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: 31 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 |
---|---|
20.0 |
|
24.0 |
|
21.0 |
|
17.0 |
|
15.0 |
|
29.0 |
|
5.0 |
|
14.0 |
|
19.0 |
|
3.0 |
|
27.0 |
|
18.0 |
|
4.0 |
|
28.0 |
|
1.0 |
|
13.0 |
|
0.0 |
|
8.0 |
|
11.0 |
|
10.0 |
|
26.0 |
|
30.0 |
|
9.0 |
|
22.0 |
|
23.0 |
|
7.0 |
|
12.0 |
|
2.0 |
|
6.0 |
|
16.0 |
|
25.0 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 1.0 |
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_bc")
# Run inference
preds = model("뮤트뱀부 래빗 유아방수요 뮤트라인_인디핑크_M 출산/육아 > 유아침구 > 방수요")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 7 | 15.3246 | 42 |
Label | Training Sample Count |
---|---|
0.0 | 210 |
1.0 | 420 |
2.0 | 80 |
3.0 | 196 |
4.0 | 770 |
5.0 | 490 |
6.0 | 210 |
7.0 | 439 |
8.0 | 280 |
9.0 | 840 |
10.0 | 930 |
11.0 | 560 |
12.0 | 210 |
13.0 | 348 |
14.0 | 650 |
15.0 | 930 |
16.0 | 350 |
17.0 | 700 |
18.0 | 700 |
19.0 | 1740 |
20.0 | 1210 |
21.0 | 370 |
22.0 | 280 |
23.0 | 350 |
24.0 | 630 |
25.0 | 140 |
26.0 | 560 |
27.0 | 490 |
28.0 | 505 |
29.0 | 700 |
30.0 | 150 |
Training Hyperparameters
- batch_size: (256, 256)
- num_epochs: (30, 30)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 50
- 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 |
---|---|---|---|
0.0003 | 1 | 0.2935 | - |
0.0156 | 50 | 0.282 | - |
0.0311 | 100 | 0.2556 | - |
0.0467 | 150 | 0.22 | - |
0.0623 | 200 | 0.1921 | - |
0.0779 | 250 | 0.1653 | - |
0.0934 | 300 | 0.1377 | - |
0.1090 | 350 | 0.1105 | - |
0.1246 | 400 | 0.0842 | - |
0.1401 | 450 | 0.0677 | - |
0.1557 | 500 | 0.0582 | - |
0.1713 | 550 | 0.052 | - |
0.1869 | 600 | 0.047 | - |
0.2024 | 650 | 0.0429 | - |
0.2180 | 700 | 0.0339 | - |
0.2336 | 750 | 0.0298 | - |
0.2491 | 800 | 0.0259 | - |
0.2647 | 850 | 0.0247 | - |
0.2803 | 900 | 0.0228 | - |
0.2959 | 950 | 0.0214 | - |
0.3114 | 1000 | 0.017 | - |
0.3270 | 1050 | 0.0173 | - |
0.3426 | 1100 | 0.0149 | - |
0.3581 | 1150 | 0.0138 | - |
0.3737 | 1200 | 0.0128 | - |
0.3893 | 1250 | 0.0122 | - |
0.4049 | 1300 | 0.0112 | - |
0.4204 | 1350 | 0.0077 | - |
0.4360 | 1400 | 0.0069 | - |
0.4516 | 1450 | 0.0059 | - |
0.4671 | 1500 | 0.0051 | - |
0.4827 | 1550 | 0.0047 | - |
0.4983 | 1600 | 0.0046 | - |
0.5139 | 1650 | 0.0045 | - |
0.5294 | 1700 | 0.0036 | - |
0.5450 | 1750 | 0.0032 | - |
0.5606 | 1800 | 0.0027 | - |
0.5761 | 1850 | 0.0024 | - |
