metadata
base_model: klue/roberta-base
library_name: setfit
metrics:
- metric
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: '[자체제작] 14k 콩사다리 체인 반지 핑크_D style(1푼 굵기)_10호 (주)제이디아이인터내셔널'
- text: 실리콘 동전 지갑 심플 캐릭터 [on] 블랙캣(동전지갑) 비150
- text: 체크 남자 베레모 아빠 모자 헌팅캡 패션 빵모자 외출 베이지체크 (4JS) 포제이스
- text: TIMBERLAND 남성 앨번 6인치 워터프루프 워커부츠_TB0A1OIZC641 070(250) 비츠컴퍼니
- text: 라인댄스화 헬스화 스포츠 여성 재즈화 댄스화 볼룸 모던 미드힐 37_블랙 스트레이트 3.5cm/굽(메쉬) 사랑옵다
inference: true
model-index:
- name: SetFit with klue/roberta-base
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: metric
value: 0.9385943021823656
name: Metric
SetFit with klue/roberta-base
This is a SetFit model that can be used for Text Classification. This SetFit model uses klue/roberta-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:
- 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: klue/roberta-base
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 17 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 |
---|---|
2.0 |
|
9.0 |
|
15.0 |
|
13.0 |
|
1.0 |
|
7.0 |
|
11.0 |
|
4.0 |
|
14.0 |
|
0.0 |
|
16.0 |
|
8.0 |
|
5.0 |
|
10.0 |
|
6.0 |
|
3.0 |
|
12.0 |
|
Evaluation
Metrics
Label | Metric |
---|---|
all | 0.9386 |
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("mini1013/master_item_ac")
# Run inference
preds = model("실리콘 동전 지갑 심플 캐릭터 [on] 블랙캣(동전지갑) 비150")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 3 | 10.2537 | 30 |
Label | Training Sample Count |
---|---|
0.0 | 450 |
1.0 | 650 |
2.0 | 650 |
3.0 | 150 |
4.0 | 300 |
5.0 | 120 |
6.0 | 224 |
7.0 | 350 |
8.0 | 100 |
9.0 | 467 |
10.0 | 500 |
11.0 | 600 |
12.0 | 150 |
13.0 | 450 |
14.0 | 400 |
15.0 | 1000 |
16.0 | 250 |
Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (20, 20)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 40
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0009 | 1 | 0.407 | - |
0.0469 | 50 | 0.3772 | - |
0.0939 | 100 | 0.3062 | - |
0.1408 | 150 | 0.2861 | - |
0.1878 | 200 | 0.2513 | - |
0.2347 | 250 | 0.2284 | - |
0.2817 | 300 | 0.1952 | - |
0.3286 | 350 | 0.149 | - |
0.3756 | 400 | 0.1154 | - |
0.4225 | 450 | 0.1042 | - |
0.4695 | 500 | 0.0802 | - |
0.5164 | 550 | 0.0765 | - |
0.5634 | 600 | 0.0767 | - |
0.6103 | 650 | 0.0475 | - |
0.6573 | 700 | 0.0535 | - |
0.7042 | 750 | 0.0293 | - |
0.7512 | 800 | 0.0388 | - |
