metadata
base_model: desarrolloasesoreslocales/bert-leg-al-corpus
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
Que no estando conforme en absoluto con los hechos denunciados, se
interpone el presente
escrito en tiempo y forma en base a las siguientes
- text: >-
En primer lugar, indicar algo fundamental y es el hecho de que mi vehículo
NO estaba estacionado,
sino que se encontraba parado, puesto que reunía todos los requisitos
legales
establecidos en el anexo l, apartado 81 del RD Legislativo 6/2015, de 30
de octubre, donde se
recoge que la_parada_es la inmovilización de un vehículo durante un tiempo
inferior a dos
minutos, sin que el conductor pueda abandonarlo. Es decir, que se estaría
sancionando un
hecho no constitutivo de infracción administrativa, y, por tanto,
vulnerando el principio de tipicidad
recogido en el artículo 27.1 de la Ley 40/2015, de 1 de octubre, de
Régimen Jurídico del
Sector Público, puesto que solo constituyen infracciones administrativas
las vulneraciones del
ordenamiento jurídico previstas como tales infracciones en una Ley.
- text: >-
QUINTA.- Hay que alegar que con el presente expediente se vulnera el
PRINCIPIO DE PROPORCIONALIDAD que debe regir a la hora de la imposición de
las sanciones.
- text: >-
Que SE SOLICITA LA APERTURA DEL PERÍODO DE PRUEBA conforme indica el
artículo 95 del RD Legislativo 6/2015, de 30 de octubre, así como el
artículo 13 del R.D. 320/94 por
el que se aprueba el Reglamento del Procedimiento Sancionador en materia
de tráfico, circulación de
vehículos a motor y seguridad vial, y, en su virtud, se propone la
práctica de los siguientes medios
de prueba, esenciales para la comprobación de los hechos y mi defensa:
* Documento Fotográfico del presunto vehículo infractor para verificar si
el mismo circulaba
a velocidad superior a la permitida, el cual es exigible que conste de al
menos dos fotogramas
tomados en diferentes instantes, debiendo figurar en los citados
fotogramas, los datos necesarios
para relacionar dicha prueba con el cinemómetro que, en principio, los
obtuvo; así, como, en caso de
tratarse de un radar instalado en un vehículo, deberá constar la velocidad
a la que circula el mismo.
- text: |-
En relación a la cuantía económica impuesta y en base al principio de
proporcionalidad, creo que es excesiva.
inference: true
model-index:
- name: SetFit with desarrolloasesoreslocales/bert-leg-al-corpus
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.9126984126984127
name: Accuracy
SetFit with desarrolloasesoreslocales/bert-leg-al-corpus
This is a SetFit model that can be used for Text Classification. This SetFit model uses desarrolloasesoreslocales/bert-leg-al-corpus 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: desarrolloasesoreslocales/bert-leg-al-corpus
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 21 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 |
---|---|
2060 |
|
2037 |
|
2027 |
|
2002 |
|
237 |
|
2022 |
|
2026 |
|
2039 |
|
2038 |
|
353 |
|
304 |
|
2001 |
|
2014 |
|
49 |
|
78 |
|
1001 |
|
357 |
|
2017 |
|
2013 |
|
994 |
|
2010 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.9127 |
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("desarrolloasesoreslocales/bert-leg-al-setfit")
# Run inference
preds = model("En relación a la cuantía económica impuesta y en base al principio de
proporcionalidad, creo que es excesiva.")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 3 | 30.9507 | 213 |
Label | Training Sample Count |
---|---|
49 | 34 |
78 | 34 |
237 | 33 |
304 | 34 |
353 | 34 |
357 | 34 |
994 | 34 |
1001 | 34 |
2001 | 34 |
2002 | 34 |
2010 | 34 |
2013 | 34 |
2014 | 34 |
2017 | 34 |
2022 | 33 |
2026 | 34 |
2027 | 33 |
2037 | 33 |
2038 | 34 |
2039 | 34 |
2060 | 34 |
Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 60
- body_learning_rate: (1e-06, 1e-06)
- head_learning_rate: 0.0002
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: True
- use_amp: True
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0004 | 1 | 0.3326 | - |
0.0075 | 20 | 0.352 | 0.3591 |
0.0150 | 40 | 0.3662 | 0.3591 |
0.0225 | 60 | 0.3348 | 0.3589 |
0.0300 | 80 | 0.3773 | 0.3588 |
0.0376 | 100 | 0.3523 | 0.3585 |
0.0451 | 120 | 0.36 | 0.3582 |
0.0526 | 140 | 0.3726 | 0.3579 |
0.0601 | 160 | 0.3715 | 0.3575 |
0.0676 | 180 | 0.3519 | 0.357 |
0.0751 | 200 | 0.3611 | 0.3565 |
0.0826 | 220 | 0.3635 | 0.356 |
0.0901 | 240 | 0.3594 | 0.3553 |
0.0976 | 260 | 0.3383 | 0.3546 |
0.1051 | 280 | 0.3393 | 0.3539 |
0.1127 | 300 | 0.3299 | 0.353 |
0.1202 | 320 | 0.3044 | 0.3522 |
0.1277 | 340 | 0.3433 | 0.3512 |
0.1352 | 360 | 0.3564 | 0.3503 |
0.1427 | 380 | 0.3365 | 0.3493 |
0.1502 | 400 | 0.3721 | 0.3481 |
0.1577 | 420 | 0.3636 | 0.3469 |