0.5917 | 1900 | 0.0019 | - |
0.6073 | 1950 | 0.0015 | - |
0.6229 | 2000 | 0.0017 | - |
0.6384 | 2050 | 0.0015 | - |
0.6540 | 2100 | 0.0016 | - |
0.6696 | 2150 | 0.0013 | - |
0.6851 | 2200 | 0.0017 | - |
0.7007 | 2250 | 0.0016 | - |
0.7163 | 2300 | 0.0018 | - |
0.7319 | 2350 | 0.0016 | - |
0.7474 | 2400 | 0.0013 | - |
0.7630 | 2450 | 0.0011 | - |
0.7786 | 2500 | 0.0015 | - |
0.7941 | 2550 | 0.0013 | - |
0.8097 | 2600 | 0.0009 | - |
0.8253 | 2650 | 0.0015 | - |
0.8409 | 2700 | 0.0013 | - |
0.8564 | 2750 | 0.001 | - |
0.8720 | 2800 | 0.001 | - |
0.8876 | 2850 | 0.0012 | - |
0.9031 | 2900 | 0.0013 | - |
0.9187 | 2950 | 0.0011 | - |
0.9343 | 3000 | 0.001 | - |
0.9499 | 3050 | 0.001 | - |
0.9654 | 3100 | 0.0005 | - |
0.9810 | 3150 | 0.0007 | - |
0.9966 | 3200 | 0.0006 | - |
1.0121 | 3250 | 0.0007 | - |
1.0277 | 3300 | 0.0004 | - |
1.0433 | 3350 | 0.0004 | - |
1.0589 | 3400 | 0.0006 | - |
1.0744 | 3450 | 0.0003 | - |
1.0900 | 3500 | 0.0003 | - |
1.1056 | 3550 | 0.0002 | - |
1.1211 | 3600 | 0.0002 | - |
1.1367 | 3650 | 0.0002 | - |
1.1523 | 3700 | 0.0001 | - |
1.1679 | 3750 | 0.0001 | - |
1.1834 | 3800 | 0.0001 | - |
1.1990 | 3850 | 0.0001 | - |
1.2146 | 3900 | 0.0001 | - |
1.2301 | 3950 | 0.0001 | - |
1.2457 | 4000 | 0.0001 | - |
1.2613 | 4050 | 0.0001 | - |
1.2769 | 4100 | 0.0001 | - |
1.2924 | 4150 | 0.0001 | - |
1.3080 | 4200 | 0.0001 | - |
1.3236 | 4250 | 0.0001 | - |
1.3391 | 4300 | 0.0001 | - |
1.3547 | 4350 | 0.0001 | - |
1.3703 | 4400 | 0.0001 | - |
1.3859 | 4450 | 0.0001 | - |
1.4014 | 4500 | 0.0001 | - |
1.4170 | 4550 | 0.0001 | - |
1.4326 | 4600 | 0.0001 | - |
1.4481 | 4650 | 0.0001 | - |
1.4637 | 4700 | 0.0001 | - |
1.4793 | 4750 | 0.0001 | - |
1.4949 | 4800 | 0.0001 | - |
1.5104 | 4850 | 0.0001 | - |
1.5260 | 4900 | 0.0 | - |
1.5416 | 4950 | 0.0 | - |
1.5571 | 5000 | 0.0 | - |
1.5727 | 5050 | 0.0 | - |
1.5883 | 5100 | 0.0 | - |
1.6039 | 5150 | 0.0 | - |
1.6194 | 5200 | 0.0 | - |
1.6350 | 5250 | 0.0 | - |
1.6506 | 5300 | 0.0001 | - |
1.6661 | 5350 | 0.0003 | - |
1.6817 | 5400 | 0.0003 | - |
1.6973 | 5450 | 0.0005 | - |
1.7129 | 5500 | 0.0001 | - |
1.7284 | 5550 | 0.0 | - |
1.7440 | 5600 | 0.0 | - |
1.7596 | 5650 | 0.0 | - |
1.7751 | 5700 | 0.0 | - |
1.7907 | 5750 | 0.0 | - |
1.8063 | 5800 | 0.0 | - |
1.8219 | 5850 | 0.0 | - |
1.8374 | 5900 | 0.0 | - |
1.8530 | 5950 | 0.0 | - |
1.8686 | 6000 | 0.0 | - |
1.8841 | 6050 | 0.0 | - |
