metadata
base_model: mini1013/master_domain
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
아이오페 바이오 컨디셔닝 에센스/맨 에센스/택1 맨 바이오 컨디셔닝 에센스 (#M)11st>스킨케어>에센스/세럼>에센스/세럼 11st
> 뷰티 > 스킨케어 > 에센스/세럼 > 에센스/세럼
- text: >-
오딧세이 블랙 에멀전 100ml + 에멀전 30ml MinSellAmount (#M)화장품/향수>남성화장품>남성로션 Gmarket >
뷰티 > 화장품/향수 > 남성화장품 > 남성로션
- text: >-
(랩시리즈)(2) 올인원 로션 Size Up 세트 (+대용량 샘플 20ml 추가 증정) 단일상품
(#M)화장품/향수>남성화장품>남성로션 Gmarket > 뷰티 > 화장품/향수 > 남성화장품 > 남성로션
- text: >-
설화수 본윤 단품 2종 세트 _G 설화수 본윤 단품 2종 세트 (#M)화장품/향수>남성화장품>남성에센스/크림 Gmarket > 뷰티
> 화장품/향수 > 남성화장품 > 남성에센스/크림
- text: >-
아이오페 맨 바이오 컨디셔닝 에센스 145ml × 4개 (#M)쿠팡 홈>뷰티>남성화장품>남성스킨케어>스킨/로션/크림 Coupang >
뷰티 > 남성화장품 > 남성스킨케어 > 스킨/로션/크림
inference: true
model-index:
- name: SetFit with mini1013/master_domain
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.8685831622176592
name: Accuracy
SetFit with mini1013/master_domain
This is a SetFit model that can be used for Text Classification. This SetFit model uses mini1013/master_domain 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: mini1013/master_domain
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 11 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 |
---|---|
3 |
|
5 |
|
1 |
|
8 |
|
2 |
|
10 |
|
7 |
|
4 |
|
0 |
|
9 |
|
6 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.8686 |
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_cate_bt_top1_test")
# Run inference
preds = model("오딧세이 블랙 에멀전 100ml + 에멀전 30ml MinSellAmount (#M)화장품/향수>남성화장품>남성로션 Gmarket > 뷰티 > 화장품/향수 > 남성화장품 > 남성로션")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 11 | 20.6216 | 56 |
Label | Training Sample Count |
---|---|
0 | 50 |
1 | 50 |
2 | 50 |
3 | 50 |
4 | 50 |
5 | 50 |
6 | 50 |
7 | 50 |
8 | 50 |
9 | 50 |
10 | 18 |
Training Hyperparameters
- batch_size: (64, 64)
- num_epochs: (30, 30)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 100
- 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 |
---|---|---|---|
0.0012 | 1 | 0.429 | - |
0.0617 | 50 | 0.4399 | - |
0.1235 | 100 | 0.4253 | - |
0.1852 | 150 | 0.4166 | - |
0.2469 | 200 | 0.4024 | - |
0.3086 | 250 | 0.3741 | - |
0.3704 | 300 | 0.3411 | - |
0.4321 | 350 | 0.3274 | - |
0.4938 | 400 | 0.2956 | - |
0.5556 | 450 | 0.2724 | - |
0.6173 | 500 | 0.2624 | - |
0.6790 | 550 | 0.2515 | - |
