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: 40 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 |
|
20 |
|
21 |
|
32 |
|
5 |
|
12 |
|
36 |
|
8 |
|
15 |
|
31 |
|
37 |
|
18 |
|
19 |
|
38 |
|
10 |
|
39 |
|
28 |
|
1 |
|
0 |
|
35 |
|
14 |
|
13 |
|
33 |
|
9 |
|
22 |
|
29 |
|
24 |
|
11 |
|
27 |
|
30 |
|
2 |
|
23 |
|
17 |
|
16 |
|
7 |
|
4 |
|
25 |
|
34 |
|
6 |
|
26 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.9742 |
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_top_fd")
# Run inference
preds = model("아하이북식왕만두1.4kg 곤드레전병 (#M)홈>식품>냉동/간편조리식품>만두 Naverstore > 식품 > 냉동/간편조리식품 > 만두")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 9 | 20.8971 | 87 |
Label | Training Sample Count |
---|---|
0 | 50 |
1 | 50 |
2 | 14 |
3 | 50 |
4 | 50 |
5 | 50 |
6 | 4 |
7 | 50 |
8 | 50 |
9 | 50 |
10 | 50 |
11 | 50 |
12 | 50 |
13 | 50 |
14 | 50 |
15 | 50 |
16 | 50 |
17 | 50 |
18 | 50 |
19 | 50 |
20 | 50 |
21 | 50 |
22 | 50 |
23 | 50 |
24 | 50 |
25 | 14 |
26 | 1 |
27 | 50 |
28 | 50 |
29 | 50 |
30 | 50 |
31 | 50 |
32 | 50 |
33 | 50 |
34 | 6 |
35 | 50 |
36 | 50 |
37 | 50 |
38 | 50 |
39 | 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 |
---|---|---|---|
0.0004 | 1 | 0.4359 | - |
0.0179 | 50 | 0.4342 | - |
0.0358 | 100 | 0.4186 | - |
0.0536 | 150 | 0.4027 | - |
0.0715 | 200 | 0.3974 | - |
0.0894 | 250 | 0.3648 | - |
0.1073 | 300 | 0.322 | - |
0.1252 | 350 | 0.3116 | - |
0.1431 | 400 | 0.2926 | - |
0.1609 | 450 | 0.269 | - |
0.1788 | 500 | 0.2341 | - |
0.1967 | 550 | 0.1681 | - |
0.2146 | 600 | 0.129 | - |
0.2325 | 650 | 0.1035 | - |
0.2504 | 700 | 0.0867 | - |
0.2682 | 750 | 0.0765 | - |
0.2861 | 800 | 0.063 | - |
0.3040 | 850 | 0.0588 | - |
0.3219 | 900 | 0.0549 | - |
0.3398 | 950 | 0.0516 | - |
0.3577 | 1000 | 0.0454 | - |
0.3755 | 1050 | 0.0429 | - |
0.3934 | 1100 | 0.0389 | - |
0.4113 | 1150 | 0.0375 | - |
0.4292 | 1200 | 0.0347 | - |
0.4471 | 1250 | 0.0367 | - |
0.4649 | 1300 | 0.0308 | - |
0.4828 | 1350 | 0.0311 | - |
0.5007 | 1400 | 0.0299 | - |
0.5186 | 1450 | 0.0304 | - |
0.5365 | 1500 | 0.0262 | - |
0.5544 | 1550 | 0.0252 | - |
0.5722 | 1600 | 0.027 | - |
0.5901 | 1650 | 0.0232 | - |
0.6080 | 1700 | 0.0228 | - |
0.6259 | 1750 | 0.0189 | - |
0.6438 | 1800 | 0.0213 | - |
0.6617 | 1850 | 0.0214 | - |