1.8997 | 6100 | 0.0 | - |
1.9153 | 6150 | 0.0 | - |
1.9309 | 6200 | 0.0 | - |
1.9464 | 6250 | 0.0 | - |
1.9620 | 6300 | 0.0 | - |
1.9776 | 6350 | 0.0 | - |
1.9931 | 6400 | 0.0 | - |
2.0087 | 6450 | 0.0 | - |
2.0243 | 6500 | 0.0 | - |
2.0399 | 6550 | 0.0 | - |
2.0554 | 6600 | 0.0 | - |
2.0710 | 6650 | 0.0 | - |
2.0866 | 6700 | 0.0 | - |
2.1021 | 6750 | 0.0 | - |
2.1177 | 6800 | 0.0 | - |
2.1333 | 6850 | 0.0 | - |
2.1489 | 6900 | 0.0 | - |
2.1644 | 6950 | 0.0 | - |
2.1800 | 7000 | 0.0 | - |
2.1956 | 7050 | 0.0 | - |
2.2111 | 7100 | 0.0 | - |
2.2267 | 7150 | 0.0 | - |
2.2423 | 7200 | 0.0 | - |
2.2579 | 7250 | 0.0 | - |
2.2734 | 7300 | 0.0 | - |
2.2890 | 7350 | 0.0 | - |
2.3046 | 7400 | 0.0 | - |
2.3201 | 7450 | 0.0 | - |
2.3357 | 7500 | 0.0 | - |
2.3513 | 7550 | 0.0 | - |
2.3669 | 7600 | 0.0 | - |
2.3824 | 7650 | 0.0 | - |
2.3980 | 7700 | 0.0 | - |
2.4136 | 7750 | 0.0 | - |
2.4291 | 7800 | 0.0 | - |
2.4447 | 7850 | 0.0 | - |
2.4603 | 7900 | 0.0 | - |
2.4759 | 7950 | 0.0 | - |
2.4914 | 8000 | 0.0 | - |
2.5070 | 8050 | 0.0 | - |
2.5226 | 8100 | 0.0 | - |
2.5382 | 8150 | 0.0 | - |
2.5537 | 8200 | 0.0 | - |
2.5693 | 8250 | 0.0 | - |
2.5849 | 8300 | 0.0 | - |
2.6004 | 8350 | 0.0 | - |
2.6160 | 8400 | 0.0 | - |
2.6316 | 8450 | 0.0 | - |
2.6472 | 8500 | 0.0 | - |
2.6627 | 8550 | 0.0 | - |
2.6783 | 8600 | 0.0 | - |
2.6939 | 8650 | 0.0 | - |
2.7094 | 8700 | 0.0 | - |
2.7250 | 8750 | 0.0 | - |
2.7406 | 8800 | 0.0 | - |
2.7562 | 8850 | 0.0 | - |
2.7717 | 8900 | 0.0 | - |
2.7873 | 8950 | 0.0 | - |
2.8029 | 9000 | 0.0 | - |
2.8184 | 9050 | 0.0 | - |
2.8340 | 9100 | 0.0 | - |
2.8496 | 9150 | 0.0 | - |
2.8652 | 9200 | 0.0 | - |
2.8807 | 9250 | 0.0 | - |
2.8963 | 9300 | 0.0 | - |
2.9119 | 9350 | 0.0 | - |
2.9274 | 9400 | 0.0 | - |
2.9430 | 9450 | 0.0 | - |
2.9586 | 9500 | 0.0 | - |
2.9742 | 9550 | 0.0 | - |
2.9897 | 9600 | 0.0 | - |
3.0053 | 9650 | 0.0 | - |
3.0209 | 9700 | 0.0 | - |
3.0364 | 9750 | 0.0 | - |
3.0520 | 9800 | 0.0 | - |
3.0676 | 9850 | 0.0 | - |
3.0832 | 9900 | 0.0 | - |
3.0987 | 9950 | 0.0 | - |
3.1143 | 10000 | 0.0 | - |
3.1299 | 10050 | 0.0 | - |
3.1454 | 10100 | 0.0 | - |
3.1610 | 10150 | 0.0 | - |
3.1766 | 10200 | 0.0 | - |
3.1922 | 10250 | 0.0 | - |
3.2077 | 10300 | 0.0 | - |
3.2233 | 10350 | 0.0 | - |
3.2389 | 10400 | 0.0 | - |
3.2544 | 10450 | 0.0 | - |
3.2700 | 10500 | 0.0 | - |
3.2856 | 10550 | 0.0 | - |
