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: 100 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 |
|---|---|
| 42 |
|
| 60 |
|
| 1 |
|
| 68 |
|
| 52 |
|
| 33 |
|
| 71 |
|
| 48 |
|
| 18 |
|
| 38 |
|
| 98 |
|
| 87 |
|
| 47 |
|
| 67 |
|
| 96 |
|
| 22 |
|
| 30 |
|
| 24 |
|
| 79 |
|
| 80 |
|
| 23 |
|
| 50 |
|
| 5 |
|
| 19 |
|
| 55 |
|
| 7 |
|
| 64 |
|
| 9 |
|
| 74 |
|
| 90 |
|
| 40 |
|
| 92 |
|
| 59 |
|
| 29 |
|
| 70 |
|
| 56 |
|
| 13 |
|
| 82 |
|
| 58 |
|
| 25 |
|
| 37 |
|
| 78 |
|
| 27 |
|
| 66 |
|
| 41 |
|
| 16 |
|
| 76 |
|
| 73 |
|
| 86 |
|
| 83 |
|
| 17 |
|
| 85 |
|
| 63 |
|
| 51 |
|
| 89 |
|
| 28 |
|
| 8 |
|
| 46 |
|
| 75 |
|
| 15 |
|
| 81 |
|
| 61 |
|
| 21 |
|
| 72 |
|
| 31 |
|
| 84 |
|
| 11 |
|
| 99 |
|
| 43 |
|
| 91 |
|
| 49 |
|
| 53 |
|
| 26 |
|
| 77 |
|
| 94 |
|
| 2 |
|
| 12 |
|
| 93 |
|
| 35 |
|
| 54 |
|
| 45 |
|
| 6 |
|
| 39 |
|
| 0 |
|
| 65 |
|
| 4 |
|
| 95 |
|
| 88 |
|
| 20 |
|
| 62 |
|
| 3 |
|
| 32 |
|
| 10 |
|
| 69 |
|
| 14 |
|
| 34 |
|
| 36 |
|
| 44 |
|
| 97 |
|
| 57 |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.854 |
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_bt_test_org_allcate")
# Run inference
preds = model("러쉬 배쓰밤 20종 러쉬 입욕제 티스티 토스티 (#M)홈>러쉬 Naverstore > 화장품/미용 > 바디케어 > 입욕제")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 10 | 23.0575 | 125 |
| Label | Training Sample Count |
|---|---|
| 0 | 50 |
| 1 | 50 |
| 2 | 50 |
| 3 | 50 |
| 4 | 50 |
| 5 | 50 |
| 6 | 50 |
| 7 | 46 |
| 8 | 50 |
| 9 | 50 |
| 10 | 50 |
| 11 | 50 |
| 12 | 50 |
| 13 | 49 |
| 14 | 50 |
| 15 | 50 |
| 16 | 50 |
| 17 | 50 |
| 18 | 50 |
| 19 | 50 |
| 20 | 50 |
| 21 | 50 |
| 22 | 50 |
| 23 | 50 |
| 24 | 50 |
| 25 | 50 |
| 26 | 50 |
| 27 | 50 |
| 28 | 50 |
| 29 | 50 |
| 30 | 50 |
| 31 | 50 |
| 32 | 50 |
| 33 | 50 |
| 34 | 50 |
| 35 | 50 |
| 36 | 50 |
| 37 | 50 |
| 38 | 50 |
| 39 | 50 |
| 40 | 50 |
| 41 | 50 |
| 42 | 50 |
| 43 | 50 |
| 44 | 50 |
| 45 | 50 |
| 46 | 50 |
| 47 | 50 |
| 48 | 50 |
| 49 | 50 |
| 50 | 50 |
| 51 | 50 |
| 52 | 50 |
| 53 | 50 |
| 54 | 50 |
| 55 | 50 |
| 56 | 50 |
| 57 | 50 |
| 58 | 50 |
| 59 | 50 |
| 60 | 49 |
| 61 | 50 |
| 62 | 50 |
| 63 | 50 |
| 64 | 50 |
| 65 | 50 |
| 66 | 50 |
| 67 | 50 |
| 68 | 50 |
