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: 18 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 |
---|---|
10 |
|
7 |
|
4 |
|
3 |
|
11 |
|
12 |
|
8 |
|
5 |
|
6 |
|
15 |
|
16 |
|
1 |
|
14 |
|
2 |
|
17 |
|
13 |
|
9 |
|
0 |
|
Evaluation
Metrics
Label | Metric |
---|---|
all | 0.9798 |
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("setfit_model_id")
# Run inference
preds = model("해외직구 > 건강식품 > 칼슘")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 1 | 7.8919 | 45 |
Label | Training Sample Count |
---|---|
0 | 52 |
1 | 422 |
2 | 377 |
3 | 535 |
4 | 4826 |
5 | 4085 |
6 | 3868 |
7 | 3223 |
8 | 3998 |
9 | 19 |
10 | 887 |
11 | 22087 |
12 | 2307 |
13 | 113 |
14 | 1409 |
15 | 2267 |
16 | 2404 |
17 | 929 |
Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (10, 10)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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 |
---|---|---|---|
0.0002 | 1 | 0.2773 | - |
0.0119 | 50 | 0.2679 | - |
0.0238 | 100 | 0.2132 | - |
0.0357 | 150 | 0.1508 | - |
0.0476 | 200 | 0.1032 | - |
0.0595 | 250 | 0.0765 | - |
0.0714 | 300 | 0.0692 | - |
0.0833 | 350 | 0.0675 | - |
0.0951 | 400 | 0.05 | - |
0.1070 | 450 | 0.0564 | - |
0.1189 | 500 | 0.0408 | - |
0.1308 | 550 | 0.0309 | - |
0.1427 | 600 | 0.029 | - |
0.1546 | 650 | 0.0268 | - |
0.1665 | 700 | 0.0357 | - |
0.1784 | 750 | 0.0295 | - |
0.1903 | 800 | 0.0242 | - |
0.2022 | 850 | 0.026 | - |
0.2141 | 900 | 0.0225 | - |
0.2260 | 950 | 0.0266 | - |
0.2379 | 1000 | 0.0193 | - |
0.2498 | 1050 | 0.0179 | - |
0.2617 | 1100 | 0.0208 | - |
0.2735 | 1150 | 0.0238 | - |
0.2854 | 1200 | 0.0196 | - |
0.2973 | 1250 | 0.0126 | - |
0.3092 | 1300 | 0.0194 | - |
0.3211 | 1350 | 0.0124 | - |
0.3330 | 1400 | 0.0175 | - |
0.3449 | 1450 | 0.0163 | - |
0.3568 | 1500 | 0.0097 | - |
0.3687 | 1550 | 0.0083 | - |
0.3806 | 1600 | 0.0192 | - |
0.3925 | 1650 | 0.0078 | - |
0.4044 | 1700 | 0.012 | - |
0.4163 | 1750 | 0.0087 | - |
0.4282 | 1800 | 0.0123 | - |
0.4401 | 1850 | 0.0149 | - |
0.4520 | 1900 | 0.0113 | - |
0.4638 | 1950 | 0.0102 | - |
0.4757 | 2000 | 0.0075 | - |
0.4876 | 2050 | 0.0049 | - |
0.4995 | 2100 | 0.0132 | - |
0.5114 | 2150 | 0.0044 | - |
0.5233 | 2200 | 0.0061 | - |
0.5352 | 2250 | 0.0088 | - |
0.5471 | 2300 | 0.0103 | - |
0.5590 | 2350 | 0.0107 | - |
0.5709 | 2400 | 0.0111 | - |
0.5828 | 2450 | 0.0119 | - |
0.5947 | 2500 | 0.0044 | - |
0.6066 | 2550 | 0.0105 | - |
0.6185 | 2600 | 0.0056 | - |
0.6304 | 2650 | 0.0089 | - |
0.6422 | 2700 | 0.0062 | - |
0.6541 | 2750 | 0.0099 | - |
0.6660 | 2800 | 0.0047 | - |
0.6779 | 2850 | 0.015 | - |