0.7407 | 600 | 0.2446 | - |
0.8025 | 650 | 0.2304 | - |
0.8642 | 700 | 0.213 | - |
0.9259 | 750 | 0.2048 | - |
0.9877 | 800 | 0.1821 | - |
1.0494 | 850 | 0.1701 | - |
1.1111 | 900 | 0.1579 | - |
1.1728 | 950 | 0.1496 | - |
1.2346 | 1000 | 0.1328 | - |
1.2963 | 1050 | 0.1171 | - |
1.3580 | 1100 | 0.1073 | - |
1.4198 | 1150 | 0.096 | - |
1.4815 | 1200 | 0.0837 | - |
1.5432 | 1250 | 0.0765 | - |
1.6049 | 1300 | 0.0659 | - |
1.6667 | 1350 | 0.0528 | - |
1.7284 | 1400 | 0.045 | - |
1.7901 | 1450 | 0.0303 | - |
1.8519 | 1500 | 0.0273 | - |
1.9136 | 1550 | 0.0258 | - |
1.9753 | 1600 | 0.0295 | - |
2.0370 | 1650 | 0.0212 | - |
2.0988 | 1700 | 0.0216 | - |
2.1605 | 1750 | 0.0235 | - |
2.2222 | 1800 | 0.0247 | - |
2.2840 | 1850 | 0.0229 | - |
2.3457 | 1900 | 0.0181 | - |
2.4074 | 1950 | 0.0184 | - |
2.4691 | 2000 | 0.0179 | - |
2.5309 | 2050 | 0.0174 | - |
2.5926 | 2100 | 0.0205 | - |
2.6543 | 2150 | 0.0196 | - |
2.7160 | 2200 | 0.0177 | - |
2.7778 | 2250 | 0.0178 | - |
2.8395 | 2300 | 0.0195 | - |
2.9012 | 2350 | 0.0199 | - |
2.9630 | 2400 | 0.0185 | - |
3.0247 | 2450 | 0.0155 | - |
3.0864 | 2500 | 0.0146 | - |
3.1481 | 2550 | 0.0185 | - |
3.2099 | 2600 | 0.0179 | - |
3.2716 | 2650 | 0.0187 | - |
3.3333 | 2700 | 0.0184 | - |
3.3951 | 2750 | 0.0163 | - |
3.4568 | 2800 | 0.0163 | - |
3.5185 | 2850 | 0.016 | - |
3.5802 | 2900 | 0.0141 | - |
3.6420 | 2950 | 0.0153 | - |
3.7037 | 3000 | 0.0149 | - |
3.7654 | 3050 | 0.0096 | - |
3.8272 | 3100 | 0.0053 | - |
3.8889 | 3150 | 0.005 | - |
3.9506 | 3200 | 0.0042 | - |
4.0123 | 3250 | 0.0032 | - |
4.0741 | 3300 | 0.0034 | - |
4.1358 | 3350 | 0.0041 | - |
4.1975 | 3400 | 0.0029 | - |
4.2593 | 3450 | 0.0035 | - |
4.3210 | 3500 | 0.0038 | - |
4.3827 | 3550 | 0.0032 | - |
4.4444 | 3600 | 0.0035 | - |
4.5062 | 3650 | 0.0024 | - |
4.5679 | 3700 | 0.0046 | - |
4.6296 | 3750 | 0.005 | - |
4.6914 | 3800 | 0.0039 | - |
4.7531 | 3850 | 0.004 | - |
4.8148 | 3900 | 0.0031 | - |
4.8765 | 3950 | 0.0051 | - |
4.9383 | 4000 | 0.0044 | - |
5.0 | 4050 | 0.0031 | - |
5.0617 | 4100 | 0.0028 | - |
5.1235 | 4150 | 0.0037 | - |
5.1852 | 4200 | 0.0034 | - |
5.2469 | 4250 | 0.0041 | - |
5.3086 | 4300 | 0.0039 | - |
5.3704 | 4350 | 0.0033 | - |
5.4321 | 4400 | 0.0028 | - |
5.4938 | 4450 | 0.0031 | - |
