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: 15 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 |
---|---|
0 |
|
8 |
|
4 |
|
13 |
|
10 |
|
14 |
|
1 |
|
3 |
|
11 |
|
12 |
|
7 |
|
9 |
|
2 |
|
5 |
|
6 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.9065 |
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_top4_test")
# Run inference
preds = model("러쉬 [러쉬]오늘을 사랑해(섹스 밤+피치 배쓰 밤) (#M)11st>바디케어>바디워시>가루형입욕제 11st > 뷰티 > 바디케어 > 바디워시 > 가루형입욕제")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 12 | 21.7607 | 51 |
Label | Training Sample Count |
---|---|
0 | 50 |
1 | 50 |
2 | 50 |
3 | 50 |
4 | 50 |
5 | 50 |
6 | 50 |
7 | 48 |
8 | 50 |
9 | 50 |
10 | 50 |
11 | 50 |
12 | 50 |
13 | 50 |
14 | 50 |
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 |
---|---|---|---|
0.0009 | 1 | 0.4485 | - |
0.0428 | 50 | 0.4501 | - |
0.0855 | 100 | 0.4537 | - |
0.1283 | 150 | 0.4456 | - |
0.1711 | 200 | 0.438 | - |
0.2139 | 250 | 0.4018 | - |
0.2566 | 300 | 0.3949 | - |
0.2994 | 350 | 0.3759 | - |
0.3422 | 400 | 0.3392 | - |
0.3849 | 450 | 0.3183 | - |
0.4277 | 500 | 0.2821 | - |
0.4705 | 550 | 0.2688 | - |
0.5133 | 600 | 0.2653 | - |
0.5560 | 650 | 0.2568 | - |
0.5988 | 700 | 0.2565 | - |
0.6416 | 750 | 0.2598 | - |
0.6843 | 800 | 0.2502 | - |
0.7271 | 850 | 0.2427 | - |
0.7699 | 900 | 0.2356 | - |
0.8127 | 950 | 0.2285 | - |
0.8554 | 1000 | 0.2192 | - |
0.8982 | 1050 | 0.2219 | - |
0.9410 | 1100 | 0.2181 | - |
0.9837 | 1150 | 0.2123 | - |
1.0265 | 1200 | 0.2079 | - |
1.0693 | 1250 | 0.2067 | - |
1.1121 | 1300 | 0.1987 | - |
1.1548 | 1350 | 0.1957 | - |
1.1976 | 1400 | 0.1908 | - |
1.2404 | 1450 | 0.1883 | - |
1.2831 | 1500 | 0.1824 | - |
1.3259 | 1550 | 0.1821 | - |
1.3687 | 1600 | 0.1821 | - |
1.4115 | 1650 | 0.1686 | - |
1.4542 | 1700 | 0.1693 | - |
1.4970 | 1750 | 0.1611 | - |
1.5398 | 1800 | 0.1581 | - |
1.5825 | 1850 | 0.1416 | - |
1.6253 | 1900 | 0.1412 | - |
1.6681 | 1950 | 0.1257 | - |
1.7109 | 2000 | 0.13 | - |
1.7536 | 2050 | 0.1205 | - |
1.7964 | 2100 | 0.1182 | - |
1.8392 | 2150 | 0.111 | - |
1.8820 | 2200 | 0.1141 | - |
1.9247 | 2250 | 0.1022 | - |
1.9675 | 2300 | 0.0919 | - |
2.0103 | 2350 | 0.0834 | - |
2.0530 | 2400 | 0.0776 | - |
2.0958 | 2450 | 0.0701 | - |
2.1386 | 2500 | 0.0644 | - |
2.1814 | 2550 | 0.0552 | - |
2.2241 | 2600 | 0.0486 | - |
2.2669 | 2650 | 0.0433 | - |
2.3097 | 2700 | 0.0323 | - |
2.3524 | 2750 | 0.0279 | - |
2.3952 | 2800 | 0.0268 | - |
2.4380 | 2850 | 0.0247 | - |
2.4808 | 2900 | 0.0154 | - |
2.5235 | 2950 | 0.0126 | - |