| 69 | 50 |
| 70 | 50 |
| 71 | 50 |
| 72 | 50 |
| 73 | 50 |
| 74 | 50 |
| 75 | 50 |
| 76 | 50 |
| 77 | 50 |
| 78 | 50 |
| 79 | 50 |
| 80 | 50 |
| 81 | 50 |
| 82 | 50 |
| 83 | 50 |
| 84 | 50 |
| 85 | 50 |
| 86 | 50 |
| 87 | 50 |
| 88 | 50 |
| 89 | 50 |
| 90 | 50 |
| 91 | 50 |
| 92 | 50 |
| 93 | 50 |
| 94 | 50 |
| 95 | 50 |
| 96 | 50 |
| 97 | 50 |
| 98 | 50 |
| 99 | 50 |
Training Hyperparameters
- batch_size: (64, 64)
- num_epochs: (20, 20)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 30
- 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 |
|---|---|---|---|
| 0.0004 | 1 | 0.4519 | - |
| 0.0214 | 50 | 0.4285 | - |
| 0.0427 | 100 | 0.4177 | - |
| 0.0641 | 150 | 0.3848 | - |
| 0.0854 | 200 | 0.3587 | - |
| 0.1068 | 250 | 0.3257 | - |
| 0.1282 | 300 | 0.2752 | - |
| 0.1495 | 350 | 0.2489 | - |
| 0.1709 | 400 | 0.2172 | - |
| 0.1922 | 450 | 0.1901 | - |
| 0.2136 | 500 | 0.1643 | - |
| 0.2349 | 550 | 0.1478 | - |
| 0.2563 | 600 | 0.1345 | - |
| 0.2777 | 650 | 0.1151 | - |
| 0.2990 | 700 | 0.1073 | - |
| 0.3204 | 750 | 0.1002 | - |
| 0.3417 | 800 | 0.0929 | - |
| 0.3631 | 850 | 0.0902 | - |
| 0.3845 | 900 | 0.0857 | - |
| 0.4058 | 950 | 0.0749 | - |
| 0.4272 | 1000 | 0.0756 | - |
| 0.4485 | 1050 | 0.0665 | - |
| 0.4699 | 1100 | 0.0659 | - |
| 0.4912 | 1150 | 0.0604 | - |
| 0.5126 | 1200 | 0.0561 | - |
| 0.5340 | 1250 | 0.0555 | - |
| 0.5553 | 1300 | 0.0499 | - |
| 0.5767 | 1350 | 0.0505 | - |
| 0.5980 | 1400 | 0.0492 | - |
| 0.6194 | 1450 | 0.0478 | - |
| 0.6408 | 1500 | 0.0429 | - |
| 0.6621 | 1550 | 0.0419 | - |
| 0.6835 | 1600 | 0.04 | - |
| 0.7048 | 1650 | 0.0376 | - |
| 0.7262 | 1700 | 0.0385 | - |
| 0.7475 | 1750 | 0.0368 | - |
| 0.7689 | 1800 | 0.0345 | - |
| 0.7903 | 1850 | 0.0322 | - |
| 0.8116 | 1900 | 0.0333 | - |
| 0.8330 | 1950 | 0.03 | - |
| 0.8543 | 2000 | 0.0301 | - |
| 0.8757 | 2050 | 0.0321 | - |
| 0.8971 | 2100 | 0.0293 | - |
| 0.9184 | 2150 | 0.0299 | - |
| 0.9398 | 2200 | 0.0289 | - |
| 0.9611 | 2250 | 0.0273 | - |
| 0.9825 | 2300 | 0.0269 | - |
| 1.0038 | 2350 | 0.0272 | - |
| 1.0252 | 2400 | 0.0275 | - |
| 1.0466 | 2450 | 0.0263 | - |
| 1.0679 | 2500 | 0.024 | - |
| 1.0893 | 2550 | 0.0243 | - |