5.5556 | 4500 | 0.0033 | - |
5.6173 | 4550 | 0.0042 | - |
5.6790 | 4600 | 0.0045 | - |
5.7407 | 4650 | 0.0034 | - |
5.8025 | 4700 | 0.0031 | - |
5.8642 | 4750 | 0.0034 | - |
5.9259 | 4800 | 0.0037 | - |
5.9877 | 4850 | 0.0042 | - |
6.0494 | 4900 | 0.0041 | - |
6.1111 | 4950 | 0.0033 | - |
6.1728 | 5000 | 0.0033 | - |
6.2346 | 5050 | 0.0032 | - |
6.2963 | 5100 | 0.0066 | - |
6.3580 | 5150 | 0.0052 | - |
6.4198 | 5200 | 0.003 | - |
6.4815 | 5250 | 0.0027 | - |
6.5432 | 5300 | 0.0042 | - |
6.6049 | 5350 | 0.0036 | - |
6.6667 | 5400 | 0.0044 | - |
6.7284 | 5450 | 0.0049 | - |
6.7901 | 5500 | 0.011 | - |
6.8519 | 5550 | 0.0072 | - |
6.9136 | 5600 | 0.0084 | - |
6.9753 | 5650 | 0.0062 | - |
7.0370 | 5700 | 0.0093 | - |
7.0988 | 5750 | 0.0067 | - |
7.1605 | 5800 | 0.009 | - |
7.2222 | 5850 | 0.0103 | - |
7.2840 | 5900 | 0.0049 | - |
7.3457 | 5950 | 0.0023 | - |
7.4074 | 6000 | 0.0035 | - |
7.4691 | 6050 | 0.002 | - |
7.5309 | 6100 | 0.005 | - |
7.5926 | 6150 | 0.0049 | - |
7.6543 | 6200 | 0.0026 | - |
7.7160 | 6250 | 0.0006 | - |
7.7778 | 6300 | 0.0001 | - |
7.8395 | 6350 | 0.0002 | - |
7.9012 | 6400 | 0.0 | - |
7.9630 | 6450 | 0.0 | - |
8.0247 | 6500 | 0.0 | - |
8.0864 | 6550 | 0.0 | - |
8.1481 | 6600 | 0.0 | - |
8.2099 | 6650 | 0.0 | - |
8.2716 | 6700 | 0.0 | - |
8.3333 | 6750 | 0.0 | - |
8.3951 | 6800 | 0.0 | - |
8.4568 | 6850 | 0.0 | - |
8.5185 | 6900 | 0.0 | - |
8.5802 | 6950 | 0.0 | - |
8.6420 | 7000 | 0.0 | - |
8.7037 | 7050 | 0.0 | - |
8.7654 | 7100 | 0.0 | - |
8.8272 | 7150 | 0.0 | - |
8.8889 | 7200 | 0.0 | - |
8.9506 | 7250 | 0.0 | - |
9.0123 | 7300 | 0.0 | - |
9.0741 | 7350 | 0.0 | - |
9.1358 | 7400 | 0.0 | - |
9.1975 | 7450 | 0.0 | - |
9.2593 | 7500 | 0.0 | - |
9.3210 | 7550 | 0.0 | - |
9.3827 | 7600 | 0.0 | - |
9.4444 | 7650 | 0.0 | - |
9.5062 | 7700 | 0.0 | - |
9.5679 | 7750 | 0.0 | - |
9.6296 | 7800 | 0.0 | - |
9.6914 | 7850 | 0.0 | - |
9.7531 | 7900 | 0.0 | - |
9.8148 | 7950 | 0.0 | - |
9.8765 | 8000 | 0.0 | - |
9.9383 | 8050 | 0.0 | - |
10.0 | 8100 | 0.0 | - |
10.0617 | 8150 | 0.0 | - |
10.1235 | 8200 | 0.0 | - |
10.1852 | 8250 | 0.0 | - |
10.2469 | 8300 | 0.0 | - |
10.3086 | 8350 | 0.0 | - |
10.3704 | 8400 | 0.0 | - |
10.4321 | 8450 | 0.0 | - |
10.4938 | 8500 | 0.0005 | - |
10.5556 | 8550 | 0.0012 | - |
10.6173 | 8600 | 0.005 | - |