2.5663 | 3000 | 0.0097 | - |
2.6091 | 3050 | 0.0099 | - |
2.6518 | 3100 | 0.0082 | - |
2.6946 | 3150 | 0.0078 | - |
2.7374 | 3200 | 0.0058 | - |
2.7802 | 3250 | 0.0048 | - |
2.8229 | 3300 | 0.0039 | - |
2.8657 | 3350 | 0.0032 | - |
2.9085 | 3400 | 0.0024 | - |
2.9512 | 3450 | 0.0021 | - |
2.9940 | 3500 | 0.0018 | - |
3.0368 | 3550 | 0.0014 | - |
3.0796 | 3600 | 0.001 | - |
3.1223 | 3650 | 0.0006 | - |
3.1651 | 3700 | 0.0007 | - |
3.2079 | 3750 | 0.0007 | - |
3.2506 | 3800 | 0.0006 | - |
3.2934 | 3850 | 0.0009 | - |
3.3362 | 3900 | 0.001 | - |
3.3790 | 3950 | 0.001 | - |
3.4217 | 4000 | 0.0005 | - |
3.4645 | 4050 | 0.0004 | - |
3.5073 | 4100 | 0.0008 | - |
3.5500 | 4150 | 0.0004 | - |
3.5928 | 4200 | 0.0022 | - |
3.6356 | 4250 | 0.0021 | - |
3.6784 | 4300 | 0.0051 | - |
3.7211 | 4350 | 0.0037 | - |
3.7639 | 4400 | 0.0026 | - |
3.8067 | 4450 | 0.0021 | - |
3.8494 | 4500 | 0.0009 | - |
3.8922 | 4550 | 0.0004 | - |
3.9350 | 4600 | 0.0002 | - |
3.9778 | 4650 | 0.0002 | - |
4.0205 | 4700 | 0.0001 | - |
4.0633 | 4750 | 0.0001 | - |
4.1061 | 4800 | 0.0001 | - |
4.1488 | 4850 | 0.0001 | - |
4.1916 | 4900 | 0.0001 | - |
4.2344 | 4950 | 0.0001 | - |
4.2772 | 5000 | 0.0001 | - |
4.3199 | 5050 | 0.0001 | - |
4.3627 | 5100 | 0.0001 | - |
4.4055 | 5150 | 0.0001 | - |
4.4482 | 5200 | 0.0001 | - |
4.4910 | 5250 | 0.0001 | - |
4.5338 | 5300 | 0.0001 | - |
4.5766 | 5350 | 0.0001 | - |
4.6193 | 5400 | 0.0001 | - |
4.6621 | 5450 | 0.0001 | - |
4.7049 | 5500 | 0.0001 | - |
4.7476 | 5550 | 0.0001 | - |
4.7904 | 5600 | 0.0001 | - |
4.8332 | 5650 | 0.0001 | - |
4.8760 | 5700 | 0.0001 | - |
4.9187 | 5750 | 0.0001 | - |
4.9615 | 5800 | 0.0002 | - |
5.0043 | 5850 | 0.0001 | - |
5.0470 | 5900 | 0.0001 | - |
5.0898 | 5950 | 0.0001 | - |
5.1326 | 6000 | 0.0001 | - |
5.1754 | 6050 | 0.0001 | - |
5.2181 | 6100 | 0.0001 | - |
5.2609 | 6150 | 0.0 | - |
5.3037 | 6200 | 0.0 | - |
5.3464 | 6250 | 0.0001 | - |
5.3892 | 6300 | 0.0003 | - |
5.4320 | 6350 | 0.0008 | - |
5.4748 | 6400 | 0.0016 | - |
5.5175 | 6450 | 0.0069 | - |
5.5603 | 6500 | 0.0152 | - |
5.6031 | 6550 | 0.0175 | - |
5.6459 | 6600 | 0.0055 | - |
5.6886 | 6650 | 0.0041 | - |
5.7314 | 6700 | 0.0024 | - |
5.7742 | 6750 | 0.0025 | - |
5.8169 | 6800 | 0.0015 | - |
5.8597 | 6850 | 0.0016 | - |
5.9025 | 6900 | 0.0018 | - |
5.9453 | 6950 | 0.0007 | - |
5.9880 | 7000 | 0.0012 | - |
6.0308 | 7050 | 0.001 | - |
6.0736 | 7100 | 0.001 | - |
6.1163 | 7150 | 0.0003 | - |
6.1591 | 7200 | 0.0001 | - |
6.2019 | 7250 | 0.0003 | - |