| 1.1106 | 2600 | 0.0213 | - |
| 1.1320 | 2650 | 0.0227 | - |
| 1.1534 | 2700 | 0.0204 | - |
| 1.1747 | 2750 | 0.0243 | - |
| 1.1961 | 2800 | 0.0256 | - |
| 1.2174 | 2850 | 0.0209 | - |
| 1.2388 | 2900 | 0.0231 | - |
| 1.2601 | 2950 | 0.0252 | - |
| 1.2815 | 3000 | 0.0208 | - |
| 1.3029 | 3050 | 0.0219 | - |
| 1.3242 | 3100 | 0.0218 | - |
| 1.3456 | 3150 | 0.022 | - |
| 1.3669 | 3200 | 0.0208 | - |
| 1.3883 | 3250 | 0.0205 | - |
| 1.4097 | 3300 | 0.0198 | - |
| 1.4310 | 3350 | 0.0184 | - |
| 1.4524 | 3400 | 0.0176 | - |
| 1.4737 | 3450 | 0.0178 | - |
| 1.4951 | 3500 | 0.0179 | - |
| 1.5164 | 3550 | 0.0147 | - |
| 1.5378 | 3600 | 0.0168 | - |
| 1.5592 | 3650 | 0.0183 | - |
| 1.5805 | 3700 | 0.0183 | - |
| 1.6019 | 3750 | 0.0173 | - |
| 1.6232 | 3800 | 0.0182 | - |
| 1.6446 | 3850 | 0.0165 | - |
| 1.6660 | 3900 | 0.0165 | - |
| 1.6873 | 3950 | 0.0158 | - |
| 1.7087 | 4000 | 0.0151 | - |
| 1.7300 | 4050 | 0.017 | - |
| 1.7514 | 4100 | 0.0152 | - |
| 1.7727 | 4150 | 0.0144 | - |
| 1.7941 | 4200 | 0.015 | - |
| 1.8155 | 4250 | 0.0133 | - |
| 1.8368 | 4300 | 0.0143 | - |
| 1.8582 | 4350 | 0.0139 | - |
| 1.8795 | 4400 | 0.0119 | - |
| 1.9009 | 4450 | 0.016 | - |
| 1.9223 | 4500 | 0.0119 | - |
| 1.9436 | 4550 | 0.0116 | - |
| 1.9650 | 4600 | 0.0111 | - |
| 1.9863 | 4650 | 0.0129 | - |
| 2.0077 | 4700 | 0.0126 | - |
| 2.0290 | 4750 | 0.0116 | - |
| 2.0504 | 4800 | 0.0095 | - |
| 2.0718 | 4850 | 0.0088 | - |
| 2.0931 | 4900 | 0.0086 | - |
| 2.1145 | 4950 | 0.0085 | - |
| 2.1358 | 5000 | 0.0101 | - |
| 2.1572 | 5050 | 0.0093 | - |
| 2.1786 | 5100 | 0.0105 | - |
| 2.1999 | 5150 | 0.0103 | - |
| 2.2213 | 5200 | 0.0086 | - |
| 2.2426 | 5250 | 0.0091 | - |
| 2.2640 | 5300 | 0.011 | - |
| 2.2853 | 5350 | 0.0092 | - |
| 2.3067 | 5400 | 0.0083 | - |
| 2.3281 | 5450 | 0.0092 | - |
| 2.3494 | 5500 | 0.0083 | - |
| 2.3708 | 5550 | 0.0085 | - |
| 2.3921 | 5600 | 0.0068 | - |
| 2.4135 | 5650 | 0.0081 | - |
| 2.4349 | 5700 | 0.0083 | - |
| 2.4562 | 5750 | 0.0073 | - |
| 2.4776 | 5800 | 0.0083 | - |
| 2.4989 | 5850 | 0.0069 | - |
| 2.5203 | 5900 | 0.0062 | - |
| 2.5416 | 5950 | 0.0067 | - |
| 2.5630 | 6000 | 0.008 | - |
| 2.5844 | 6050 | 0.008 | - |
| 2.6057 | 6100 | 0.0072 | - |