10.6790 | 8650 | 0.0036 | - |
10.7407 | 8700 | 0.0023 | - |
10.8025 | 8750 | 0.0014 | - |
10.8642 | 8800 | 0.0001 | - |
10.9259 | 8850 | 0.0002 | - |
10.9877 | 8900 | 0.0 | - |
11.0494 | 8950 | 0.0 | - |
11.1111 | 9000 | 0.0 | - |
11.1728 | 9050 | 0.0 | - |
11.2346 | 9100 | 0.0001 | - |
11.2963 | 9150 | 0.0 | - |
11.3580 | 9200 | 0.0 | - |
11.4198 | 9250 | 0.0001 | - |
11.4815 | 9300 | 0.0002 | - |
11.5432 | 9350 | 0.0 | - |
11.6049 | 9400 | 0.0 | - |
11.6667 | 9450 | 0.0 | - |
11.7284 | 9500 | 0.0 | - |
11.7901 | 9550 | 0.0 | - |
11.8519 | 9600 | 0.0002 | - |
11.9136 | 9650 | 0.0003 | - |
11.9753 | 9700 | 0.0 | - |
12.0370 | 9750 | 0.0 | - |
12.0988 | 9800 | 0.0 | - |
12.1605 | 9850 | 0.0 | - |
12.2222 | 9900 | 0.0 | - |
12.2840 | 9950 | 0.0 | - |
12.3457 | 10000 | 0.0 | - |
12.4074 | 10050 | 0.0 | - |
12.4691 | 10100 | 0.0 | - |
12.5309 | 10150 | 0.0 | - |
12.5926 | 10200 | 0.0 | - |
12.6543 | 10250 | 0.0011 | - |
12.7160 | 10300 | 0.0005 | - |
12.7778 | 10350 | 0.0 | - |
12.8395 | 10400 | 0.0 | - |
12.9012 | 10450 | 0.0 | - |
12.9630 | 10500 | 0.0 | - |
13.0247 | 10550 | 0.0 | - |
13.0864 | 10600 | 0.0 | - |
13.1481 | 10650 | 0.0003 | - |
13.2099 | 10700 | 0.0 | - |
13.2716 | 10750 | 0.0001 | - |
13.3333 | 10800 | 0.0001 | - |
13.3951 | 10850 | 0.0002 | - |
13.4568 | 10900 | 0.0002 | - |
13.5185 | 10950 | 0.0 | - |
13.5802 | 11000 | 0.0 | - |
13.6420 | 11050 | 0.0 | - |
13.7037 | 11100 | 0.0 | - |
13.7654 | 11150 | 0.0 | - |
13.8272 | 11200 | 0.0 | - |
13.8889 | 11250 | 0.0 | - |
13.9506 | 11300 | 0.0 | - |
14.0123 | 11350 | 0.0 | - |
14.0741 | 11400 | 0.0 | - |
14.1358 | 11450 | 0.0 | - |
14.1975 | 11500 | 0.0 | - |
14.2593 | 11550 | 0.0 | - |
14.3210 | 11600 | 0.0 | - |
14.3827 | 11650 | 0.0 | - |
14.4444 | 11700 | 0.0 | - |
14.5062 | 11750 | 0.0 | - |
14.5679 | 11800 | 0.0 | - |
14.6296 | 11850 | 0.0 | - |
14.6914 | 11900 | 0.0 | - |
14.7531 | 11950 | 0.0 | - |
14.8148 | 12000 | 0.0 | - |
14.8765 | 12050 | 0.0 | - |
14.9383 | 12100 | 0.0 | - |
15.0 | 12150 | 0.0 | - |
15.0617 | 12200 | 0.0 | - |
15.1235 | 12250 | 0.0 | - |
15.1852 | 12300 | 0.0 | - |
15.2469 | 12350 | 0.0 | - |
15.3086 | 12400 | 0.0 | - |
15.3704 | 12450 | 0.0028 | - |
15.4321 | 12500 | 0.0004 | - |
15.4938 | 12550 | 0.0002 | - |