0.1652 | 440 | 0.3661 | 0.3457 |
0.1727 | 460 | 0.351 | 0.3443 |
0.1802 | 480 | 0.327 | 0.3433 |
0.1878 | 500 | 0.3447 | 0.3417 |
0.1953 | 520 | 0.3271 | 0.34 |
0.2028 | 540 | 0.3364 | 0.3383 |
0.2103 | 560 | 0.298 | 0.3366 |
0.2178 | 580 | 0.3392 | - |
0.0004 | 1 | 0.3057 | - |
0.0075 | 20 | 0.3281 | 0.3341 |
0.0150 | 40 | 0.3415 | 0.332 |
0.0225 | 60 | 0.3017 | 0.3285 |
0.0300 | 80 | 0.3462 | 0.3232 |
0.0376 | 100 | 0.3056 | 0.3156 |
0.0451 | 120 | 0.3111 | 0.3063 |
0.0526 | 140 | 0.3136 | 0.2955 |
0.0601 | 160 | 0.3067 | 0.2802 |
0.0676 | 180 | 0.2645 | 0.2593 |
0.0751 | 200 | 0.2517 | 0.233 |
0.0826 | 220 | 0.212 | 0.2077 |
0.0901 | 240 | 0.1686 | 0.1881 |
0.0976 | 260 | 0.1783 | 0.1731 |
0.1051 | 280 | 0.1268 | 0.1592 |
0.1127 | 300 | 0.137 | 0.1474 |
0.1202 | 320 | 0.1372 | 0.1376 |
0.1277 | 340 | 0.146 | 0.1287 |
0.1352 | 360 | 0.1579 | 0.1226 |
0.1427 | 380 | 0.1037 | 0.1162 |
0.1502 | 400 | 0.1391 | 0.1105 |
0.1577 | 420 | 0.136 | 0.1053 |
0.1652 | 440 | 0.1212 | 0.1013 |
0.1727 | 460 | 0.128 | 0.098 |
0.1802 | 480 | 0.0701 | 0.0944 |
0.1878 | 500 | 0.1274 | 0.0913 |
0.1953 | 520 | 0.1235 | 0.0882 |
0.2028 | 540 | 0.0761 | 0.0857 |
0.2103 | 560 | 0.0942 | 0.0833 |
0.2178 | 580 | 0.1046 | 0.0815 |
0.2253 | 600 | 0.0812 | 0.0795 |
0.2328 | 620 | 0.0888 | 0.0776 |
0.2403 | 640 | 0.0867 | 0.076 |
0.2478 | 660 | 0.0997 | 0.0743 |
0.2554 | 680 | 0.1344 | 0.0728 |
0.2629 | 700 | 0.07 | 0.0716 |
0.2704 | 720 | 0.1014 | 0.0703 |
0.2779 | 740 | 0.125 | 0.0692 |
0.2854 | 760 | 0.078 | 0.0674 |
0.2929 | 780 | 0.062 | 0.0666 |
0.3004 | 800 | 0.1564 | 0.0659 |
0.3079 | 820 | 0.0957 | 0.0648 |
0.3154 | 840 | 0.1069 | 0.0635 |
0.3229 | 860 | 0.0982 | 0.0622 |
0.3305 | 880 | 0.0384 | 0.0608 |
0.3380 | 900 | 0.1394 | 0.0597 |
0.3455 | 920 | 0.0349 | 0.0591 |
0.3530 | 940 | 0.087 | 0.0579 |
0.3605 | 960 | 0.0878 | 0.0571 |
0.3680 | 980 | 0.0695 | 0.0565 |
0.3755 | 1000 | 0.0437 | 0.0556 |
0.3830 | 1020 | 0.0431 | 0.0551 |
0.3905 | 1040 | 0.0391 | 0.0545 |
0.3980 | 1060 | 0.0936 | 0.0544 |
0.4056 | 1080 | 0.066 | 0.054 |
0.4131 | 1100 | 0.1169 | 0.0534 |
0.4206 | 1120 | 0.0445 | 0.0525 |
0.4281 | 1140 | 0.0365 | 0.0519 |
0.4356 | 1160 | 0.0714 | 0.0517 |
0.4431 | 1180 | 0.043 | 0.051 |
0.4506 | 1200 | 0.0754 | 0.0506 |
0.4581 | 1220 | 0.0592 | 0.0501 |
0.4656 | 1240 | 0.0775 | 0.0498 |
0.4732 | 1260 | 0.0601 | 0.0494 |