| 2.6271 | 6150 | 0.0063 | - |
| 2.6484 | 6200 | 0.0079 | - |
| 2.6698 | 6250 | 0.0086 | - |
| 2.6912 | 6300 | 0.0083 | - |
| 2.7125 | 6350 | 0.0076 | - |
| 2.7339 | 6400 | 0.0069 | - |
| 2.7552 | 6450 | 0.0084 | - |
| 2.7766 | 6500 | 0.0092 | - |
| 2.7979 | 6550 | 0.0068 | - |
| 2.8193 | 6600 | 0.0062 | - |
| 2.8407 | 6650 | 0.0064 | - |
| 2.8620 | 6700 | 0.0077 | - |
| 2.8834 | 6750 | 0.0053 | - |
| 2.9047 | 6800 | 0.0062 | - |
| 2.9261 | 6850 | 0.0066 | - |
| 2.9475 | 6900 | 0.0076 | - |
| 2.9688 | 6950 | 0.0052 | - |
| 2.9902 | 7000 | 0.0072 | - |
| 3.0115 | 7050 | 0.0073 | - |
| 3.0329 | 7100 | 0.0047 | - |
| 3.0543 | 7150 | 0.0055 | - |
| 3.0756 | 7200 | 0.0052 | - |
| 3.0970 | 7250 | 0.0048 | - |
| 3.1183 | 7300 | 0.0065 | - |
| 3.1397 | 7350 | 0.0059 | - |
| 3.1610 | 7400 | 0.0046 | - |
| 3.1824 | 7450 | 0.0047 | - |
| 3.2038 | 7500 | 0.0049 | - |
| 3.2251 | 7550 | 0.005 | - |
| 3.2465 | 7600 | 0.006 | - |
| 3.2678 | 7650 | 0.0055 | - |
| 3.2892 | 7700 | 0.0058 | - |
| 3.3106 | 7750 | 0.0048 | - |
| 3.3319 | 7800 | 0.0045 | - |
| 3.3533 | 7850 | 0.0047 | - |
| 3.3746 | 7900 | 0.0042 | - |
| 3.3960 | 7950 | 0.0052 | - |
| 3.4173 | 8000 | 0.0034 | - |
| 3.4387 | 8050 | 0.004 | - |
| 3.4601 | 8100 | 0.0029 | - |
| 3.4814 | 8150 | 0.0036 | - |
| 3.5028 | 8200 | 0.0049 | - |
| 3.5241 | 8250 | 0.0045 | - |
| 3.5455 | 8300 | 0.0038 | - |
| 3.5669 | 8350 | 0.0038 | - |
| 3.5882 | 8400 | 0.0043 | - |
| 3.6096 | 8450 | 0.0025 | - |
| 3.6309 | 8500 | 0.0048 | - |
| 3.6523 | 8550 | 0.004 | - |
| 3.6736 | 8600 | 0.004 | - |
| 3.6950 | 8650 | 0.0037 | - |
| 3.7164 | 8700 | 0.0037 | - |
| 3.7377 | 8750 | 0.0041 | - |
| 3.7591 | 8800 | 0.003 | - |
| 3.7804 | 8850 | 0.0036 | - |
| 3.8018 | 8900 | 0.0031 | - |
| 3.8232 | 8950 | 0.0032 | - |
| 3.8445 | 9000 | 0.0035 | - |
| 3.8659 | 9050 | 0.0035 | - |
| 3.8872 | 9100 | 0.0027 | - |
| 3.9086 | 9150 | 0.0031 | - |
| 3.9299 | 9200 | 0.0038 | - |
| 3.9513 | 9250 | 0.0032 | - |
| 3.9727 | 9300 | 0.0032 | - |
| 3.9940 | 9350 | 0.0032 | - |
| 4.0154 | 9400 | 0.0031 | - |
| 4.0367 | 9450 | 0.0023 | - |
| 4.0581 | 9500 | 0.0028 | - |
| 4.0795 | 9550 | 0.0029 | - |
| 4.1008 | 9600 | 0.0031 | - |