6.2447 | 7300 | 0.0001 | - |
6.2874 | 7350 | 0.0001 | - |
6.3302 | 7400 | 0.0001 | - |
6.3730 | 7450 | 0.0045 | - |
6.4157 | 7500 | 0.0018 | - |
6.4585 | 7550 | 0.0005 | - |
6.5013 | 7600 | 0.0012 | - |
6.5441 | 7650 | 0.0005 | - |
6.5868 | 7700 | 0.0002 | - |
6.6296 | 7750 | 0.0003 | - |
6.6724 | 7800 | 0.0002 | - |
6.7151 | 7850 | 0.0 | - |
6.7579 | 7900 | 0.0 | - |
6.8007 | 7950 | 0.0 | - |
6.8435 | 8000 | 0.0 | - |
6.8862 | 8050 | 0.0 | - |
6.9290 | 8100 | 0.0 | - |
6.9718 | 8150 | 0.0 | - |
7.0145 | 8200 | 0.0 | - |
7.0573 | 8250 | 0.0 | - |
7.1001 | 8300 | 0.0 | - |
7.1429 | 8350 | 0.0 | - |
7.1856 | 8400 | 0.0 | - |
7.2284 | 8450 | 0.0 | - |
7.2712 | 8500 | 0.0 | - |
7.3139 | 8550 | 0.0001 | - |
7.3567 | 8600 | 0.0 | - |
7.3995 | 8650 | 0.0 | - |
7.4423 | 8700 | 0.0 | - |
7.4850 | 8750 | 0.0 | - |
7.5278 | 8800 | 0.0 | - |
7.5706 | 8850 | 0.0 | - |
7.6133 | 8900 | 0.0 | - |
7.6561 | 8950 | 0.0 | - |
7.6989 | 9000 | 0.0 | - |
7.7417 | 9050 | 0.0 | - |
7.7844 | 9100 | 0.0 | - |
7.8272 | 9150 | 0.0 | - |
7.8700 | 9200 | 0.0 | - |
7.9127 | 9250 | 0.0 | - |
7.9555 | 9300 | 0.0 | - |
7.9983 | 9350 | 0.0 | - |
8.0411 | 9400 | 0.0 | - |
8.0838 | 9450 | 0.0013 | - |
8.1266 | 9500 | 0.0024 | - |
8.1694 | 9550 | 0.0043 | - |
8.2121 | 9600 | 0.0038 | - |
8.2549 | 9650 | 0.0029 | - |
8.2977 | 9700 | 0.0003 | - |
8.3405 | 9750 | 0.0004 | - |
8.3832 | 9800 | 0.0 | - |
8.4260 | 9850 | 0.0021 | - |
8.4688 | 9900 | 0.0013 | - |
8.5115 | 9950 | 0.0012 | - |
8.5543 | 10000 | 0.0011 | - |
8.5971 | 10050 | 0.0006 | - |
8.6399 | 10100 | 0.0003 | - |
8.6826 | 10150 | 0.0 | - |
8.7254 | 10200 | 0.0 | - |
8.7682 | 10250 | 0.0 | - |
8.8109 | 10300 | 0.0 | - |
8.8537 | 10350 | 0.0 | - |
8.8965 | 10400 | 0.0 | - |
8.9393 | 10450 | 0.0 | - |
8.9820 | 10500 | 0.0 | - |
9.0248 | 10550 | 0.0 | - |
9.0676 | 10600 | 0.0 | - |
9.1104 | 10650 | 0.0 | - |
9.1531 | 10700 | 0.0 | - |
9.1959 | 10750 | 0.0 | - |
9.2387 | 10800 | 0.0 | - |
9.2814 | 10850 | 0.0 | - |
9.3242 | 10900 | 0.0 | - |
9.3670 | 10950 | 0.0 | - |
9.4098 | 11000 | 0.0 | - |
9.4525 | 11050 | 0.0 | - |
9.4953 | 11100 | 0.0 | - |
9.5381 | 11150 | 0.0 | - |
9.5808 | 11200 | 0.0 | - |
9.6236 | 11250 | 0.0 | - |
9.6664 | 11300 | 0.0 | - |
9.7092 | 11350 | 0.0 | - |
9.7519 | 11400 | 0.0 | - |
9.7947 | 11450 | 0.0 | - |
9.8375 | 11500 | 0.0 | - |
9.8802 | 11550 | 0.0 | - |
9.9230 | 11600 | 0.0 | - |
9.9658 | 11650 | 0.0 | - |
10.0086 | 11700 | 0.0 | - |
10.0513 | 11750 | 0.0001 | - |
10.0941 | 11800 | 0.0011 | - |