3.3012 | 10600 | 0.0 | - |
3.3167 | 10650 | 0.0 | - |
3.3323 | 10700 | 0.0 | - |
3.3479 | 10750 | 0.0 | - |
3.3634 | 10800 | 0.0 | - |
3.3790 | 10850 | 0.0 | - |
3.3946 | 10900 | 0.0 | - |
3.4102 | 10950 | 0.0 | - |
3.4257 | 11000 | 0.0 | - |
3.4413 | 11050 | 0.0027 | - |
3.4569 | 11100 | 0.0016 | - |
3.4724 | 11150 | 0.0022 | - |
3.4880 | 11200 | 0.0007 | - |
3.5036 | 11250 | 0.0007 | - |
3.5192 | 11300 | 0.0005 | - |
3.5347 | 11350 | 0.0006 | - |
3.5503 | 11400 | 0.0004 | - |
3.5659 | 11450 | 0.0001 | - |
3.5814 | 11500 | 0.0003 | - |
3.5970 | 11550 | 0.0006 | - |
3.6126 | 11600 | 0.0002 | - |
3.6282 | 11650 | 0.0002 | - |
3.6437 | 11700 | 0.0003 | - |
3.6593 | 11750 | 0.0004 | - |
3.6749 | 11800 | 0.0002 | - |
3.6904 | 11850 | 0.0002 | - |
3.7060 | 11900 | 0.0005 | - |
3.7216 | 11950 | 0.0003 | - |
3.7372 | 12000 | 0.0003 | - |
3.7527 | 12050 | 0.0004 | - |
3.7683 | 12100 | 0.0003 | - |
3.7839 | 12150 | 0.0003 | - |
3.7994 | 12200 | 0.0005 | - |
3.8150 | 12250 | 0.0005 | - |
3.8306 | 12300 | 0.0003 | - |
3.8462 | 12350 | 0.0004 | - |
3.8617 | 12400 | 0.0002 | - |
3.8773 | 12450 | 0.0004 | - |
3.8929 | 12500 | 0.0004 | - |
3.9084 | 12550 | 0.0003 | - |
3.9240 | 12600 | 0.0002 | - |
3.9396 | 12650 | 0.0002 | - |
3.9552 | 12700 | 0.0001 | - |
3.9707 | 12750 | 0.0003 | - |
3.9863 | 12800 | 0.0004 | - |
4.0019 | 12850 | 0.0003 | - |
4.0174 | 12900 | 0.0002 | - |
4.0330 | 12950 | 0.0001 | - |
4.0486 | 13000 | 0.0002 | - |
4.0642 | 13050 | 0.0003 | - |
4.0797 | 13100 | 0.0004 | - |
4.0953 | 13150 | 0.0004 | - |
4.1109 | 13200 | 0.0004 | - |
4.1264 | 13250 | 0.0004 | - |
4.1420 | 13300 | 0.0002 | - |
4.1576 | 13350 | 0.0002 | - |
4.1732 | 13400 | 0.0003 | - |
4.1887 | 13450 | 0.0004 | - |
4.2043 | 13500 | 0.0 | - |
4.2199 | 13550 | 0.0 | - |
4.2354 | 13600 | 0.0001 | - |
4.2510 | 13650 | 0.0004 | - |
4.2666 | 13700 | 0.0005 | - |
4.2822 | 13750 | 0.0004 | - |
4.2977 | 13800 | 0.0003 | - |
4.3133 | 13850 | 0.0001 | - |
4.3289 | 13900 | 0.0001 | - |
4.3444 | 13950 | 0.0001 | - |
4.3600 | 14000 | 0.0 | - |
4.3756 | 14050 | 0.0 | - |
4.3912 | 14100 | 0.0 | - |
4.4067 | 14150 | 0.0 | - |
4.4223 | 14200 | 0.0 | - |
4.4379 | 14250 | 0.0 | - |
4.4534 | 14300 | 0.0 | - |
4.4690 | 14350 | 0.0 | - |
4.4846 | 14400 | 0.0 | - |
4.5002 | 14450 | 0.0 | - |
4.5157 | 14500 | 0.0 | - |
4.5313 | 14550 | 0.0 | - |
4.5469 | 14600 | 0.0 | - |