| 4.1222 | 9650 | 0.0024 | - |
| 4.1435 | 9700 | 0.0034 | - |
| 4.1649 | 9750 | 0.0031 | - |
| 4.1862 | 9800 | 0.0036 | - |
| 4.2076 | 9850 | 0.0042 | - |
| 4.2290 | 9900 | 0.0044 | - |
| 4.2503 | 9950 | 0.0034 | - |
| 4.2717 | 10000 | 0.0044 | - |
| 4.2930 | 10050 | 0.0038 | - |
| 4.3144 | 10100 | 0.0044 | - |
| 4.3358 | 10150 | 0.0039 | - |
| 4.3571 | 10200 | 0.0049 | - |
| 4.3785 | 10250 | 0.004 | - |
| 4.3998 | 10300 | 0.0031 | - |
| 4.4212 | 10350 | 0.0021 | - |
| 4.4425 | 10400 | 0.0025 | - |
| 4.4639 | 10450 | 0.0032 | - |
| 4.4853 | 10500 | 0.003 | - |
| 4.5066 | 10550 | 0.0027 | - |
| 4.5280 | 10600 | 0.0019 | - |
| 4.5493 | 10650 | 0.002 | - |
| 4.5707 | 10700 | 0.0026 | - |
| 4.5921 | 10750 | 0.0025 | - |
| 4.6134 | 10800 | 0.0028 | - |
| 4.6348 | 10850 | 0.0021 | - |
| 4.6561 | 10900 | 0.0031 | - |
| 4.6775 | 10950 | 0.0017 | - |
| 4.6988 | 11000 | 0.003 | - |
| 4.7202 | 11050 | 0.0036 | - |
| 4.7416 | 11100 | 0.0024 | - |
| 4.7629 | 11150 | 0.0017 | - |
| 4.7843 | 11200 | 0.0024 | - |
| 4.8056 | 11250 | 0.0016 | - |
| 4.8270 | 11300 | 0.0021 | - |
| 4.8484 | 11350 | 0.0022 | - |
| 4.8697 | 11400 | 0.0024 | - |
| 4.8911 | 11450 | 0.004 | - |
| 4.9124 | 11500 | 0.003 | - |
| 4.9338 | 11550 | 0.0032 | - |
| 4.9551 | 11600 | 0.0024 | - |
| 4.9765 | 11650 | 0.0016 | - |
| 4.9979 | 11700 | 0.002 | - |
| 5.0192 | 11750 | 0.0024 | - |
| 5.0406 | 11800 | 0.0022 | - |
| 5.0619 | 11850 | 0.0018 | - |
| 5.0833 | 11900 | 0.0015 | - |
| 5.1047 | 11950 | 0.0023 | - |
| 5.1260 | 12000 | 0.0021 | - |
| 5.1474 | 12050 | 0.0015 | - |
| 5.1687 | 12100 | 0.002 | - |
| 5.1901 | 12150 | 0.0014 | - |
| 5.2114 | 12200 | 0.0011 | - |
| 5.2328 | 12250 | 0.0016 | - |
| 5.2542 | 12300 | 0.0019 | - |
| 5.2755 | 12350 | 0.0019 | - |
| 5.2969 | 12400 | 0.0027 | - |
| 5.3182 | 12450 | 0.0013 | - |
| 5.3396 | 12500 | 0.0023 | - |
| 5.3610 | 12550 | 0.0015 | - |
| 5.3823 | 12600 | 0.0026 | - |
| 5.4037 | 12650 | 0.0014 | - |
| 5.4250 | 12700 | 0.0016 | - |
| 5.4464 | 12750 | 0.0017 | - |
| 5.4677 | 12800 | 0.0013 | - |
| 5.4891 | 12850 | 0.002 | - |
| 5.5105 | 12900 | 0.0028 | - |
| 5.5318 | 12950 | 0.0021 | - |
| 5.5532 | 13000 | 0.0028 | - |
| 5.5745 | 13050 | 0.0016 | - |