10.1369 | 11850 | 0.0027 | - |
10.1796 | 11900 | 0.0064 | - |
10.2224 | 11950 | 0.0015 | - |
10.2652 | 12000 | 0.0004 | - |
10.3080 | 12050 | 0.0 | - |
10.3507 | 12100 | 0.0005 | - |
10.3935 | 12150 | 0.0028 | - |
10.4363 | 12200 | 0.0012 | - |
10.4790 | 12250 | 0.002 | - |
10.5218 | 12300 | 0.0015 | - |
10.5646 | 12350 | 0.0005 | - |
10.6074 | 12400 | 0.0002 | - |
10.6501 | 12450 | 0.0001 | - |
10.6929 | 12500 | 0.0 | - |
10.7357 | 12550 | 0.0002 | - |
10.7784 | 12600 | 0.0 | - |
10.8212 | 12650 | 0.0 | - |
10.8640 | 12700 | 0.0 | - |
10.9068 | 12750 | 0.0 | - |
10.9495 | 12800 | 0.0 | - |
10.9923 | 12850 | 0.0 | - |
11.0351 | 12900 | 0.0 | - |
11.0778 | 12950 | 0.0002 | - |
11.1206 | 13000 | 0.0 | - |
11.1634 | 13050 | 0.0 | - |
11.2062 | 13100 | 0.0 | - |
11.2489 | 13150 | 0.0 | - |
11.2917 | 13200 | 0.0 | - |
11.3345 | 13250 | 0.0 | - |
11.3772 | 13300 | 0.0 | - |
11.4200 | 13350 | 0.0 | - |
11.4628 | 13400 | 0.0 | - |
11.5056 | 13450 | 0.0 | - |
11.5483 | 13500 | 0.0 | - |
11.5911 | 13550 | 0.0 | - |
11.6339 | 13600 | 0.0 | - |
11.6766 | 13650 | 0.0 | - |
11.7194 | 13700 | 0.0 | - |
11.7622 | 13750 | 0.0 | - |
11.8050 | 13800 | 0.0 | - |
11.8477 | 13850 | 0.0 | - |
11.8905 | 13900 | 0.0 | - |
11.9333 | 13950 | 0.0 | - |
11.9760 | 14000 | 0.0 | - |
12.0188 | 14050 | 0.0 | - |
12.0616 | 14100 | 0.0 | - |
12.1044 | 14150 | 0.0 | - |
12.1471 | 14200 | 0.0 | - |
12.1899 | 14250 | 0.0 | - |
12.2327 | 14300 | 0.0 | - |
12.2754 | 14350 | 0.0 | - |
12.3182 | 14400 | 0.0 | - |
12.3610 | 14450 | 0.0 | - |
12.4038 | 14500 | 0.0 | - |
12.4465 | 14550 | 0.0 | - |
12.4893 | 14600 | 0.0 | - |
12.5321 | 14650 | 0.0 | - |
12.5749 | 14700 | 0.0 | - |
12.6176 | 14750 | 0.0 | - |
12.6604 | 14800 | 0.0 | - |
12.7032 | 14850 | 0.0 | - |
12.7459 | 14900 | 0.0001 | - |
12.7887 | 14950 | 0.0006 | - |
12.8315 | 15000 | 0.0 | - |
12.8743 | 15050 | 0.0 | - |
12.9170 | 15100 | 0.0 | - |
12.9598 | 15150 | 0.0 | - |
13.0026 | 15200 | 0.0 | - |
13.0453 | 15250 | 0.0 | - |
13.0881 | 15300 | 0.0 | - |
13.1309 | 15350 | 0.0002 | - |
13.1737 | 15400 | 0.0005 | - |
13.2164 | 15450 | 0.0017 | - |
13.2592 | 15500 | 0.0015 | - |
13.3020 | 15550 | 0.0012 | - |
13.3447 | 15600 | 0.0017 | - |
13.3875 | 15650 | 0.0006 | - |
13.4303 | 15700 | 0.0002 | - |
13.4731 | 15750 | 0.0007 | - |
13.5158 | 15800 | 0.0001 | - |
13.5586 | 15850 | 0.0001 | - |
13.6014 | 15900 | 0.0001 | - |
13.6441 | 15950 | 0.0 | - |
13.6869 | 16000 | 0.0 | - |
13.7297 | 16050 | 0.0 | - |
13.7725 | 16100 | 0.0 | - |
