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: 28 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 |
---|---|
3.0 |
|
8.0 |
|
21.0 |
|
12.0 |
|
18.0 |
|
11.0 |
|
9.0 |
|
10.0 |
|
0.0 |
|
6.0 |
|
15.0 |
|
26.0 |
|
25.0 |
|
1.0 |
|
20.0 |
|
4.0 |
|
16.0 |
|
13.0 |
|
2.0 |
|
14.0 |
|
7.0 |
|
17.0 |
|
27.0 |
|
24.0 |
|
22.0 |
|
23.0 |
|
19.0 |
|
5.0 |
|
Evaluation
Metrics
Label | Metric |
---|---|
all | 0.9453 |
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_lh")
# Run inference
preds = model("닥종이 한지 순지 한지공예재료 색한지 색한지-49 한지세상")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 3 | 11.1993 | 36 |
Label | Training Sample Count |
---|---|
0.0 | 777 |
1.0 | 306 |
2.0 | 950 |
3.0 | 500 |
4.0 | 500 |
5.0 | 100 |
6.0 | 200 |
7.0 | 150 |
8.0 | 850 |
9.0 | 350 |
10.0 | 1200 |
11.0 | 400 |
12.0 | 500 |
13.0 | 600 |
14.0 | 450 |
15.0 | 661 |
16.0 | 450 |
17.0 | 270 |
18.0 | 600 |
19.0 | 250 |
20.0 | 750 |
21.0 | 350 |
22.0 | 550 |
23.0 | 150 |
24.0 | 100 |
25.0 | 800 |
26.0 | 550 |
27.0 | 550 |
Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (20, 20)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 40
- 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 |
---|---|---|---|
0.0005 | 1 | 0.3875 | - |
0.0231 | 50 | 0.3958 | - |
0.0461 | 100 | 0.3383 | - |
0.0692 | 150 | 0.2797 | - |
0.0923 | 200 | 0.2387 | - |
0.1154 | 250 | 0.2188 | - |
0.1384 | 300 | 0.1931 | - |
0.1615 | 350 | 0.1977 | - |
0.1846 | 400 | 0.1783 | - |
0.2077 | 450 | 0.18 | - |
0.2307 | 500 | 0.1473 | - |
0.2538 | 550 | 0.1518 | - |
0.2769 | 600 | 0.1293 | - |
0.3000 | 650 | 0.1377 | - |
0.3230 | 700 | 0.1154 | - |
0.3461 | 750 | 0.1155 | - |
0.3692 | 800 | 0.1077 | - |
0.3922 | 850 | 0.1152 | - |
0.4153 | 900 | 0.0943 | - |
0.4384 | 950 | 0.0869 | - |
0.4615 | 1000 | 0.087 | - |
0.4845 | 1050 | 0.0762 | - |
0.5076 | 1100 | 0.0716 | - |
0.5307 | 1150 | 0.0698 | - |
0.5538 | 1200 | 0.0661 | - |
0.5768 | 1250 | 0.0651 | - |
0.5999 | 1300 | 0.0741 | - |
0.6230 | 1350 | 0.0479 | - |
0.6461 | 1400 | 0.0514 | - |
0.6691 | 1450 | 0.0471 | - |
0.6922 | 1500 | 0.0439 | - |
0.7153 | 1550 | 0.0524 | - |
0.7383 | 1600 | 0.0454 | - |
0.7614 | 1650 | 0.051 | - |
0.7845 | 1700 | 0.0403 | - |
0.8076 | 1750 | 0.0381 | - |
0.8306 | 1800 | 0.0311 | - |
0.8537 | 1850 | 0.0388 | - |
0.8768 | 1900 | 0.0439 | - |
0.8999 | 1950 | 0.031 | - |
0.9229 | 2000 | 0.0328 | - |
0.9460 | 2050 | 0.0382 | - |
0.9691 | 2100 | 0.0256 | - |
0.9922 | 2150 | 0.0521 | - |
1.0152 | 2200 | 0.0313 | - |
1.0383 | 2250 | 0.0271 | - |
1.0614 | 2300 | 0.0382 | - |
1.0844 | 2350 | 0.0201 | - |
1.1075 | 2400 | 0.0327 | - |
1.1306 | 2450 | 0.0219 | - |
1.1537 | 2500 | 0.0179 | - |
1.1767 | 2550 | 0.0226 | - |
1.1998 | 2600 | 0.0265 | - |
1.2229 | 2650 | 0.0203 | - |
1.2460 | 2700 | 0.0191 | - |
1.2690 | 2750 | 0.0222 | - |
1.2921 | 2800 | 0.0256 | - |
1.3152 | 2850 | 0.018 | - |
1.3383 | 2900 | 0.0153 | - |