0.4807 | 1280 | 0.0903 | 0.0486 |
0.4882 | 1300 | 0.0518 | 0.0481 |
0.4957 | 1320 | 0.0462 | 0.0477 |
0.5032 | 1340 | 0.0413 | 0.0477 |
0.5107 | 1360 | 0.044 | 0.0473 |
0.5182 | 1380 | 0.0724 | 0.047 |
0.5257 | 1400 | 0.0433 | 0.0468 |
0.5332 | 1420 | 0.0511 | 0.0465 |
0.5407 | 1440 | 0.065 | 0.0462 |
0.5483 | 1460 | 0.0611 | 0.0458 |
0.5558 | 1480 | 0.0419 | 0.0459 |
0.5633 | 1500 | 0.0595 | 0.0456 |
0.5708 | 1520 | 0.0718 | 0.0452 |
0.5783 | 1540 | 0.0577 | 0.0449 |
0.5858 | 1560 | 0.0515 | 0.0447 |
0.5933 | 1580 | 0.0388 | 0.0444 |
0.6008 | 1600 | 0.0764 | 0.0443 |
0.6083 | 1620 | 0.0876 | 0.0441 |
0.6158 | 1640 | 0.0361 | 0.0436 |
0.6234 | 1660 | 0.0549 | 0.0435 |
0.6309 | 1680 | 0.0207 | 0.0434 |
0.6384 | 1700 | 0.0366 | 0.0434 |
0.6459 | 1720 | 0.0342 | 0.0433 |
0.6534 | 1740 | 0.0313 | 0.043 |
0.6609 | 1760 | 0.0342 | 0.0432 |
0.6684 | 1780 | 0.0744 | 0.0429 |
0.6759 | 1800 | 0.0282 | 0.0428 |
0.6834 | 1820 | 0.0479 | 0.0429 |
0.6910 | 1840 | 0.0497 | 0.0426 |
0.6985 | 1860 | 0.0513 | 0.0426 |
0.7060 | 1880 | 0.02 | 0.0423 |
0.7135 | 1900 | 0.0238 | 0.0424 |
0.7210 | 1920 | 0.0446 | 0.0422 |
0.7285 | 1940 | 0.0853 | 0.0419 |
0.7360 | 1960 | 0.0234 | 0.0416 |
0.7435 | 1980 | 0.0646 | 0.0416 |
0.7510 | 2000 | 0.0387 | 0.0419 |
0.7585 | 2020 | 0.0419 | 0.0416 |
0.7661 | 2040 | 0.0326 | 0.0419 |
0.7736 | 2060 | 0.0344 | 0.0414 |
0.7811 | 2080 | 0.0246 | 0.041 |
0.7886 | 2100 | 0.0383 | 0.0408 |
0.7961 | 2120 | 0.0315 | 0.0407 |
0.8036 | 2140 | 0.0408 | 0.0406 |
0.8111 | 2160 | 0.0293 | 0.0403 |
0.8186 | 2180 | 0.0242 | 0.0405 |
0.8261 | 2200 | 0.0317 | 0.0399 |
0.8336 | 2220 | 0.0416 | 0.0396 |
0.8412 | 2240 | 0.0503 | 0.0395 |
0.8487 | 2260 | 0.0468 | 0.0394 |
0.8562 | 2280 | 0.0231 | 0.0395 |
0.8637 | 2300 | 0.0363 | 0.0394 |
0.8712 | 2320 | 0.0423 | 0.0395 |
0.8787 | 2340 | 0.0321 | 0.0391 |
0.8862 | 2360 | 0.0226 | 0.0391 |
0.8937 | 2380 | 0.0567 | 0.039 |
0.9012 | 2400 | 0.0421 | 0.039 |
0.9087 | 2420 | 0.0462 | 0.0389 |
0.9163 | 2440 | 0.0109 | 0.0388 |
0.9238 | 2460 | 0.0209 | 0.039 |
0.9313 | 2480 | 0.0133 | 0.0387 |
0.9388 | 2500 | 0.0309 | 0.0386 |
0.9463 | 2520 | 0.0179 | 0.0386 |
0.9538 | 2540 | 0.0288 | 0.0387 |
0.9613 | 2560 | 0.0434 | 0.0388 |
0.9688 | 2580 | 0.0307 | 0.0387 |
0.9763 | 2600 | 0.0396 | 0.039 |
0.9839 | 2620 | 0.0418 | 0.0385 |
0.9914 | 2640 | 0.0115 | 0.0385 |