0.6898 | 2900 | 0.0034 | - |
0.7017 | 2950 | 0.0061 | - |
0.7136 | 3000 | 0.0077 | - |
0.7255 | 3050 | 0.0097 | - |
0.7374 | 3100 | 0.0071 | - |
0.7493 | 3150 | 0.0062 | - |
0.7612 | 3200 | 0.0157 | - |
0.7731 | 3250 | 0.0026 | - |
0.7850 | 3300 | 0.0048 | - |
0.7969 | 3350 | 0.0039 | - |
0.8088 | 3400 | 0.0088 | - |
0.8206 | 3450 | 0.0011 | - |
0.8325 | 3500 | 0.0034 | - |
0.8444 | 3550 | 0.0031 | - |
0.8563 | 3600 | 0.0033 | - |
0.8682 | 3650 | 0.0117 | - |
0.8801 | 3700 | 0.0073 | - |
0.8920 | 3750 | 0.0047 | - |
0.9039 | 3800 | 0.0008 | - |
0.9158 | 3850 | 0.0062 | - |
0.9277 | 3900 | 0.0032 | - |
0.9396 | 3950 | 0.0033 | - |
0.9515 | 4000 | 0.0081 | - |
0.9634 | 4050 | 0.0123 | - |
0.9753 | 4100 | 0.0025 | - |
0.9872 | 4150 | 0.0078 | - |
0.9990 | 4200 | 0.0047 | - |
1.0109 | 4250 | 0.0027 | - |
1.0228 | 4300 | 0.0052 | - |
1.0347 | 4350 | 0.0064 | - |
1.0466 | 4400 | 0.0092 | - |
1.0585 | 4450 | 0.0034 | - |
1.0704 | 4500 | 0.0046 | - |
1.0823 | 4550 | 0.0071 | - |
1.0942 | 4600 | 0.0061 | - |
1.1061 | 4650 | 0.0043 | - |
1.1180 | 4700 | 0.0052 | - |
1.1299 | 4750 | 0.0029 | - |
1.1418 | 4800 | 0.001 | - |
1.1537 | 4850 | 0.0053 | - |
1.1656 | 4900 | 0.0029 | - |
1.1775 | 4950 | 0.0003 | - |
1.1893 | 5000 | 0.0012 | - |
1.2012 | 5050 | 0.0014 | - |
1.2131 | 5100 | 0.0021 | - |
1.2250 | 5150 | 0.0024 | - |
1.2369 | 5200 | 0.0015 | - |
1.2488 | 5250 | 0.0057 | - |
1.2607 | 5300 | 0.0037 | - |
1.2726 | 5350 | 0.0088 | - |
1.2845 | 5400 | 0.01 | - |
1.2964 | 5450 | 0.0059 | - |
1.3083 | 5500 | 0.0016 | - |
1.3202 | 5550 | 0.004 | - |
1.3321 | 5600 | 0.0022 | - |
1.3440 | 5650 | 0.0044 | - |
1.3559 | 5700 | 0.0084 | - |
1.3677 | 5750 | 0.0046 | - |
1.3796 | 5800 | 0.0043 | - |
1.3915 | 5850 | 0.0044 | - |
1.4034 | 5900 | 0.0051 | - |
1.4153 | 5950 | 0.0051 | - |
1.4272 | 6000 | 0.0048 | - |
1.4391 | 6050 | 0.0021 | - |
1.4510 | 6100 | 0.0041 | - |
1.4629 | 6150 | 0.0047 | - |
1.4748 | 6200 | 0.0048 | - |
1.4867 | 6250 | 0.0019 | - |
1.4986 | 6300 | 0.005 | - |
1.5105 | 6350 | 0.0001 | - |
1.5224 | 6400 | 0.0004 | - |
1.5343 | 6450 | 0.0012 | - |
1.5461 | 6500 | 0.0003 | - |
1.5580 | 6550 | 0.0042 | - |
1.5699 | 6600 | 0.0022 | - |
1.5818 | 6650 | 0.0021 | - |
1.5937 | 6700 | 0.0014 | - |
1.6056 | 6750 | 0.0002 | - |
1.6175 | 6800 | 0.0014 | - |
1.6294 | 6850 | 0.0057 | - |
1.6413 | 6900 | 0.0023 | - |
1.6532 | 6950 | 0.0024 | - |
1.6651 | 7000 | 0.0028 | - |
1.6770 | 7050 | 0.0017 | - |
1.6889 | 7100 | 0.0056 | - |
1.7008 | 7150 | 0.0003 | - |
1.7127 | 7200 | 0.0006 | - |
1.7245 | 7250 | 0.0055 | - |
1.7364 | 7300 | 0.0001 | - |