0.7981 | 850 | 0.0156 | - |
0.8451 | 900 | 0.0348 | - |
0.8920 | 950 | 0.0241 | - |
0.9390 | 1000 | 0.023 | - |
0.9859 | 1050 | 0.0166 | - |
1.0329 | 1100 | 0.0124 | - |
1.0798 | 1150 | 0.0139 | - |
1.1268 | 1200 | 0.0122 | - |
1.1737 | 1250 | 0.0111 | - |
1.2207 | 1300 | 0.0062 | - |
1.2676 | 1350 | 0.0106 | - |
1.3146 | 1400 | 0.0112 | - |
1.3615 | 1450 | 0.0137 | - |
1.4085 | 1500 | 0.0154 | - |
1.4554 | 1550 | 0.0185 | - |
1.5023 | 1600 | 0.0248 | - |
1.5493 | 1650 | 0.0128 | - |
1.5962 | 1700 | 0.018 | - |
1.6432 | 1750 | 0.0013 | - |
1.6901 | 1800 | 0.0151 | - |
1.7371 | 1850 | 0.0208 | - |
1.7840 | 1900 | 0.0076 | - |
1.8310 | 1950 | 0.0138 | - |
1.8779 | 2000 | 0.0133 | - |
1.9249 | 2050 | 0.0131 | - |
1.9718 | 2100 | 0.0123 | - |
2.0188 | 2150 | 0.0165 | - |
2.0657 | 2200 | 0.0084 | - |
2.1127 | 2250 | 0.0062 | - |
2.1596 | 2300 | 0.0068 | - |
2.2066 | 2350 | 0.0023 | - |
2.2535 | 2400 | 0.006 | - |
2.3005 | 2450 | 0.0048 | - |
2.3474 | 2500 | 0.0016 | - |
2.3944 | 2550 | 0.0046 | - |
2.4413 | 2600 | 0.001 | - |
2.4883 | 2650 | 0.0022 | - |
2.5352 | 2700 | 0.0014 | - |
2.5822 | 2750 | 0.0004 | - |
2.6291 | 2800 | 0.0002 | - |
2.6761 | 2850 | 0.0004 | - |
2.7230 | 2900 | 0.0016 | - |
2.7700 | 2950 | 0.0018 | - |
2.8169 | 3000 | 0.0004 | - |
2.8638 | 3050 | 0.0001 | - |
2.9108 | 3100 | 0.0002 | - |
2.9577 | 3150 | 0.0018 | - |
3.0047 | 3200 | 0.0019 | - |
3.0516 | 3250 | 0.0001 | - |
3.0986 | 3300 | 0.0011 | - |
3.1455 | 3350 | 0.0001 | - |
3.1925 | 3400 | 0.0001 | - |
3.2394 | 3450 | 0.0002 | - |
3.2864 | 3500 | 0.0007 | - |
3.3333 | 3550 | 0.0001 | - |
3.3803 | 3600 | 0.0002 | - |
3.4272 | 3650 | 0.0001 | - |
3.4742 | 3700 | 0.0011 | - |
3.5211 | 3750 | 0.0013 | - |
3.5681 | 3800 | 0.0014 | - |
3.6150 | 3850 | 0.0001 | - |
3.6620 | 3900 | 0.0001 | - |
3.7089 | 3950 | 0.0002 | - |
3.7559 | 4000 | 0.0001 | - |
3.8028 | 4050 | 0.0014 | - |
3.8498 | 4100 | 0.0002 | - |
3.8967 | 4150 | 0.0001 | - |
3.9437 | 4200 | 0.0 | - |
3.9906 | 4250 | 0.0 | - |
4.0376 | 4300 | 0.0001 | - |
4.0845 | 4350 | 0.0002 | - |
4.1315 | 4400 | 0.0 | - |
4.1784 | 4450 | 0.0001 | - |
4.2254 | 4500 | 0.0 | - |
4.2723 | 4550 | 0.0 | - |
4.3192 | 4600 | 0.0003 | - |
4.3662 | 4650 | 0.0007 | - |
4.4131 | 4700 | 0.0 | - |
4.4601 | 4750 | 0.0001 | - |
4.5070 | 4800 | 0.0011 | - |