0.9989 | 2660 | 0.0416 | 0.0386 |
1.0064 | 2680 | 0.0236 | 0.0385 |
1.0139 | 2700 | 0.0302 | 0.0385 |
1.0214 | 2720 | 0.0261 | 0.0385 |
1.0289 | 2740 | 0.0184 | 0.0388 |
1.0364 | 2760 | 0.0364 | 0.0385 |
1.0439 | 2780 | 0.0253 | 0.0385 |
1.0514 | 2800 | 0.0271 | 0.0385 |
1.0590 | 2820 | 0.0056 | 0.0385 |
1.0665 | 2840 | 0.0327 | 0.0384 |
1.0740 | 2860 | 0.0237 | 0.0381 |
1.0815 | 2880 | 0.0375 | 0.0383 |
1.0890 | 2900 | 0.0204 | 0.038 |
1.0965 | 2920 | 0.0551 | 0.0379 |
1.1040 | 2940 | 0.0274 | 0.038 |
1.1115 | 2960 | 0.0136 | 0.038 |
1.1190 | 2980 | 0.0193 | 0.0381 |
1.1265 | 3000 | 0.0449 | 0.0376 |
1.1341 | 3020 | 0.0403 | 0.0376 |
1.1416 | 3040 | 0.0237 | 0.0372 |
1.1491 | 3060 | 0.0133 | 0.037 |
1.1566 | 3080 | 0.0313 | 0.037 |
1.1641 | 3100 | 0.0205 | 0.0369 |
1.1716 | 3120 | 0.0274 | 0.0371 |
1.1791 | 3140 | 0.0178 | 0.0372 |
1.1866 | 3160 | 0.0258 | 0.0375 |
1.1941 | 3180 | 0.0466 | 0.0371 |
1.2017 | 3200 | 0.0258 | 0.037 |
1.2092 | 3220 | 0.0277 | 0.0371 |
1.2167 | 3240 | 0.0137 | 0.0371 |
1.2242 | 3260 | 0.0232 | 0.0372 |
1.2317 | 3280 | 0.0267 | 0.0371 |
1.2392 | 3300 | 0.0126 | 0.0375 |
1.2467 | 3320 | 0.0514 | 0.0372 |
1.2542 | 3340 | 0.0175 | 0.0373 |
1.2617 | 3360 | 0.0188 | 0.0374 |
1.2692 | 3380 | 0.0137 | 0.0372 |
1.2768 | 3400 | 0.035 | 0.0372 |
1.2843 | 3420 | 0.0309 | 0.0371 |
1.2918 | 3440 | 0.0462 | 0.0373 |
1.2993 | 3460 | 0.0388 | 0.0371 |
1.3068 | 3480 | 0.0573 | 0.0372 |
1.3143 | 3500 | 0.02 | 0.0373 |
1.3218 | 3520 | 0.0376 | 0.0372 |
1.3293 | 3540 | 0.0165 | 0.0374 |
1.3368 | 3560 | 0.0118 | 0.0372 |
1.3443 | 3580 | 0.0167 | 0.0374 |
1.3519 | 3600 | 0.0137 | 0.0373 |
1.3594 | 3620 | 0.0044 | 0.0371 |
1.3669 | 3640 | 0.0085 | 0.037 |
1.3744 | 3660 | 0.0336 | 0.0368 |
1.3819 | 3680 | 0.0217 | 0.0369 |
1.3894 | 3700 | 0.0395 | 0.0369 |
1.3969 | 3720 | 0.033 | 0.0371 |
1.4044 | 3740 | 0.0173 | 0.0368 |
1.4119 | 3760 | 0.0126 | 0.0367 |
1.4195 | 3780 | 0.012 | 0.0367 |
1.4270 | 3800 | 0.0364 | 0.0366 |
1.4345 | 3820 | 0.0238 | 0.0366 |
1.4420 | 3840 | 0.0199 | 0.0365 |
1.4495 | 3860 | 0.0152 | 0.0368 |
1.4570 | 3880 | 0.035 | 0.0366 |
1.4645 | 3900 | 0.0413 | 0.0365 |
1.4720 | 3920 | 0.0208 | 0.0366 |
1.4795 | 3940 | 0.0074 | 0.0366 |
1.4870 | 3960 | 0.0116 | 0.0366 |
1.4946 | 3980 | 0.0088 | 0.0365 |
1.5021 | 4000 | 0.0264 | 0.0367 |
1.5096 | 4020 | 0.0161 | 0.0363 |