1.7483 | 7350 | 0.0071 | - |
1.7602 | 7400 | 0.0013 | - |
1.7721 | 7450 | 0.0021 | - |
1.7840 | 7500 | 0.0022 | - |
1.7959 | 7550 | 0.001 | - |
1.8078 | 7600 | 0.0075 | - |
1.8197 | 7650 | 0.0003 | - |
1.8316 | 7700 | 0.0004 | - |
1.8435 | 7750 | 0.0004 | - |
1.8554 | 7800 | 0.0023 | - |
1.8673 | 7850 | 0.0032 | - |
1.8792 | 7900 | 0.0021 | - |
1.8911 | 7950 | 0.0028 | - |
1.9029 | 8000 | 0.0031 | - |
1.9148 | 8050 | 0.002 | - |
1.9267 | 8100 | 0.0041 | - |
1.9386 | 8150 | 0.0027 | - |
1.9505 | 8200 | 0.0003 | - |
1.9624 | 8250 | 0.0062 | - |
1.9743 | 8300 | 0.0005 | - |
1.9862 | 8350 | 0.0044 | - |
1.9981 | 8400 | 0.0016 | - |
2.0100 | 8450 | 0.0002 | - |
2.0219 | 8500 | 0.0003 | - |
2.0338 | 8550 | 0.0021 | - |
2.0457 | 8600 | 0.0027 | - |
2.0576 | 8650 | 0.001 | - |
2.0695 | 8700 | 0.0004 | - |
2.0814 | 8750 | 0.0027 | - |
2.0932 | 8800 | 0.0003 | - |
2.1051 | 8850 | 0.0015 | - |
2.1170 | 8900 | 0.002 | - |
2.1289 | 8950 | 0.0005 | - |
2.1408 | 9000 | 0.0067 | - |
2.1527 | 9050 | 0.001 | - |
2.1646 | 9100 | 0.0024 | - |
2.1765 | 9150 | 0.0004 | - |
2.1884 | 9200 | 0.0038 | - |
2.2003 | 9250 | 0.0001 | - |
2.2122 | 9300 | 0.0048 | - |
2.2241 | 9350 | 0.0021 | - |
2.2360 | 9400 | 0.0031 | - |
2.2479 | 9450 | 0.0024 | - |
2.2598 | 9500 | 0.0006 | - |
2.2716 | 9550 | 0.007 | - |
2.2835 | 9600 | 0.0001 | - |
2.2954 | 9650 | 0.0018 | - |
2.3073 | 9700 | 0.0013 | - |
2.3192 | 9750 | 0.0059 | - |
2.3311 | 9800 | 0.0012 | - |
2.3430 | 9850 | 0.0028 | - |
2.3549 | 9900 | 0.0025 | - |
2.3668 | 9950 | 0.0006 | - |
2.3787 | 10000 | 0.0005 | - |
2.3906 | 10050 | 0.0001 | - |
2.4025 | 10100 | 0.0002 | - |
2.4144 | 10150 | 0.0009 | - |
2.4263 | 10200 | 0.0004 | - |
2.4382 | 10250 | 0.001 | - |
2.4500 | 10300 | 0.0003 | - |
2.4619 | 10350 | 0.0003 | - |
2.4738 | 10400 | 0.0026 | - |
2.4857 | 10450 | 0.0002 | - |
2.4976 | 10500 | 0.0045 | - |
2.5095 | 10550 | 0.0017 | - |
2.5214 | 10600 | 0.0002 | - |
2.5333 | 10650 | 0.0018 | - |
2.5452 | 10700 | 0.0001 | - |
2.5571 | 10750 | 0.0023 | - |
2.5690 | 10800 | 0.0013 | - |
2.5809 | 10850 | 0.0022 | - |
2.5928 | 10900 | 0.0036 | - |
2.6047 | 10950 | 0.0012 | - |
2.6166 | 11000 | 0.0028 | - |
2.6284 | 11050 | 0.0019 | - |
2.6403 | 11100 | 0.0001 | - |
2.6522 | 11150 | 0.0044 | - |
2.6641 | 11200 | 0.0012 | - |
2.6760 | 11250 | 0.0013 | - |
2.6879 | 11300 | 0.0001 | - |
2.6998 | 11350 | 0.0016 | - |
2.7117 | 11400 | 0.0037 | - |
2.7236 | 11450 | 0.0003 | - |
2.7355 | 11500 | 0.0004 | - |
2.7474 | 11550 | 0.0055 | - |
2.7593 | 11600 | 0.0002 | - |
2.7712 | 11650 | 0.0001 | - |