0.6795 | 1900 | 0.02 | - |
0.6974 | 1950 | 0.0166 | - |
0.7153 | 2000 | 0.0179 | - |
0.7332 | 2050 | 0.0148 | - |
0.7511 | 2100 | 0.0153 | - |
0.7690 | 2150 | 0.0133 | - |
0.7868 | 2200 | 0.0157 | - |
0.8047 | 2250 | 0.015 | - |
0.8226 | 2300 | 0.0109 | - |
0.8405 | 2350 | 0.0125 | - |
0.8584 | 2400 | 0.011 | - |
0.8763 | 2450 | 0.0117 | - |
0.8941 | 2500 | 0.011 | - |
0.9120 | 2550 | 0.0113 | - |
0.9299 | 2600 | 0.01 | - |
0.9478 | 2650 | 0.0093 | - |
0.9657 | 2700 | 0.0091 | - |
0.9835 | 2750 | 0.0096 | - |
1.0014 | 2800 | 0.0084 | - |
1.0193 | 2850 | 0.0073 | - |
1.0372 | 2900 | 0.0075 | - |
1.0551 | 2950 | 0.0061 | - |
1.0730 | 3000 | 0.0075 | - |
1.0908 | 3050 | 0.008 | - |
1.1087 | 3100 | 0.0071 | - |
1.1266 | 3150 | 0.0068 | - |
1.1445 | 3200 | 0.0044 | - |
1.1624 | 3250 | 0.0055 | - |
1.1803 | 3300 | 0.0066 | - |
1.1981 | 3350 | 0.0042 | - |
1.2160 | 3400 | 0.0042 | - |
1.2339 | 3450 | 0.0045 | - |
1.2518 | 3500 | 0.0053 | - |
1.2697 | 3550 | 0.004 | - |
1.2876 | 3600 | 0.0039 | - |
1.3054 | 3650 | 0.0035 | - |
1.3233 | 3700 | 0.0031 | - |
1.3412 | 3750 | 0.0034 | - |
1.3591 | 3800 | 0.0031 | - |
1.3770 | 3850 | 0.0039 | - |
1.3948 | 3900 | 0.0041 | - |
1.4127 | 3950 | 0.0027 | - |
1.4306 | 4000 | 0.0039 | - |
1.4485 | 4050 | 0.0034 | - |
1.4664 | 4100 | 0.0029 | - |
1.4843 | 4150 | 0.0032 | - |
1.5021 | 4200 | 0.0028 | - |
1.5200 | 4250 | 0.003 | - |
1.5379 | 4300 | 0.0026 | - |
1.5558 | 4350 | 0.003 | - |
1.5737 | 4400 | 0.0026 | - |
1.5916 | 4450 | 0.0024 | - |
1.6094 | 4500 | 0.003 | - |
1.6273 | 4550 | 0.0019 | - |
1.6452 | 4600 | 0.0033 | - |
1.6631 | 4650 | 0.0036 | - |
1.6810 | 4700 | 0.0039 | - |
1.6989 | 4750 | 0.0044 | - |
1.7167 | 4800 | 0.0027 | - |
1.7346 | 4850 | 0.0021 | - |
1.7525 | 4900 | 0.0025 | - |
1.7704 | 4950 | 0.0028 | - |
1.7883 | 5000 | 0.0034 | - |
1.8062 | 5050 | 0.0022 | - |
1.8240 | 5100 | 0.0015 | - |
1.8419 | 5150 | 0.0019 | - |
1.8598 | 5200 | 0.0031 | - |
1.8777 | 5250 | 0.0035 | - |
1.8956 | 5300 | 0.0023 | - |
1.9134 | 5350 | 0.0033 | - |
1.9313 | 5400 | 0.0015 | - |
1.9492 | 5450 | 0.0015 | - |
1.9671 | 5500 | 0.0017 | - |
1.9850 | 5550 | 0.0013 | - |
2.0029 | 5600 | 0.0017 | - |
2.0207 | 5650 | 0.0015 | - |
2.0386 | 5700 | 0.0017 | - |