| 5.5959 | 13100 | 0.0012 | - |
| 5.6173 | 13150 | 0.0031 | - |
| 5.6386 | 13200 | 0.0023 | - |
| 5.6600 | 13250 | 0.0017 | - |
| 5.6813 | 13300 | 0.0016 | - |
| 5.7027 | 13350 | 0.0018 | - |
| 5.7240 | 13400 | 0.0028 | - |
| 5.7454 | 13450 | 0.0029 | - |
| 5.7668 | 13500 | 0.0013 | - |
| 5.7881 | 13550 | 0.0012 | - |
| 5.8095 | 13600 | 0.0017 | - |
| 5.8308 | 13650 | 0.001 | - |
| 5.8522 | 13700 | 0.0017 | - |
| 5.8736 | 13750 | 0.0019 | - |
| 5.8949 | 13800 | 0.0013 | - |
| 5.9163 | 13850 | 0.001 | - |
| 5.9376 | 13900 | 0.0015 | - |
| 5.9590 | 13950 | 0.0013 | - |
| 5.9804 | 14000 | 0.0012 | - |
| 6.0017 | 14050 | 0.0013 | - |
| 6.0231 | 14100 | 0.0006 | - |
| 6.0444 | 14150 | 0.0013 | - |
| 6.0658 | 14200 | 0.0013 | - |
| 6.0871 | 14250 | 0.0012 | - |
| 6.1085 | 14300 | 0.0009 | - |
| 6.1299 | 14350 | 0.0013 | - |
| 6.1512 | 14400 | 0.0015 | - |
| 6.1726 | 14450 | 0.004 | - |
| 6.1939 | 14500 | 0.0038 | - |
| 6.2153 | 14550 | 0.0026 | - |
| 6.2367 | 14600 | 0.002 | - |
| 6.2580 | 14650 | 0.0017 | - |
| 6.2794 | 14700 | 0.002 | - |
| 6.3007 | 14750 | 0.0016 | - |
| 6.3221 | 14800 | 0.0013 | - |
| 6.3434 | 14850 | 0.001 | - |
| 6.3648 | 14900 | 0.002 | - |
| 6.3862 | 14950 | 0.0021 | - |
| 6.4075 | 15000 | 0.0012 | - |
| 6.4289 | 15050 | 0.0009 | - |
| 6.4502 | 15100 | 0.0013 | - |
| 6.4716 | 15150 | 0.0015 | - |
| 6.4930 | 15200 | 0.0005 | - |
| 6.5143 | 15250 | 0.0013 | - |
| 6.5357 | 15300 | 0.0013 | - |
| 6.5570 | 15350 | 0.0009 | - |
| 6.5784 | 15400 | 0.0014 | - |
| 6.5997 | 15450 | 0.0009 | - |
| 6.6211 | 15500 | 0.0015 | - |
| 6.6425 | 15550 | 0.0014 | - |
| 6.6638 | 15600 | 0.0015 | - |
| 6.6852 | 15650 | 0.0021 | - |
| 6.7065 | 15700 | 0.0021 | - |
| 6.7279 | 15750 | 0.0013 | - |
| 6.7493 | 15800 | 0.0018 | - |
| 6.7706 | 15850 | 0.0017 | - |
| 6.7920 | 15900 | 0.0016 | - |
| 6.8133 | 15950 | 0.0014 | - |
| 6.8347 | 16000 | 0.001 | - |
| 6.8560 | 16050 | 0.0013 | - |
| 6.8774 | 16100 | 0.0005 | - |
| 6.8988 | 16150 | 0.0009 | - |
| 6.9201 | 16200 | 0.0015 | - |
| 6.9415 | 16250 | 0.0014 | - |
| 6.9628 | 16300 | 0.0029 | - |
| 6.9842 | 16350 | 0.0024 | - |
| 7.0056 | 16400 | 0.0016 | - |
| 7.0269 | 16450 | 0.0012 | - |