2.7831 | 11700 | 0.0006 | - |
2.7950 | 11750 | 0.0061 | - |
2.8069 | 11800 | 0.0007 | - |
2.8187 | 11850 | 0.0027 | - |
2.8306 | 11900 | 0.0022 | - |
2.8425 | 11950 | 0.0002 | - |
2.8544 | 12000 | 0.0022 | - |
2.8663 | 12050 | 0.0015 | - |
2.8782 | 12100 | 0.0003 | - |
2.8901 | 12150 | 0.001 | - |
2.9020 | 12200 | 0.0014 | - |
2.9139 | 12250 | 0.0001 | - |
2.9258 | 12300 | 0.0009 | - |
2.9377 | 12350 | 0.0007 | - |
2.9496 | 12400 | 0.0005 | - |
2.9615 | 12450 | 0.0004 | - |
2.9734 | 12500 | 0.0004 | - |
2.9853 | 12550 | 0.0026 | - |
2.9971 | 12600 | 0.0011 | - |
3.0090 | 12650 | 0.0019 | - |
3.0209 | 12700 | 0.0 | - |
3.0328 | 12750 | 0.0004 | - |
3.0447 | 12800 | 0.0004 | - |
3.0566 | 12850 | 0.0001 | - |
3.0685 | 12900 | 0.0003 | - |
3.0804 | 12950 | 0.0003 | - |
3.0923 | 13000 | 0.0015 | - |
3.1042 | 13050 | 0.0018 | - |
3.1161 | 13100 | 0.002 | - |
3.1280 | 13150 | 0.0018 | - |
3.1399 | 13200 | 0.0002 | - |
3.1518 | 13250 | 0.0003 | - |
3.1637 | 13300 | 0.0007 | - |
3.1755 | 13350 | 0.0002 | - |
3.1874 | 13400 | 0.0014 | - |
3.1993 | 13450 | 0.0026 | - |
3.2112 | 13500 | 0.0005 | - |
3.2231 | 13550 | 0.0015 | - |
3.2350 | 13600 | 0.0012 | - |
3.2469 | 13650 | 0.0029 | - |
3.2588 | 13700 | 0.0001 | - |
3.2707 | 13750 | 0.0001 | - |
3.2826 | 13800 | 0.0013 | - |
3.2945 | 13850 | 0.0021 | - |
3.3064 | 13900 | 0.0002 | - |
3.3183 | 13950 | 0.0014 | - |
3.3302 | 14000 | 0.0021 | - |
3.3421 | 14050 | 0.0011 | - |
3.3539 | 14100 | 0.0007 | - |
3.3658 | 14150 | 0.0015 | - |
3.3777 | 14200 | 0.0022 | - |
3.3896 | 14250 | 0.0 | - |
3.4015 | 14300 | 0.0008 | - |
3.4134 | 14350 | 0.0002 | - |
3.4253 | 14400 | 0.0002 | - |
3.4372 | 14450 | 0.002 | - |
3.4491 | 14500 | 0.0019 | - |
3.4610 | 14550 | 0.0018 | - |
3.4729 | 14600 | 0.0001 | - |
3.4848 | 14650 | 0.002 | - |
3.4967 | 14700 | 0.0003 | - |
3.5086 | 14750 | 0.0004 | - |
3.5205 | 14800 | 0.0003 | - |
3.5324 | 14850 | 0.0019 | - |
3.5442 | 14900 | 0.0005 | - |
3.5561 | 14950 | 0.0007 | - |
3.5680 | 15000 | 0.0023 | - |
3.5799 | 15050 | 0.0019 | - |
3.5918 | 15100 | 0.0002 | - |
3.6037 | 15150 | 0.002 | - |
3.6156 | 15200 | 0.0023 | - |
3.6275 | 15250 | 0.0019 | - |
3.6394 | 15300 | 0.0005 | - |
3.6513 | 15350 | 0.0001 | - |
3.6632 | 15400 | 0.0009 | - |
3.6751 | 15450 | 0.0003 | - |
3.6870 | 15500 | 0.0052 | - |
3.6989 | 15550 | 0.0058 | - |
3.7108 | 15600 | 0.0003 | - |
3.7226 | 15650 | 0.0011 | - |
3.7345 | 15700 | 0.003 | - |
3.7464 | 15750 | 0.0003 | - |
3.7583 | 15800 | 0.0001 | - |
3.7702 | 15850 | 0.0004 | - |
3.7821 | 15900 | 0.0004 | - |
3.7940 | 15950 | 0.0001 | - |