4.5540 | 4850 | 0.0003 | - |
4.6009 | 4900 | 0.0005 | - |
4.6479 | 4950 | 0.0001 | - |
4.6948 | 5000 | 0.0001 | - |
4.7418 | 5050 | 0.0001 | - |
4.7887 | 5100 | 0.0001 | - |
4.8357 | 5150 | 0.0 | - |
4.8826 | 5200 | 0.0 | - |
4.9296 | 5250 | 0.0 | - |
4.9765 | 5300 | 0.0001 | - |
5.0235 | 5350 | 0.0 | - |
5.0704 | 5400 | 0.0 | - |
5.1174 | 5450 | 0.0 | - |
5.1643 | 5500 | 0.0 | - |
5.2113 | 5550 | 0.0 | - |
5.2582 | 5600 | 0.0001 | - |
5.3052 | 5650 | 0.0 | - |
5.3521 | 5700 | 0.0 | - |
5.3991 | 5750 | 0.0 | - |
5.4460 | 5800 | 0.0 | - |
5.4930 | 5850 | 0.0 | - |
5.5399 | 5900 | 0.0 | - |
5.5869 | 5950 | 0.0 | - |
5.6338 | 6000 | 0.0 | - |
5.6808 | 6050 | 0.0 | - |
5.7277 | 6100 | 0.0 | - |
5.7746 | 6150 | 0.0 | - |
5.8216 | 6200 | 0.0 | - |
5.8685 | 6250 | 0.0 | - |
5.9155 | 6300 | 0.0001 | - |
5.9624 | 6350 | 0.0004 | - |
6.0094 | 6400 | 0.0007 | - |
6.0563 | 6450 | 0.0 | - |
6.1033 | 6500 | 0.0001 | - |
6.1502 | 6550 | 0.0 | - |
6.1972 | 6600 | 0.0001 | - |
6.2441 | 6650 | 0.0 | - |
6.2911 | 6700 | 0.0 | - |
6.3380 | 6750 | 0.0009 | - |
6.3850 | 6800 | 0.0 | - |
6.4319 | 6850 | 0.0001 | - |
6.4789 | 6900 | 0.0 | - |
6.5258 | 6950 | 0.0001 | - |
6.5728 | 7000 | 0.0 | - |
6.6197 | 7050 | 0.0 | - |
6.6667 | 7100 | 0.0 | - |
6.7136 | 7150 | 0.0 | - |
6.7606 | 7200 | 0.0001 | - |
6.8075 | 7250 | 0.0 | - |
6.8545 | 7300 | 0.0 | - |
6.9014 | 7350 | 0.0 | - |
6.9484 | 7400 | 0.0 | - |
6.9953 | 7450 | 0.0 | - |
7.0423 | 7500 | 0.0 | - |
7.0892 | 7550 | 0.0 | - |
7.1362 | 7600 | 0.0 | - |
7.1831 | 7650 | 0.0 | - |
7.2300 | 7700 | 0.0 | - |
7.2770 | 7750 | 0.0001 | - |
7.3239 | 7800 | 0.0 | - |
7.3709 | 7850 | 0.0 | - |
7.4178 | 7900 | 0.0 | - |
7.4648 | 7950 | 0.0 | - |
7.5117 | 8000 | 0.0 | - |
7.5587 | 8050 | 0.0 | - |
7.6056 | 8100 | 0.0 | - |
7.6526 | 8150 | 0.0024 | - |
7.6995 | 8200 | 0.0 | - |
7.7465 | 8250 | 0.0 | - |
7.7934 | 8300 | 0.0 | - |
7.8404 | 8350 | 0.0 | - |
7.8873 | 8400 | 0.0 | - |
7.9343 | 8450 | 0.0 | - |
7.9812 | 8500 | 0.0 | - |
8.0282 | 8550 | 0.0 | - |
8.0751 | 8600 | 0.0 | - |
8.1221 | 8650 | 0.0 | - |
8.1690 | 8700 | 0.0 | - |
8.2160 | 8750 | 0.0 | - |
8.2629 | 8800 | 0.0 | - |
8.3099 | 8850 | 0.0 | - |
8.3568 | 8900 | 0.0 | - |
8.4038 | 8950 | 0.0 | - |
8.4507 | 9000 | 0.0 | - |
8.4977 | 9050 | 0.0 | - |
8.5446 | 9100 | 0.0 | - |
8.5915 | 9150 | 0.0 | - |