4.5624 | 14650 | 0.0 | - |
4.5780 | 14700 | 0.0 | - |
4.5936 | 14750 | 0.0 | - |
4.6092 | 14800 | 0.0 | - |
4.6247 | 14850 | 0.0 | - |
4.6403 | 14900 | 0.0001 | - |
4.6559 | 14950 | 0.0 | - |
4.6714 | 15000 | 0.0 | - |
4.6870 | 15050 | 0.0 | - |
4.7026 | 15100 | 0.0 | - |
4.7182 | 15150 | 0.0 | - |
4.7337 | 15200 | 0.0 | - |
4.7493 | 15250 | 0.0 | - |
4.7649 | 15300 | 0.0 | - |
4.7804 | 15350 | 0.0 | - |
4.7960 | 15400 | 0.0 | - |
4.8116 | 15450 | 0.0 | - |
4.8272 | 15500 | 0.0 | - |
4.8427 | 15550 | 0.0 | - |
4.8583 | 15600 | 0.0 | - |
4.8739 | 15650 | 0.0 | - |
4.8894 | 15700 | 0.0 | - |
4.9050 | 15750 | 0.0 | - |
4.9206 | 15800 | 0.0 | - |
4.9362 | 15850 | 0.0 | - |
4.9517 | 15900 | 0.0 | - |
4.9673 | 15950 | 0.0 | - |
4.9829 | 16000 | 0.0 | - |
4.9984 | 16050 | 0.0 | - |
5.0140 | 16100 | 0.0002 | - |
5.0296 | 16150 | 0.0004 | - |
5.0452 | 16200 | 0.0001 | - |
5.0607 | 16250 | 0.0 | - |
5.0763 | 16300 | 0.0 | - |
5.0919 | 16350 | 0.0 | - |
5.1074 | 16400 | 0.0 | - |
5.1230 | 16450 | 0.0 | - |
5.1386 | 16500 | 0.0 | - |
5.1542 | 16550 | 0.0 | - |
5.1697 | 16600 | 0.0 | - |
5.1853 | 16650 | 0.0 | - |
5.2009 | 16700 | 0.0 | - |
5.2164 | 16750 | 0.0 | - |
5.2320 | 16800 | 0.0 | - |
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5.2632 | 16900 | 0.0 | - |
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5.6992 | 18300 | 0.0 | - |
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5.7926 | 18600 | 0.0 | - |
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5.8549 | 18800 | 0.0 | - |
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5.9639 | 19150 | 0.0 | - |
5.9794 | 19200 | 0.0 | - |
5.9950 | 19250 | 0.0 | - |
6.0106 | 19300 | 0.0 | - |
6.0262 | 19350 | 0.0 | - |
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6.0573 | 19450 | 0.0 | - |
6.0729 | 19500 | 0.0 | - |
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6.1040 | 19600 | 0.0 | - |
6.1196 | 19650 | 0.0 | - |
6.1352 | 19700 | 0.0 | - |
6.1507 | 19750 | 0.0 | - |
6.1663 | 19800 | 0.0 | - |
6.1819 | 19850 | 0.0 | - |
6.1974 | 19900 | 0.0 | - |
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6.2909 | 20200 | 0.0 | - |
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6.3999 | 20550 | 0.0 | - |
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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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