3.8059 | 16000 | 0.0009 | - |
3.8178 | 16050 | 0.002 | - |
3.8297 | 16100 | 0.0004 | - |
3.8416 | 16150 | 0.0001 | - |
3.8535 | 16200 | 0.0004 | - |
3.8654 | 16250 | 0.0001 | - |
3.8773 | 16300 | 0.0014 | - |
3.8892 | 16350 | 0.002 | - |
3.9010 | 16400 | 0.0023 | - |
3.9129 | 16450 | 0.002 | - |
3.9248 | 16500 | 0.0004 | - |
3.9367 | 16550 | 0.0002 | - |
3.9486 | 16600 | 0.0001 | - |
3.9605 | 16650 | 0.0007 | - |
3.9724 | 16700 | 0.0009 | - |
3.9843 | 16750 | 0.0002 | - |
3.9962 | 16800 | 0.0006 | - |
4.0081 | 16850 | 0.0001 | - |
4.0200 | 16900 | 0.0004 | - |
4.0319 | 16950 | 0.0014 | - |
4.0438 | 17000 | 0.0001 | - |
4.0557 | 17050 | 0.001 | - |
4.0676 | 17100 | 0.0003 | - |
4.0794 | 17150 | 0.0045 | - |
4.0913 | 17200 | 0.0039 | - |
4.1032 | 17250 | 0.0005 | - |
4.1151 | 17300 | 0.001 | - |
4.1270 | 17350 | 0.0019 | - |
4.1389 | 17400 | 0.0 | - |
4.1508 | 17450 | 0.0003 | - |
4.1627 | 17500 | 0.0007 | - |
4.1746 | 17550 | 0.0052 | - |
4.1865 | 17600 | 0.0002 | - |
4.1984 | 17650 | 0.0006 | - |
4.2103 | 17700 | 0.0001 | - |
4.2222 | 17750 | 0.0 | - |
4.2341 | 17800 | 0.0002 | - |
4.2460 | 17850 | 0.0003 | - |
4.2578 | 17900 | 0.0012 | - |
4.2697 | 17950 | 0.0005 | - |
4.2816 | 18000 | 0.0003 | - |
4.2935 | 18050 | 0.0031 | - |
4.3054 | 18100 | 0.0026 | - |
4.3173 | 18150 | 0.001 | - |
4.3292 | 18200 | 0.0 | - |
4.3411 | 18250 | 0.0002 | - |
4.3530 | 18300 | 0.0006 | - |
4.3649 | 18350 | 0.0018 | - |
4.3768 | 18400 | 0.0003 | - |
4.3887 | 18450 | 0.0012 | - |
4.4006 | 18500 | 0.0 | - |
4.4125 | 18550 | 0.0001 | - |
4.4244 | 18600 | 0.002 | - |
4.4363 | 18650 | 0.0012 | - |
4.4481 | 18700 | 0.0021 | - |
4.4600 | 18750 | 0.0002 | - |
4.4719 | 18800 | 0.0015 | - |
4.4838 | 18850 | 0.0002 | - |
4.4957 | 18900 | 0.0 | - |
4.5076 | 18950 | 0.0003 | - |
4.5195 | 19000 | 0.0001 | - |
4.5314 | 19050 | 0.001 | - |
4.5433 | 19100 | 0.0001 | - |
4.5552 | 19150 | 0.0 | - |
4.5671 | 19200 | 0.0017 | - |
4.5790 | 19250 | 0.0003 | - |
4.5909 | 19300 | 0.001 | - |
4.6028 | 19350 | 0.0015 | - |
4.6147 | 19400 | 0.0001 | - |
4.6265 | 19450 | 0.0001 | - |
4.6384 | 19500 | 0.0022 | - |
4.6503 | 19550 | 0.0005 | - |
4.6622 | 19600 | 0.0003 | - |
4.6741 | 19650 | 0.0009 | - |
4.6860 | 19700 | 0.0001 | - |
4.6979 | 19750 | 0.0018 | - |
4.7098 | 19800 | 0.0001 | - |
4.7217 | 19850 | 0.0012 | - |
4.7336 | 19900 | 0.0002 | - |
4.7455 | 19950 | 0.0003 | - |
4.7574 | 20000 | 0.0006 | - |
4.7693 | 20050 | 0.0011 | - |
4.7812 | 20100 | 0.0033 | - |
4.7931 | 20150 | 0.0003 | - |
4.8049 | 20200 | 0.001 | - |
4.8168 | 20250 | 0.003 | - |
4.8287 | 20300 | 0.0035 | - |