8.6385 | 9200 | 0.0002 | - |
8.6854 | 9250 | 0.0003 | - |
8.7324 | 9300 | 0.0005 | - |
8.7793 | 9350 | 0.0001 | - |
8.8263 | 9400 | 0.0001 | - |
8.8732 | 9450 | 0.0001 | - |
8.9202 | 9500 | 0.0 | - |
8.9671 | 9550 | 0.0 | - |
9.0141 | 9600 | 0.0001 | - |
9.0610 | 9650 | 0.0001 | - |
9.1080 | 9700 | 0.0 | - |
9.1549 | 9750 | 0.0 | - |
9.2019 | 9800 | 0.0001 | - |
9.2488 | 9850 | 0.0 | - |
9.2958 | 9900 | 0.0 | - |
9.3427 | 9950 | 0.0 | - |
9.3897 | 10000 | 0.0 | - |
9.4366 | 10050 | 0.0 | - |
9.4836 | 10100 | 0.0 | - |
9.5305 | 10150 | 0.0 | - |
9.5775 | 10200 | 0.0 | - |
9.6244 | 10250 | 0.0 | - |
9.6714 | 10300 | 0.0 | - |
9.7183 | 10350 | 0.0 | - |
9.7653 | 10400 | 0.0 | - |
9.8122 | 10450 | 0.0 | - |
9.8592 | 10500 | 0.0016 | - |
9.9061 | 10550 | 0.0 | - |
9.9531 | 10600 | 0.0 | - |
10.0 | 10650 | 0.0 | - |
10.0469 | 10700 | 0.0003 | - |
10.0939 | 10750 | 0.0 | - |
10.1408 | 10800 | 0.0 | - |
10.1878 | 10850 | 0.0 | - |
10.2347 | 10900 | 0.0 | - |
10.2817 | 10950 | 0.0 | - |
10.3286 | 11000 | 0.0 | - |
10.3756 | 11050 | 0.0 | - |
10.4225 | 11100 | 0.0 | - |
10.4695 | 11150 | 0.0 | - |
10.5164 | 11200 | 0.0 | - |
10.5634 | 11250 | 0.0 | - |
10.6103 | 11300 | 0.0 | - |
10.6573 | 11350 | 0.0 | - |
10.7042 | 11400 | 0.0 | - |
10.7512 | 11450 | 0.0 | - |
10.7981 | 11500 | 0.0 | - |
10.8451 | 11550 | 0.0 | - |
10.8920 | 11600 | 0.0 | - |
10.9390 | 11650 | 0.0 | - |
10.9859 | 11700 | 0.0 | - |
11.0329 | 11750 | 0.0 | - |
11.0798 | 11800 | 0.0 | - |
11.1268 | 11850 | 0.0 | - |
11.1737 | 11900 | 0.0 | - |
11.2207 | 11950 | 0.0 | - |
11.2676 | 12000 | 0.0 | - |
11.3146 | 12050 | 0.0 | - |
11.3615 | 12100 | 0.0 | - |
11.4085 | 12150 | 0.0 | - |
11.4554 | 12200 | 0.0 | - |
11.5023 | 12250 | 0.0015 | - |
11.5493 | 12300 | 0.0 | - |
11.5962 | 12350 | 0.0 | - |
11.6432 | 12400 | 0.0 | - |
11.6901 | 12450 | 0.0 | - |
11.7371 | 12500 | 0.0 | - |
11.7840 | 12550 | 0.0002 | - |
11.8310 | 12600 | 0.0 | - |
11.8779 | 12650 | 0.0 | - |
11.9249 | 12700 | 0.0 | - |
11.9718 | 12750 | 0.0001 | - |
12.0188 | 12800 | 0.0 | - |
12.0657 | 12850 | 0.0 | - |
12.1127 | 12900 | 0.0 | - |
12.1596 | 12950 | 0.0001 | - |
12.2066 | 13000 | 0.0001 | - |
12.2535 | 13050 | 0.0 | - |
12.3005 | 13100 | 0.0 | - |
12.3474 | 13150 | 0.0001 | - |
12.3944 | 13200 | 0.0 | - |
12.4413 | 13250 | 0.0 | - |