13.8152 | 16150 | 0.0 | - |
13.8580 | 16200 | 0.0 | - |
13.9008 | 16250 | 0.0 | - |
13.9435 | 16300 | 0.0 | - |
13.9863 | 16350 | 0.0 | - |
14.0291 | 16400 | 0.0 | - |
14.0719 | 16450 | 0.0 | - |
14.1146 | 16500 | 0.0 | - |
14.1574 | 16550 | 0.0 | - |
14.2002 | 16600 | 0.0 | - |
14.2429 | 16650 | 0.0 | - |
14.2857 | 16700 | 0.0 | - |
14.3285 | 16750 | 0.0 | - |
14.3713 | 16800 | 0.0 | - |
14.4140 | 16850 | 0.0 | - |
14.4568 | 16900 | 0.0 | - |
14.4996 | 16950 | 0.0 | - |
14.5423 | 17000 | 0.0 | - |
14.5851 | 17050 | 0.0 | - |
14.6279 | 17100 | 0.0002 | - |
14.6707 | 17150 | 0.0 | - |
14.7134 | 17200 | 0.0 | - |
14.7562 | 17250 | 0.0 | - |
14.7990 | 17300 | 0.0 | - |
14.8417 | 17350 | 0.0 | - |
14.8845 | 17400 | 0.0 | - |
14.9273 | 17450 | 0.0 | - |
14.9701 | 17500 | 0.0 | - |
15.0128 | 17550 | 0.0 | - |
15.0556 | 17600 | 0.0 | - |
15.0984 | 17650 | 0.0 | - |
15.1411 | 17700 | 0.0 | - |
15.1839 | 17750 | 0.0 | - |
15.2267 | 17800 | 0.0 | - |
15.2695 | 17850 | 0.0 | - |
15.3122 | 17900 | 0.0 | - |
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15.3978 | 18000 | 0.0 | - |
15.4405 | 18050 | 0.0 | - |
15.4833 | 18100 | 0.0 | - |
15.5261 | 18150 | 0.0 | - |
15.5689 | 18200 | 0.0 | - |
15.6116 | 18250 | 0.0002 | - |
15.6544 | 18300 | 0.0 | - |
15.6972 | 18350 | 0.0 | - |
15.7399 | 18400 | 0.0 | - |
15.7827 | 18450 | 0.0 | - |
15.8255 | 18500 | 0.0 | - |
15.8683 | 18550 | 0.0 | - |
15.9110 | 18600 | 0.0 | - |
15.9538 | 18650 | 0.0 | - |
15.9966 | 18700 | 0.0 | - |
16.0393 | 18750 | 0.0 | - |
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16.2532 | 19000 | 0.0 | - |
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16.4243 | 19200 | 0.0 | - |
16.4671 | 19250 | 0.0 | - |
16.5098 | 19300 | 0.0 | - |
16.5526 | 19350 | 0.0 | - |
16.5954 | 19400 | 0.0 | - |
16.6382 | 19450 | 0.0002 | - |
16.6809 | 19500 | 0.0 | - |
16.7237 | 19550 | 0.0 | - |
16.7665 | 19600 | 0.0 | - |
16.8092 | 19650 | 0.0 | - |
16.8520 | 19700 | 0.0 | - |
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17.0231 | 19900 | 0.0 | - |
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17.4080 | 20350 | 0.0 | - |
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17.5791 | 20550 | 0.0 | - |
17.6219 | 20600 | 0.0 | - |
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18.9050 | 22100 | 0.0002 | - |
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19.0334 | 22250 | 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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