15.5556 | 12600 | 0.0 | - |
15.6173 | 12650 | 0.0 | - |
15.6790 | 12700 | 0.0001 | - |
15.7407 | 12750 | 0.0 | - |
15.8025 | 12800 | 0.0 | - |
15.8642 | 12850 | 0.0 | - |
15.9259 | 12900 | 0.0002 | - |
15.9877 | 12950 | 0.0 | - |
16.0494 | 13000 | 0.0 | - |
16.1111 | 13050 | 0.0 | - |
16.1728 | 13100 | 0.0 | - |
16.2346 | 13150 | 0.0 | - |
16.2963 | 13200 | 0.0 | - |
16.3580 | 13250 | 0.0 | - |
16.4198 | 13300 | 0.0 | - |
16.4815 | 13350 | 0.0004 | - |
16.5432 | 13400 | 0.0 | - |
16.6049 | 13450 | 0.0002 | - |
16.6667 | 13500 | 0.0 | - |
16.7284 | 13550 | 0.0 | - |
16.7901 | 13600 | 0.0 | - |
16.8519 | 13650 | 0.0 | - |
16.9136 | 13700 | 0.0 | - |
16.9753 | 13750 | 0.0 | - |
17.0370 | 13800 | 0.0 | - |
17.0988 | 13850 | 0.0 | - |
17.1605 | 13900 | 0.0 | - |
17.2222 | 13950 | 0.0 | - |
17.2840 | 14000 | 0.0 | - |
17.3457 | 14050 | 0.0 | - |
17.4074 | 14100 | 0.0 | - |
17.4691 | 14150 | 0.0001 | - |
17.5309 | 14200 | 0.0022 | - |
17.5926 | 14250 | 0.0058 | - |
17.6543 | 14300 | 0.0001 | - |
17.7160 | 14350 | 0.0 | - |
17.7778 | 14400 | 0.0 | - |
17.8395 | 14450 | 0.0001 | - |
17.9012 | 14500 | 0.0 | - |
17.9630 | 14550 | 0.0 | - |
18.0247 | 14600 | 0.0001 | - |
18.0864 | 14650 | 0.0 | - |
18.1481 | 14700 | 0.0002 | - |
18.2099 | 14750 | 0.0 | - |
18.2716 | 14800 | 0.0 | - |
18.3333 | 14850 | 0.0 | - |
18.3951 | 14900 | 0.0003 | - |
18.4568 | 14950 | 0.0 | - |
18.5185 | 15000 | 0.0 | - |
18.5802 | 15050 | 0.0 | - |
18.6420 | 15100 | 0.0 | - |
18.7037 | 15150 | 0.0 | - |
18.7654 | 15200 | 0.0 | - |
18.8272 | 15250 | 0.0 | - |
18.8889 | 15300 | 0.0 | - |
18.9506 | 15350 | 0.0 | - |
19.0123 | 15400 | 0.0001 | - |
19.0741 | 15450 | 0.0 | - |
19.1358 | 15500 | 0.0 | - |
19.1975 | 15550 | 0.0 | - |
19.2593 | 15600 | 0.0002 | - |
19.3210 | 15650 | 0.0 | - |
19.3827 | 15700 | 0.0 | - |
19.4444 | 15750 | 0.0 | - |
19.5062 | 15800 | 0.0 | - |
19.5679 | 15850 | 0.0 | - |
19.6296 | 15900 | 0.0 | - |
19.6914 | 15950 | 0.0 | - |
19.7531 | 16000 | 0.0 | - |
19.8148 | 16050 | 0.0 | - |
19.8765 | 16100 | 0.0 | - |
19.9383 | 16150 | 0.0 | - |
20.0 | 16200 | 0.0 | - |
20.0617 | 16250 | 0.0 | - |
20.1235 | 16300 | 0.0 | - |
20.1852 | 16350 | 0.0 | - |
20.2469 | 16400 | 0.0 | - |
20.3086 | 16450 | 0.0 | - |
20.3704 | 16500 | 0.0 | - |