2.0565 | 5750 | 0.0018 | - |
2.0744 | 5800 | 0.0018 | - |
2.0923 | 5850 | 0.0017 | - |
2.1102 | 5900 | 0.0017 | - |
2.1280 | 5950 | 0.0029 | - |
2.1459 | 6000 | 0.0018 | - |
2.1638 | 6050 | 0.0018 | - |
2.1817 | 6100 | 0.0006 | - |
2.1996 | 6150 | 0.001 | - |
2.2175 | 6200 | 0.0013 | - |
2.2353 | 6250 | 0.0014 | - |
2.2532 | 6300 | 0.0005 | - |
2.2711 | 6350 | 0.0016 | - |
2.2890 | 6400 | 0.0014 | - |
2.3069 | 6450 | 0.0017 | - |
2.3247 | 6500 | 0.0021 | - |
2.3426 | 6550 | 0.0015 | - |
2.3605 | 6600 | 0.0007 | - |
2.3784 | 6650 | 0.001 | - |
2.3963 | 6700 | 0.0005 | - |
2.4142 | 6750 | 0.0006 | - |
2.4320 | 6800 | 0.0002 | - |
2.4499 | 6850 | 0.0006 | - |
2.4678 | 6900 | 0.0003 | - |
2.4857 | 6950 | 0.0002 | - |
2.5036 | 7000 | 0.0006 | - |
2.5215 | 7050 | 0.0006 | - |
2.5393 | 7100 | 0.0005 | - |
2.5572 | 7150 | 0.0004 | - |
2.5751 | 7200 | 0.0007 | - |
2.5930 | 7250 | 0.0009 | - |
2.6109 | 7300 | 0.0006 | - |
2.6288 | 7350 | 0.0003 | - |
2.6466 | 7400 | 0.0002 | - |
2.6645 | 7450 | 0.0016 | - |
2.6824 | 7500 | 0.0027 | - |
2.7003 | 7550 | 0.003 | - |
2.7182 | 7600 | 0.0036 | - |
2.7361 | 7650 | 0.0031 | - |
2.7539 | 7700 | 0.0012 | - |
2.7718 | 7750 | 0.0009 | - |
2.7897 | 7800 | 0.0004 | - |
2.8076 | 7850 | 0.0004 | - |
2.8255 | 7900 | 0.0002 | - |
2.8433 | 7950 | 0.0001 | - |
2.8612 | 8000 | 0.0001 | - |
2.8791 | 8050 | 0.0001 | - |
2.8970 | 8100 | 0.0002 | - |
2.9149 | 8150 | 0.0001 | - |
2.9328 | 8200 | 0.0004 | - |
2.9506 | 8250 | 0.0001 | - |
2.9685 | 8300 | 0.0002 | - |
2.9864 | 8350 | 0.0005 | - |
3.0043 | 8400 | 0.0004 | - |
3.0222 | 8450 | 0.0001 | - |
3.0401 | 8500 | 0.0004 | - |
3.0579 | 8550 | 0.0002 | - |
3.0758 | 8600 | 0.0003 | - |
3.0937 | 8650 | 0.0001 | - |
3.1116 | 8700 | 0.0002 | - |
3.1295 | 8750 | 0.0 | - |
3.1474 | 8800 | 0.0002 | - |
3.1652 | 8850 | 0.0001 | - |
3.1831 | 8900 | 0.0001 | - |
3.2010 | 8950 | 0.0 | - |
3.2189 | 9000 | 0.0 | - |
3.2368 | 9050 | 0.0 | - |
3.2546 | 9100 | 0.0 | - |
3.2725 | 9150 | 0.0002 | - |
3.2904 | 9200 | 0.0 | - |
3.3083 | 9250 | 0.0002 | - |
3.3262 | 9300 | 0.0002 | - |
3.3441 | 9350 | 0.0001 | - |
3.3619 | 9400 | 0.0002 | - |
3.3798 | 9450 | 0.0001 | - |
3.3977 | 9500 | 0.0001 | - |
3.4156 | 9550 | 0.0002 | - |
3.4335 | 9600 | 0.0013 | - |