12.4883 | 13300 | 0.0 | - |
12.5352 | 13350 | 0.0 | - |
12.5822 | 13400 | 0.0 | - |
12.6291 | 13450 | 0.0 | - |
12.6761 | 13500 | 0.0 | - |
12.7230 | 13550 | 0.0 | - |
12.7700 | 13600 | 0.0 | - |
12.8169 | 13650 | 0.0 | - |
12.8638 | 13700 | 0.0 | - |
12.9108 | 13750 | 0.0 | - |
12.9577 | 13800 | 0.0 | - |
13.0047 | 13850 | 0.0 | - |
13.0516 | 13900 | 0.0 | - |
13.0986 | 13950 | 0.0 | - |
13.1455 | 14000 | 0.0 | - |
13.1925 | 14050 | 0.0 | - |
13.2394 | 14100 | 0.0 | - |
13.2864 | 14150 | 0.0 | - |
13.3333 | 14200 | 0.0 | - |
13.3803 | 14250 | 0.0 | - |
13.4272 | 14300 | 0.0 | - |
13.4742 | 14350 | 0.0 | - |
13.5211 | 14400 | 0.0 | - |
13.5681 | 14450 | 0.0 | - |
13.6150 | 14500 | 0.0 | - |
13.6620 | 14550 | 0.0 | - |
13.7089 | 14600 | 0.0 | - |
13.7559 | 14650 | 0.0 | - |
13.8028 | 14700 | 0.0 | - |
13.8498 | 14750 | 0.0 | - |
13.8967 | 14800 | 0.0 | - |
13.9437 | 14850 | 0.0 | - |
13.9906 | 14900 | 0.0 | - |
14.0376 | 14950 | 0.0 | - |
14.0845 | 15000 | 0.0 | - |
14.1315 | 15050 | 0.0 | - |
14.1784 | 15100 | 0.0001 | - |
14.2254 | 15150 | 0.0 | - |
14.2723 | 15200 | 0.0 | - |
14.3192 | 15250 | 0.0 | - |
14.3662 | 15300 | 0.0 | - |
14.4131 | 15350 | 0.0 | - |
14.4601 | 15400 | 0.0 | - |
14.5070 | 15450 | 0.0 | - |
14.5540 | 15500 | 0.0 | - |
14.6009 | 15550 | 0.0 | - |
14.6479 | 15600 | 0.0 | - |
14.6948 | 15650 | 0.0 | - |
14.7418 | 15700 | 0.0 | - |
14.7887 | 15750 | 0.0 | - |
14.8357 | 15800 | 0.0 | - |
14.8826 | 15850 | 0.0 | - |
14.9296 | 15900 | 0.0 | - |
14.9765 | 15950 | 0.0 | - |
15.0235 | 16000 | 0.0 | - |
15.0704 | 16050 | 0.0 | - |
15.1174 | 16100 | 0.0 | - |
15.1643 | 16150 | 0.0 | - |
15.2113 | 16200 | 0.0 | - |
15.2582 | 16250 | 0.0 | - |
15.3052 | 16300 | 0.0 | - |
15.3521 | 16350 | 0.0 | - |
15.3991 | 16400 | 0.0 | - |
15.4460 | 16450 | 0.0 | - |
15.4930 | 16500 | 0.0 | - |
15.5399 | 16550 | 0.0 | - |
15.5869 | 16600 | 0.0 | - |
15.6338 | 16650 | 0.0 | - |
15.6808 | 16700 | 0.0 | - |
15.7277 | 16750 | 0.0 | - |
15.7746 | 16800 | 0.0 | - |
15.8216 | 16850 | 0.0 | - |
15.8685 | 16900 | 0.0 | - |
15.9155 | 16950 | 0.0 | - |
15.9624 | 17000 | 0.0 | - |
16.0094 | 17050 | 0.0 | - |
16.0563 | 17100 | 0.0 | - |
16.1033 | 17150 | 0.0 | - |
16.1502 | 17200 | 0.0 | - |
16.1972 | 17250 | 0.0 | - |
16.2441 | 17300 | 0.0 | - |
16.2911 | 17350 | 0.0 | - |