| 7.0483 | 16500 | 0.0013 | - |
| 7.0696 | 16550 | 0.0014 | - |
| 7.0910 | 16600 | 0.0007 | - |
| 7.1123 | 16650 | 0.0018 | - |
| 7.1337 | 16700 | 0.0007 | - |
| 7.1551 | 16750 | 0.0009 | - |
| 7.1764 | 16800 | 0.0011 | - |
| 7.1978 | 16850 | 0.0007 | - |
| 7.2191 | 16900 | 0.0009 | - |
| 7.2405 | 16950 | 0.0006 | - |
| 7.2619 | 17000 | 0.0014 | - |
| 7.2832 | 17050 | 0.0009 | - |
| 7.3046 | 17100 | 0.0007 | - |
| 7.3259 | 17150 | 0.0009 | - |
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| 17.5566 | 41100 | 0.0002 | - |
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| 17.5993 | 41200 | 0.0002 | - |
| 17.6207 | 41250 | 0.0002 | - |
| 17.6420 | 41300 | 0.0002 | - |
| 17.6634 | 41350 | 0.0001 | - |
| 17.6848 | 41400 | 0.0001 | - |
| 17.7061 | 41450 | 0.0002 | - |
| 17.7275 | 41500 | 0.0002 | - |
| 17.7488 | 41550 | 0.0002 | - |
| 17.7702 | 41600 | 0.0 | - |
| 17.7915 | 41650 | 0.0003 | - |
| 17.8129 | 41700 | 0.0001 | - |
| 17.8343 | 41750 | 0.0 | - |
| 17.8556 | 41800 | 0.0001 | - |
| 17.8770 | 41850 | 0.0001 | - |
| 17.8983 | 41900 | 0.0002 | - |
| 17.9197 | 41950 | 0.0001 | - |
| 17.9411 | 42000 | 0.0003 | - |
| 17.9624 | 42050 | 0.0001 | - |
| 17.9838 | 42100 | 0.0001 | - |
| 18.0051 | 42150 | 0.0001 | - |
| 18.0265 | 42200 | 0.0002 | - |
| 18.0478 | 42250 | 0.0005 | - |
| 18.0692 | 42300 | 0.0002 | - |
| 18.0906 | 42350 | 0.0003 | - |
| 18.1119 | 42400 | 0.0001 | - |
| 18.1333 | 42450 | 0.0004 | - |
| 18.1546 | 42500 | 0.0002 | - |
| 18.1760 | 42550 | 0.0002 | - |
| 18.1974 | 42600 | 0.0002 | - |
| 18.2187 | 42650 | 0.0003 | - |
| 18.2401 | 42700 | 0.0001 | - |
| 18.2614 | 42750 | 0.0001 | - |
| 18.2828 | 42800 | 0.0001 | - |
| 18.3041 | 42850 | 0.0 | - |
| 18.3255 | 42900 | 0.0 | - |
| 18.3469 | 42950 | 0.0001 | - |
| 18.3682 | 43000 | 0.0 | - |
| 18.3896 | 43050 | 0.0001 | - |
| 18.4109 | 43100 | 0.0001 | - |
| 18.4323 | 43150 | 0.0002 | - |
| 18.4537 | 43200 | 0.0001 | - |
| 18.4750 | 43250 | 0.0001 | - |
| 18.4964 | 43300 | 0.0001 | - |
| 18.5177 | 43350 | 0.0001 | - |
| 18.5391 | 43400 | 0.0 | - |
| 18.5604 | 43450 | 0.0 | - |
| 18.5818 | 43500 | 0.0001 | - |
| 18.6032 | 43550 | 0.0001 | - |
| 18.6245 | 43600 | 0.0001 | - |