3.4514 | 9650 | 0.0022 | - |
3.4692 | 9700 | 0.0072 | - |
3.4871 | 9750 | 0.0039 | - |
3.5050 | 9800 | 0.0022 | - |
3.5229 | 9850 | 0.0015 | - |
3.5408 | 9900 | 0.0007 | - |
3.5587 | 9950 | 0.0004 | - |
3.5765 | 10000 | 0.0002 | - |
3.5944 | 10050 | 0.0002 | - |
3.6123 | 10100 | 0.0002 | - |
3.6302 | 10150 | 0.0001 | - |
3.6481 | 10200 | 0.0004 | - |
3.6660 | 10250 | 0.0005 | - |
3.6838 | 10300 | 0.0002 | - |
3.7017 | 10350 | 0.0004 | - |
3.7196 | 10400 | 0.0003 | - |
3.7375 | 10450 | 0.0003 | - |
3.7554 | 10500 | 0.0003 | - |
3.7732 | 10550 | 0.0004 | - |
3.7911 | 10600 | 0.0003 | - |
3.8090 | 10650 | 0.0004 | - |
3.8269 | 10700 | 0.0 | - |
3.8448 | 10750 | 0.0004 | - |
3.8627 | 10800 | 0.0001 | - |
3.8805 | 10850 | 0.0002 | - |
3.8984 | 10900 | 0.0012 | - |
3.9163 | 10950 | 0.0007 | - |
3.9342 | 11000 | 0.0034 | - |
3.9521 | 11050 | 0.0021 | - |
3.9700 | 11100 | 0.0012 | - |
3.9878 | 11150 | 0.0005 | - |
4.0057 | 11200 | 0.0001 | - |
4.0236 | 11250 | 0.0001 | - |
4.0415 | 11300 | 0.0001 | - |
4.0594 | 11350 | 0.0 | - |
4.0773 | 11400 | 0.0002 | - |
4.0951 | 11450 | 0.0003 | - |
4.1130 | 11500 | 0.0002 | - |
4.1309 | 11550 | 0.0001 | - |
4.1488 | 11600 | 0.0 | - |
4.1667 | 11650 | 0.0 | - |
4.1845 | 11700 | 0.0 | - |
4.2024 | 11750 | 0.0 | - |
4.2203 | 11800 | 0.0 | - |
4.2382 | 11850 | 0.0 | - |
4.2561 | 11900 | 0.0 | - |
4.2740 | 11950 | 0.0 | - |
4.2918 | 12000 | 0.0 | - |
4.3097 | 12050 | 0.0 | - |
4.3276 | 12100 | 0.0 | - |
4.3455 | 12150 | 0.0 | - |
4.3634 | 12200 | 0.0003 | - |
4.3813 | 12250 | 0.0001 | - |
4.3991 | 12300 | 0.0001 | - |
4.4170 | 12350 | 0.0005 | - |
4.4349 | 12400 | 0.0013 | - |
4.4528 | 12450 | 0.0016 | - |
4.4707 | 12500 | 0.0019 | - |
4.4886 | 12550 | 0.0025 | - |
4.5064 | 12600 | 0.0024 | - |
4.5243 | 12650 | 0.0038 | - |
4.5422 | 12700 | 0.0026 | - |
4.5601 | 12750 | 0.0011 | - |
4.5780 | 12800 | 0.0003 | - |
4.5959 | 12850 | 0.0003 | - |
4.6137 | 12900 | 0.0004 | - |
4.6316 | 12950 | 0.0005 | - |
4.6495 | 13000 | 0.0001 | - |
4.6674 | 13050 | 0.0004 | - |
4.6853 | 13100 | 0.0002 | - |
4.7031 | 13150 | 0.0002 | - |
4.7210 | 13200 | 0.0 | - |
4.7389 | 13250 | 0.0 | - |
4.7568 | 13300 | 0.0002 | - |
4.7747 | 13350 | 0.0 | - |
4.7926 | 13400 | 0.0003 | - |
4.8104 | 13450 | 0.0001 | - |