16.3380 | 17400 | 0.0 | - |
16.3850 | 17450 | 0.0 | - |
16.4319 | 17500 | 0.0 | - |
16.4789 | 17550 | 0.0 | - |
16.5258 | 17600 | 0.0 | - |
16.5728 | 17650 | 0.0 | - |
16.6197 | 17700 | 0.0 | - |
16.6667 | 17750 | 0.0 | - |
16.7136 | 17800 | 0.0 | - |
16.7606 | 17850 | 0.0 | - |
16.8075 | 17900 | 0.0 | - |
16.8545 | 17950 | 0.0 | - |
16.9014 | 18000 | 0.0 | - |
16.9484 | 18050 | 0.0 | - |
16.9953 | 18100 | 0.0 | - |
17.0423 | 18150 | 0.0 | - |
17.0892 | 18200 | 0.0 | - |
17.1362 | 18250 | 0.0 | - |
17.1831 | 18300 | 0.0 | - |
17.2300 | 18350 | 0.0 | - |
17.2770 | 18400 | 0.0 | - |
17.3239 | 18450 | 0.0 | - |
17.3709 | 18500 | 0.0 | - |
17.4178 | 18550 | 0.0 | - |
17.4648 | 18600 | 0.0 | - |
17.5117 | 18650 | 0.0 | - |
17.5587 | 18700 | 0.0 | - |
17.6056 | 18750 | 0.0 | - |
17.6526 | 18800 | 0.0 | - |
17.6995 | 18850 | 0.0 | - |
17.7465 | 18900 | 0.0 | - |
17.7934 | 18950 | 0.0 | - |
17.8404 | 19000 | 0.0 | - |
17.8873 | 19050 | 0.0 | - |
17.9343 | 19100 | 0.0 | - |
17.9812 | 19150 | 0.0 | - |
18.0282 | 19200 | 0.0 | - |
18.0751 | 19250 | 0.0 | - |
18.1221 | 19300 | 0.0 | - |
18.1690 | 19350 | 0.0 | - |
18.2160 | 19400 | 0.0 | - |
18.2629 | 19450 | 0.0 | - |
18.3099 | 19500 | 0.0 | - |
18.3568 | 19550 | 0.0 | - |
18.4038 | 19600 | 0.0 | - |
18.4507 | 19650 | 0.0 | - |
18.4977 | 19700 | 0.0 | - |
18.5446 | 19750 | 0.0 | - |
18.5915 | 19800 | 0.0 | - |
18.6385 | 19850 | 0.0 | - |
18.6854 | 19900 | 0.0 | - |
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18.9671 | 20200 | 0.0 | - |
19.0141 | 20250 | 0.0 | - |
19.0610 | 20300 | 0.0 | - |
19.1080 | 20350 | 0.0 | - |
19.1549 | 20400 | 0.0 | - |
19.2019 | 20450 | 0.0 | - |
19.2488 | 20500 | 0.0 | - |
19.2958 | 20550 | 0.0 | - |
19.3427 | 20600 | 0.0 | - |
19.3897 | 20650 | 0.0 | - |
19.4366 | 20700 | 0.0 | - |
19.4836 | 20750 | 0.0 | - |
19.5305 | 20800 | 0.0 | - |
19.5775 | 20850 | 0.0 | - |
19.6244 | 20900 | 0.0 | - |
19.6714 | 20950 | 0.0 | - |
19.7183 | 21000 | 0.0 | - |
19.7653 | 21050 | 0.0 | - |
19.8122 | 21100 | 0.0 | - |
19.8592 | 21150 | 0.0 | - |
19.9061 | 21200 | 0.0 | - |
19.9531 | 21250 | 0.0 | - |
20.0 | 21300 | 0.0 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.1.1
- Transformers: 4.46.1
- PyTorch: 2.4.0+cu121
- Datasets: 2.20.0
- Tokenizers: 0.20.0
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}
}