| 18.6459 | 43650 | 0.0004 | - |
| 18.6672 | 43700 | 0.0003 | - |
| 18.6886 | 43750 | 0.0002 | - |
| 18.7100 | 43800 | 0.0001 | - |
| 18.7313 | 43850 | 0.0001 | - |
| 18.7527 | 43900 | 0.0001 | - |
| 18.7740 | 43950 | 0.0001 | - |
| 18.7954 | 44000 | 0.0002 | - |
| 18.8167 | 44050 | 0.0003 | - |
| 18.8381 | 44100 | 0.0001 | - |
| 18.8595 | 44150 | 0.0001 | - |
| 18.8808 | 44200 | 0.0001 | - |
| 18.9022 | 44250 | 0.0003 | - |
| 18.9235 | 44300 | 0.0001 | - |
| 18.9449 | 44350 | 0.0001 | - |
| 18.9663 | 44400 | 0.0 | - |
| 18.9876 | 44450 | 0.0 | - |
| 19.0090 | 44500 | 0.0003 | - |
| 19.0303 | 44550 | 0.0002 | - |
| 19.0517 | 44600 | 0.0002 | - |
| 19.0730 | 44650 | 0.0003 | - |
| 19.0944 | 44700 | 0.0002 | - |
| 19.1158 | 44750 | 0.0001 | - |
| 19.1371 | 44800 | 0.0001 | - |
| 19.1585 | 44850 | 0.0001 | - |
| 19.1798 | 44900 | 0.0 | - |
| 19.2012 | 44950 | 0.0001 | - |
| 19.2226 | 45000 | 0.0003 | - |
| 19.2439 | 45050 | 0.0001 | - |
| 19.2653 | 45100 | 0.0 | - |
| 19.2866 | 45150 | 0.0 | - |
| 19.3080 | 45200 | 0.0001 | - |
| 19.3293 | 45250 | 0.0001 | - |
| 19.3507 | 45300 | 0.0002 | - |
| 19.3721 | 45350 | 0.0002 | - |
| 19.3934 | 45400 | 0.0003 | - |
| 19.4148 | 45450 | 0.0001 | - |
| 19.4361 | 45500 | 0.0 | - |
| 19.4575 | 45550 | 0.0001 | - |
| 19.4789 | 45600 | 0.0 | - |
| 19.5002 | 45650 | 0.0002 | - |
| 19.5216 | 45700 | 0.0 | - |
| 19.5429 | 45750 | 0.0001 | - |
| 19.5643 | 45800 | 0.0001 | - |
| 19.5856 | 45850 | 0.0001 | - |
| 19.6070 | 45900 | 0.0001 | - |
| 19.6284 | 45950 | 0.0001 | - |
| 19.6497 | 46000 | 0.0 | - |
| 19.6711 | 46050 | 0.0001 | - |
| 19.6924 | 46100 | 0.0 | - |
| 19.7138 | 46150 | 0.0002 | - |
| 19.7352 | 46200 | 0.0001 | - |
| 19.7565 | 46250 | 0.0002 | - |
| 19.7779 | 46300 | 0.0002 | - |
| 19.7992 | 46350 | 0.0001 | - |
| 19.8206 | 46400 | 0.0001 | - |
| 19.8419 | 46450 | 0.0002 | - |
| 19.8633 | 46500 | 0.0003 | - |
| 19.8847 | 46550 | 0.0002 | - |
| 19.9060 | 46600 | 0.0002 | - |
| 19.9274 | 46650 | 0.0001 | - |
| 19.9487 | 46700 | 0.0001 | - |
| 19.9701 | 46750 | 0.0 | - |
| 19.9915 | 46800 | 0.0002 | - |
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}
}
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