 19850 0.0 -
0.3750 19900 0.0 -
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0.3769 20000 0.0 -
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0.3816 20250 0.0 -
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0.3957 21000 0.0 -
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0.4108 21800 0.0 -
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0.4127 21900 0.0 -
0.4136 21950 0.0 -
0.4146 22000 0.0 -
0.4155 22050 0.0 -
0.4165 22100 0.0 -
0.4174 22150 0.0 -
0.4183 22200 0.0 -
0.4193 22250 0.0 -
0.4202 22300 0.0 -
0.4212 22350 0.0 -
0.4221 22400 0.0 -
0.4231 22450 0.0 -
0.4240 22500 0.0 -
0.4249 22550 0.0 -
0.4259 22600 0.0 -
0.4268 22650 0.0 -
0.4278 22700 0.0 -
0.4287 22750 0.0 -
0.4297 22800 0.0 -
0.4306 22850 0.0 -
0.4315 22900 0.0 -
0.4325 22950 0.0 -
0.4334 23000 0.0 -
0.4344 23050 0.0 -
0.4353 23100 0.0 -
0.4362 23150 0.0 -
0.4372 23200 0.0 -
0.4381 23250 0.0 -
0.4391 23300 0.0 -
0.4400 23350 0.0 -
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0.4542 24100 0.0 -
0.4551 24150 0.0 -
0.4560 24200 0.0 -
0.4570 24250 0.0 -
0.4579 24300 0.0 -
0.4589 24350 0.0 -
0.4598 24400 0.0 -
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0.4636 24600 0.0 -
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0.4655 24700 0.0 -
0.4664 24750 0.0 -
0.4673 24800 0.0 -
0.4683 24850 0.0 -
0.4692 24900 0.0 -
0.4702 24950 0.0 -
0.4711 25000 0.0 -
0.4721 25050 0.0 -
0.4730 25100 0.0 -
0.4739 25150 0.0 -
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2.9614 157150 0.0 -
2.9623 157200 0.0 -
2.9633 157250 0.0 -
2.9642 157300 0.0 -
2.9652 157350 0.0 -
2.9661 157400 0.0 -
2.9671 157450 0.0 -
2.9680 157500 0.0 -
2.9689 157550 0.0 -
2.9699 157600 0.0 -
2.9708 157650 0.0 -
2.9718 157700 0.0 -
2.9727 157750 0.0 -
2.9737 157800 0.0 -
2.9746 157850 0.0 -
2.9755 157900 0.0 -
2.9765 157950 0.0 -
2.9774 158000 0.0 -
2.9784 158050 0.0 -
2.9793 158100 0.0 -
2.9803 158150 0.0 -
2.9812 158200 0.0 -
2.9821 158250 0.0 -
2.9831 158300 0.0 -
2.9840 158350 0.0 -
2.9850 158400 0.0 -
2.9859 158450 0.0 -
2.9868 158500 0.0 -
2.9878 158550 0.0 -
2.9887 158600 0.0 -
2.9897 158650 0.0 -
2.9906 158700 0.0 -
2.9916 158750 0.0 -
2.9925 158800 0.0 -
2.9934 158850 0.0 -
2.9944 158900 0.0 -
2.9953 158950 0.0 -
2.9963 159000 0.0 -
2.9972 159050 0.0 -
2.9982 159100 0.0 -
2.9991 159150 0.0 -

Framework Versions

  • Python: 3.11.11
  • SetFit: 1.1.1
  • 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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