4.8283 | 13500 | 0.0 | - |
4.8462 | 13550 | 0.0002 | - |
4.8641 | 13600 | 0.0 | - |
4.8820 | 13650 | 0.0 | - |
4.8999 | 13700 | 0.0 | - |
4.9177 | 13750 | 0.0 | - |
4.9356 | 13800 | 0.0 | - |
4.9535 | 13850 | 0.0 | - |
4.9714 | 13900 | 0.0 | - |
4.9893 | 13950 | 0.0 | - |
5.0072 | 14000 | 0.0 | - |
5.0250 | 14050 | 0.0 | - |
5.0429 | 14100 | 0.0002 | - |
5.0608 | 14150 | 0.0 | - |
5.0787 | 14200 | 0.0 | - |
5.0966 | 14250 | 0.0 | - |
5.1144 | 14300 | 0.0 | - |
5.1323 | 14350 | 0.0 | - |
5.1502 | 14400 | 0.0 | - |
5.1681 | 14450 | 0.0002 | - |
5.1860 | 14500 | 0.0 | - |
5.2039 | 14550 | 0.0 | - |
5.2217 | 14600 | 0.0 | - |
5.2396 | 14650 | 0.0 | - |
5.2575 | 14700 | 0.0 | - |
5.2754 | 14750 | 0.0002 | - |
5.2933 | 14800 | 0.0 | - |
5.3112 | 14850 | 0.0 | - |
5.3290 | 14900 | 0.0 | - |
5.3469 | 14950 | 0.0 | - |
5.3648 | 15000 | 0.0 | - |
5.3827 | 15050 | 0.0 | - |
5.4006 | 15100 | 0.0 | - |
5.4185 | 15150 | 0.0 | - |
5.4363 | 15200 | 0.0 | - |
5.4542 | 15250 | 0.0 | - |
5.4721 | 15300 | 0.0002 | - |
5.4900 | 15350 | 0.0 | - |
5.5079 | 15400 | 0.0 | - |
5.5258 | 15450 | 0.0 | - |
5.5436 | 15500 | 0.0 | - |
5.5615 | 15550 | 0.0 | - |
5.5794 | 15600 | 0.0 | - |
5.5973 | 15650 | 0.0 | - |
5.6152 | 15700 | 0.0 | - |
5.6330 | 15750 | 0.0 | - |
5.6509 | 15800 | 0.0 | - |
5.6688 | 15850 | 0.0 | - |
5.6867 | 15900 | 0.0002 | - |
5.7046 | 15950 | 0.0 | - |
5.7225 | 16000 | 0.0 | - |
5.7403 | 16050 | 0.0 | - |
5.7582 | 16100 | 0.0 | - |
5.7761 | 16150 | 0.0 | - |
5.7940 | 16200 | 0.0 | - |
5.8119 | 16250 | 0.0 | - |
5.8298 | 16300 | 0.0 | - |
5.8476 | 16350 | 0.0 | - |
5.8655 | 16400 | 0.0 | - |
5.8834 | 16450 | 0.0 | - |
5.9013 | 16500 | 0.0 | - |
5.9192 | 16550 | 0.0 | - |
5.9371 | 16600 | 0.0 | - |
5.9549 | 16650 | 0.0 | - |
5.9728 | 16700 | 0.0 | - |
5.9907 | 16750 | 0.0 | - |
6.0086 | 16800 | 0.0 | - |
6.0265 | 16850 | 0.0 | - |
6.0443 | 16900 | 0.0 | - |
6.0622 | 16950 | 0.0 | - |
6.0801 | 17000 | 0.0001 | - |
6.0980 | 17050 | 0.0001 | - |
6.1159 | 17100 | 0.0 | - |
6.1338 | 17150 | 0.0 | - |
6.1516 | 17200 | 0.0 | - |
6.1695 | 17250 | 0.0 | - |
6.1874 | 17300 | 0.0 | - |
6.2053 | 17350 | 0.0 | - |
6.2232 | 17400 | 0.0 | - |
6.2411 | 17450 | 0.0 | - |
6.2589 | 17500 | 0.0 | - |
6.2768 | 17550 | 0.0 | - |