4.8406 | 20350 | 0.0001 | - |
4.8525 | 20400 | 0.0002 | - |
4.8644 | 20450 | 0.0006 | - |
4.8763 | 20500 | 0.0 | - |
4.8882 | 20550 | 0.003 | - |
4.9001 | 20600 | 0.0001 | - |
4.9120 | 20650 | 0.0001 | - |
4.9239 | 20700 | 0.0002 | - |
4.9358 | 20750 | 0.0007 | - |
4.9477 | 20800 | 0.0002 | - |
4.9596 | 20850 | 0.0007 | - |
4.9715 | 20900 | 0.0032 | - |
4.9833 | 20950 | 0.0002 | - |
4.9952 | 21000 | 0.0 | - |
5.0071 | 21050 | 0.0018 | - |
5.0190 | 21100 | 0.0002 | - |
5.0309 | 21150 | 0.0017 | - |
5.0428 | 21200 | 0.0013 | - |
5.0547 | 21250 | 0.0014 | - |
5.0666 | 21300 | 0.0 | - |
5.0785 | 21350 | 0.0001 | - |
5.0904 | 21400 | 0.0001 | - |
5.1023 | 21450 | 0.0001 | - |
5.1142 | 21500 | 0.0022 | - |
5.1261 | 21550 | 0.0004 | - |
5.1380 | 21600 | 0.0002 | - |
5.1499 | 21650 | 0.0016 | - |
5.1618 | 21700 | 0.0036 | - |
5.1736 | 21750 | 0.0021 | - |
5.1855 | 21800 | 0.0018 | - |
5.1974 | 21850 | 0.0005 | - |
5.2093 | 21900 | 0.0024 | - |
5.2212 | 21950 | 0.0004 | - |
5.2331 | 22000 | 0.0002 | - |
5.2450 | 22050 | 0.0 | - |
5.2569 | 22100 | 0.0019 | - |
5.2688 | 22150 | 0.0001 | - |
5.2807 | 22200 | 0.0001 | - |
5.2926 | 22250 | 0.0014 | - |
5.3045 | 22300 | 0.0001 | - |
5.3164 | 22350 | 0.0018 | - |
5.3283 | 22400 | 0.0006 | - |
5.3402 | 22450 | 0.0004 | - |
5.3520 | 22500 | 0.0003 | - |
5.3639 | 22550 | 0.0008 | - |
5.3758 | 22600 | 0.0002 | - |
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9.3126 | 39150 | 0.0003 | - |
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9.5029 | 39950 | 0.0013 | - |
9.5147 | 40000 | 0.002 | - |
9.5266 | 40050 | 0.0003 | - |
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9.5623 | 40200 | 0.0016 | - |
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9.7526 | 41000 | 0.0 | - |
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9.8002 | 41200 | 0.0007 | - |
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9.8240 | 41300 | 0.002 | - |
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9.8478 | 41400 | 0.0019 | - |
9.8597 | 41450 | 0.0039 | - |
9.8716 | 41500 | 0.0001 | - |
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9.8953 | 41600 | 0.0007 | - |
9.9072 | 41650 | 0.0 | - |
9.9191 | 41700 | 0.0003 | - |
9.9310 | 41750 | 0.0012 | - |
9.9429 | 41800 | 0.0001 | - |
9.9548 | 41850 | 0.0001 | - |
9.9667 | 41900 | 0.0002 | - |
9.9786 | 41950 | 0.0002 | - |
9.9905 | 42000 | 0.0023 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.1.1
- Transformers: 4.45.1
- 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}
}
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