1.5171 | 4040 | 0.0549 | 0.0362 |
1.5246 | 4060 | 0.0166 | 0.0362 |
1.5321 | 4080 | 0.0162 | 0.0361 |
1.5396 | 4100 | 0.0174 | 0.0363 |
1.5471 | 4120 | 0.0231 | 0.0362 |
1.5546 | 4140 | 0.0143 | 0.0364 |
1.5621 | 4160 | 0.0168 | 0.0361 |
1.5697 | 4180 | 0.0149 | 0.0361 |
1.5772 | 4200 | 0.013 | 0.0361 |
1.5847 | 4220 | 0.0122 | 0.0361 |
1.5922 | 4240 | 0.0061 | 0.0362 |
1.5997 | 4260 | 0.0088 | 0.0361 |
1.6072 | 4280 | 0.0071 | 0.0364 |
1.6147 | 4300 | 0.0168 | 0.0361 |
1.6222 | 4320 | 0.0333 | 0.0361 |
1.6297 | 4340 | 0.0487 | 0.0362 |
1.6373 | 4360 | 0.0177 | 0.0361 |
1.6448 | 4380 | 0.0086 | 0.0361 |
1.6523 | 4400 | 0.0244 | 0.036 |
1.6598 | 4420 | 0.0294 | 0.0362 |
1.6673 | 4440 | 0.0134 | 0.0359 |
1.6748 | 4460 | 0.0081 | 0.0358 |
1.6823 | 4480 | 0.011 | 0.0357 |
1.6898 | 4500 | 0.0453 | 0.0357 |
1.6973 | 4520 | 0.0269 | 0.0358 |
1.7048 | 4540 | 0.039 | 0.0357 |
1.7124 | 4560 | 0.0346 | 0.036 |
1.7199 | 4580 | 0.0164 | 0.0357 |
1.7274 | 4600 | 0.0081 | 0.0356 |
1.7349 | 4620 | 0.0343 | 0.0357 |
1.7424 | 4640 | 0.0071 | 0.0356 |
1.7499 | 4660 | 0.0349 | 0.0357 |
1.7574 | 4680 | 0.0128 | 0.0356 |
1.7649 | 4700 | 0.0128 | 0.0359 |
1.7724 | 4720 | 0.0375 | 0.0356 |
1.7799 | 4740 | 0.0257 | 0.0356 |
1.7875 | 4760 | 0.0514 | 0.0355 |
1.7950 | 4780 | 0.0077 | 0.0354 |
1.8025 | 4800 | 0.0281 | 0.0355 |
1.8100 | 4820 | 0.0236 | 0.0354 |
1.8175 | 4840 | 0.0097 | 0.0357 |
1.8250 | 4860 | 0.0195 | 0.0354 |
1.8325 | 4880 | 0.0057 | 0.0353 |
1.8400 | 4900 | 0.0213 | 0.0354 |
1.8475 | 4920 | 0.0059 | 0.0353 |
1.8551 | 4940 | 0.0237 | 0.0354 |
1.8626 | 4960 | 0.0189 | 0.0354 |
1.8701 | 4980 | 0.0352 | 0.0356 |
1.8776 | 5000 | 0.0069 | 0.0354 |
1.8851 | 5020 | 0.0067 | 0.0354 |
1.8926 | 5040 | 0.0151 | 0.0354 |
1.9001 | 5060 | 0.0107 | 0.0354 |
1.9076 | 5080 | 0.026 | 0.0355 |
1.9151 | 5100 | 0.0764 | 0.0354 |
1.9226 | 5120 | 0.0372 | 0.0357 |
1.9302 | 5140 | 0.0162 | 0.0354 |
1.9377 | 5160 | 0.0276 | 0.0353 |
1.9452 | 5180 | 0.0306 | 0.0354 |
1.9527 | 5200 | 0.0211 | 0.0353 |
1.9602 | 5220 | 0.0252 | 0.0354 |
1.9677 | 5240 | 0.0247 | 0.0354 |
1.9752 | 5260 | 0.0239 | 0.0356 |
1.9827 | 5280 | 0.0245 | 0.0354 |
1.9902 | 5300 | 0.0144 | 0.0353 |
1.9977 | 5320 | 0.0153 | 0.0353 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.40.2
- PyTorch: 2.3.1+cu121
- Datasets: 2.20.0
- 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}
}