6.2947 | 17600 | 0.0 | - |
6.3126 | 17650 | 0.0 | - |
6.3305 | 17700 | 0.0 | - |
6.3484 | 17750 | 0.0 | - |
6.3662 | 17800 | 0.0 | - |
6.3841 | 17850 | 0.0 | - |
6.4020 | 17900 | 0.0 | - |
6.4199 | 17950 | 0.0 | - |
6.4378 | 18000 | 0.0 | - |
6.4557 | 18050 | 0.0 | - |
6.4735 | 18100 | 0.0 | - |
6.4914 | 18150 | 0.0 | - |
6.5093 | 18200 | 0.0 | - |
6.5272 | 18250 | 0.0 | - |
6.5451 | 18300 | 0.0 | - |
6.5629 | 18350 | 0.0 | - |
6.5808 | 18400 | 0.0 | - |
6.5987 | 18450 | 0.0 | - |
6.6166 | 18500 | 0.0 | - |
6.6345 | 18550 | 0.0001 | - |
6.6524 | 18600 | 0.0 | - |
6.6702 | 18650 | 0.0 | - |
6.6881 | 18700 | 0.0003 | - |
6.7060 | 18750 | 0.0 | - |
6.7239 | 18800 | 0.0 | - |
6.7418 | 18850 | 0.0 | - |
6.7597 | 18900 | 0.0001 | - |
6.7775 | 18950 | 0.0 | - |
6.7954 | 19000 | 0.0 | - |
6.8133 | 19050 | 0.0 | - |
6.8312 | 19100 | 0.0 | - |
6.8491 | 19150 | 0.0 | - |
6.8670 | 19200 | 0.0 | - |
6.8848 | 19250 | 0.0 | - |
6.9027 | 19300 | 0.0 | - |
6.9206 | 19350 | 0.0 | - |
6.9385 | 19400 | 0.0 | - |
6.9564 | 19450 | 0.0 | - |
6.9742 | 19500 | 0.0 | - |
6.9921 | 19550 | 0.0 | - |
7.0100 | 19600 | 0.0 | - |
7.0279 | 19650 | 0.0 | - |
7.0458 | 19700 | 0.0 | - |
7.0637 | 19750 | 0.0 | - |
7.0815 | 19800 | 0.0 | - |
7.0994 | 19850 | 0.0 | - |
7.1173 | 19900 | 0.0 | - |
7.1352 | 19950 | 0.0 | - |
7.1531 | 20000 | 0.0 | - |
7.1710 | 20050 | 0.0 | - |
7.1888 | 20100 | 0.0 | - |
7.2067 | 20150 | 0.0 | - |
7.2246 | 20200 | 0.0 | - |
7.2425 | 20250 | 0.0 | - |
7.2604 | 20300 | 0.0 | - |
7.2783 | 20350 | 0.0 | - |
7.2961 | 20400 | 0.0 | - |
7.3140 | 20450 | 0.0 | - |
7.3319 | 20500 | 0.0 | - |
7.3498 | 20550 | 0.0 | - |
7.3677 | 20600 | 0.0 | - |
7.3856 | 20650 | 0.0 | - |
7.4034 | 20700 | 0.0 | - |
7.4213 | 20750 | 0.0 | - |
7.4392 | 20800 | 0.0 | - |
7.4571 | 20850 | 0.0 | - |
7.4750 | 20900 | 0.0 | - |
7.4928 | 20950 | 0.0 | - |
7.5107 | 21000 | 0.0 | - |
7.5286 | 21050 | 0.0 | - |
7.5465 | 21100 | 0.0 | - |
7.5644 | 21150 | 0.0 | - |
7.5823 | 21200 | 0.0 | - |
7.6001 | 21250 | 0.0 | - |
7.6180 | 21300 | 0.0 | - |
7.6359 | 21350 | 0.0 | - |
7.6538 | 21400 | 0.0 | - |
7.6717 | 21450 | 0.0 | - |
7.6896 | 21500 | 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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