20.4321 | 16550 | 0.0 | - |
20.4938 | 16600 | 0.0 | - |
20.5556 | 16650 | 0.0 | - |
20.6173 | 16700 | 0.0 | - |
20.6790 | 16750 | 0.0 | - |
20.7407 | 16800 | 0.0 | - |
20.8025 | 16850 | 0.0 | - |
20.8642 | 16900 | 0.0 | - |
20.9259 | 16950 | 0.0 | - |
20.9877 | 17000 | 0.0 | - |
21.0494 | 17050 | 0.0 | - |
21.1111 | 17100 | 0.0 | - |
21.1728 | 17150 | 0.0 | - |
21.2346 | 17200 | 0.0 | - |
21.2963 | 17250 | 0.0 | - |
21.3580 | 17300 | 0.0 | - |
21.4198 | 17350 | 0.0 | - |
21.4815 | 17400 | 0.0 | - |
21.5432 | 17450 | 0.0 | - |
21.6049 | 17500 | 0.0 | - |
21.6667 | 17550 | 0.0 | - |
21.7284 | 17600 | 0.0 | - |
21.7901 | 17650 | 0.0 | - |
21.8519 | 17700 | 0.0 | - |
21.9136 | 17750 | 0.0 | - |
21.9753 | 17800 | 0.0 | - |
22.0370 | 17850 | 0.0 | - |
22.0988 | 17900 | 0.0 | - |
22.1605 | 17950 | 0.0 | - |
22.2222 | 18000 | 0.0 | - |
22.2840 | 18050 | 0.0 | - |
22.3457 | 18100 | 0.0 | - |
22.4074 | 18150 | 0.0 | - |
22.4691 | 18200 | 0.0 | - |
22.5309 | 18250 | 0.0 | - |
22.5926 | 18300 | 0.0 | - |
22.6543 | 18350 | 0.0 | - |
22.7160 | 18400 | 0.0 | - |
22.7778 | 18450 | 0.0 | - |
22.8395 | 18500 | 0.0 | - |
22.9012 | 18550 | 0.0 | - |
22.9630 | 18600 | 0.0 | - |
23.0247 | 18650 | 0.0 | - |
23.0864 | 18700 | 0.0 | - |
23.1481 | 18750 | 0.0 | - |
23.2099 | 18800 | 0.0 | - |
23.2716 | 18850 | 0.0 | - |
23.3333 | 18900 | 0.0 | - |
23.3951 | 18950 | 0.0 | - |
23.4568 | 19000 | 0.0 | - |
23.5185 | 19050 | 0.0 | - |
23.5802 | 19100 | 0.0 | - |
23.6420 | 19150 | 0.0 | - |
23.7037 | 19200 | 0.0 | - |
23.7654 | 19250 | 0.0 | - |
23.8272 | 19300 | 0.0 | - |
23.8889 | 19350 | 0.0 | - |
23.9506 | 19400 | 0.0 | - |
24.0123 | 19450 | 0.0 | - |
24.0741 | 19500 | 0.0 | - |
24.1358 | 19550 | 0.0 | - |
24.1975 | 19600 | 0.0 | - |
24.2593 | 19650 | 0.0 | - |
24.3210 | 19700 | 0.0 | - |
24.3827 | 19750 | 0.0 | - |
24.4444 | 19800 | 0.0 | - |
24.5062 | 19850 | 0.0 | - |
24.5679 | 19900 | 0.0 | - |
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24.6914 | 20000 | 0.0 | - |
24.7531 | 20050 | 0.0 | - |
24.8148 | 20100 | 0.0 | - |
24.8765 | 20150 | 0.0 | - |
24.9383 | 20200 | 0.0 | - |
25.0 | 20250 | 0.0 | - |
25.0617 | 20300 | 0.0 | - |
25.1235 | 20350 | 0.0 | - |
25.1852 | 20400 | 0.0 | - |
25.2469 | 20450 | 0.0 | - |
25.3086 | 20500 | 0.0 | - |
25.3704 | 20550 | 0.0 | - |
25.4321 | 20600 | 0.0 | - |
25.4938 | 20650 | 0.0 | - |
25.5556 | 20700 | 0.0 | - |
25.6173 | 20750 | 0.0 | - |
25.6790 | 20800 | 0.0 | - |
25.7407 | 20850 | 0.0 | - |
25.8025 | 20900 | 0.0 | - |
25.8642 | 20950 | 0.0 | - |
25.9259 | 21000 | 0.0 | - |
25.9877 | 21050 | 0.0 | - |
26.0494 | 21100 | 0.0 | - |
26.1111 | 21150 | 0.0 | - |
26.1728 | 21200 | 0.0 | - |
26.2346 | 21250 | 0.0 | - |
26.2963 | 21300 | 0.0 | - |
26.3580 | 21350 | 0.0 | - |
26.4198 | 21400 | 0.0 | - |
26.4815 | 21450 | 0.0 | - |
26.5432 | 21500 | 0.0 | - |
26.6049 | 21550 | 0.0 | - |
26.6667 | 21600 | 0.0 | - |
26.7284 | 21650 | 0.0 | - |
26.7901 | 21700 | 0.0 | - |
26.8519 | 21750 | 0.0 | - |
26.9136 | 21800 | 0.0 | - |
26.9753 | 21850 | 0.0 | - |
27.0370 | 21900 | 0.0 | - |
27.0988 | 21950 | 0.0 | - |
27.1605 | 22000 | 0.0 | - |
27.2222 | 22050 | 0.0 | - |
27.2840 | 22100 | 0.0 | - |
27.3457 | 22150 | 0.0 | - |
27.4074 | 22200 | 0.0 | - |
27.4691 | 22250 | 0.0 | - |
27.5309 | 22300 | 0.0 | - |
27.5926 | 22350 | 0.0 | - |
27.6543 | 22400 | 0.0 | - |
27.7160 | 22450 | 0.0 | - |
27.7778 | 22500 | 0.0 | - |
27.8395 | 22550 | 0.0 | - |
27.9012 | 22600 | 0.0 | - |
27.9630 | 22650 | 0.0 | - |
28.0247 | 22700 | 0.0 | - |
28.0864 | 22750 | 0.0 | - |
28.1481 | 22800 | 0.0 | - |
28.2099 | 22850 | 0.0 | - |
28.2716 | 22900 | 0.0 | - |
28.3333 | 22950 | 0.0 | - |
28.3951 | 23000 | 0.0 | - |
28.4568 | 23050 | 0.0 | - |
28.5185 | 23100 | 0.0 | - |
28.5802 | 23150 | 0.0 | - |
28.6420 | 23200 | 0.0 | - |
28.7037 | 23250 | 0.0 | - |
28.7654 | 23300 | 0.0 | - |
28.8272 | 23350 | 0.0 | - |
28.8889 | 23400 | 0.0 | - |
28.9506 | 23450 | 0.0 | - |
29.0123 | 23500 | 0.0 | - |
29.0741 | 23550 | 0.0 | - |
29.1358 | 23600 | 0.0 | - |
29.1975 | 23650 | 0.0 | - |
29.2593 | 23700 | 0.0 | - |
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29.4444 | 23850 | 0.0 | - |
29.5062 | 23900 | 0.0 | - |
29.5679 | 23950 | 0.0 | - |
29.6296 | 24000 | 0.0 | - |
29.6914 | 24050 | 0.0 | - |
29.7531 | 24100 | 0.0 | - |
29.8148 | 24150 | 0.0 | - |
29.8765 | 24200 | 0.0 | - |
29.9383 | 24250 | 0.0 | - |
30.0 | 24300 | 0.0 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.44.2
- PyTorch: 2.2.0a0+81ea7a4
- 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}
}