1.3613 | 2950 | 0.0128 | - |
1.3844 | 3000 | 0.0099 | - |
1.4075 | 3050 | 0.0139 | - |
1.4305 | 3100 | 0.0102 | - |
1.4536 | 3150 | 0.0137 | - |
1.4767 | 3200 | 0.0052 | - |
1.4998 | 3250 | 0.0082 | - |
1.5228 | 3300 | 0.0118 | - |
1.5459 | 3350 | 0.0085 | - |
1.5690 | 3400 | 0.0123 | - |
1.5921 | 3450 | 0.0147 | - |
1.6151 | 3500 | 0.0065 | - |
1.6382 | 3550 | 0.0057 | - |
1.6613 | 3600 | 0.0033 | - |
1.6844 | 3650 | 0.0048 | - |
1.7074 | 3700 | 0.0052 | - |
1.7305 | 3750 | 0.0013 | - |
1.7536 | 3800 | 0.0013 | - |
1.7766 | 3850 | 0.0037 | - |
1.7997 | 3900 | 0.0048 | - |
1.8228 | 3950 | 0.0048 | - |
1.8459 | 4000 | 0.0054 | - |
1.8689 | 4050 | 0.0045 | - |
1.8920 | 4100 | 0.004 | - |
1.9151 | 4150 | 0.0033 | - |
1.9382 | 4200 | 0.0043 | - |
1.9612 | 4250 | 0.0028 | - |
1.9843 | 4300 | 0.0027 | - |
2.0074 | 4350 | 0.009 | - |
2.0305 | 4400 | 0.0007 | - |
2.0535 | 4450 | 0.0044 | - |
2.0766 | 4500 | 0.0008 | - |
2.0997 | 4550 | 0.0005 | - |
2.1228 | 4600 | 0.0014 | - |
2.1458 | 4650 | 0.0008 | - |
2.1689 | 4700 | 0.0006 | - |
2.1920 | 4750 | 0.0044 | - |
2.2150 | 4800 | 0.0006 | - |
2.2381 | 4850 | 0.0019 | - |
2.2612 | 4900 | 0.0023 | - |
2.2843 | 4950 | 0.0026 | - |
2.3073 | 5000 | 0.001 | - |
2.3304 | 5050 | 0.0015 | - |
2.3535 | 5100 | 0.0023 | - |
2.3766 | 5150 | 0.0029 | - |
2.3996 | 5200 | 0.0007 | - |
2.4227 | 5250 | 0.0018 | - |
2.4458 | 5300 | 0.0007 | - |
2.4689 | 5350 | 0.0003 | - |
2.4919 | 5400 | 0.0014 | - |
2.5150 | 5450 | 0.0007 | - |
2.5381 | 5500 | 0.0003 | - |
2.5611 | 5550 | 0.0008 | - |
2.5842 | 5600 | 0.0018 | - |
2.6073 | 5650 | 0.0015 | - |
2.6304 | 5700 | 0.0005 | - |
2.6534 | 5750 | 0.0003 | - |
2.6765 | 5800 | 0.0005 | - |
2.6996 | 5850 | 0.0016 | - |
2.7227 | 5900 | 0.0028 | - |
2.7457 | 5950 | 0.0002 | - |
2.7688 | 6000 | 0.0006 | - |
2.7919 | 6050 | 0.0006 | - |
2.8150 | 6100 | 0.0015 | - |
2.8380 | 6150 | 0.0009 | - |
2.8611 | 6200 | 0.0002 | - |
2.8842 | 6250 | 0.0001 | - |
2.9072 | 6300 | 0.0001 | - |
2.9303 | 6350 | 0.0001 | - |
2.9534 | 6400 | 0.0001 | - |
2.9765 | 6450 | 0.0001 | - |
2.9995 | 6500 | 0.0001 | - |
3.0226 | 6550 | 0.0001 | - |
3.0457 | 6600 | 0.0001 | - |
3.0688 | 6650 | 0.0002 | - |
3.0918 | 6700 | 0.0004 | - |
3.1149 | 6750 | 0.002 | - |
3.1380 | 6800 | 0.001 | - |
3.1611 | 6850 | 0.0002 | - |
3.1841 | 6900 | 0.0001 | - |
3.2072 | 6950 | 0.0001 | - |
3.2303 | 7000 | 0.0001 | - |
3.2533 | 7050 | 0.0002 | - |
3.2764 | 7100 | 0.0002 | - |
3.2995 | 7150 | 0.0007 | - |
3.3226 | 7200 | 0.0002 | - |
3.3456 | 7250 | 0.0002 | - |
3.3687 | 7300 | 0.0001 | - |
3.3918 | 7350 | 0.0011 | - |
3.4149 | 7400 | 0.0009 | - |
3.4379 | 7450 | 0.0001 | - |
3.4610 | 7500 | 0.0001 | - |
3.4841 | 7550 | 0.0001 | - |
3.5072 | 7600 | 0.0001 | - |
3.5302 | 7650 | 0.0001 | - |
3.5533 | 7700 | 0.0001 | - |
3.5764 | 7750 | 0.0023 | - |
3.5994 | 7800 | 0.0003 | - |
3.6225 | 7850 | 0.0002 | - |
3.6456 | 7900 | 0.0001 | - |
3.6687 | 7950 | 0.0001 | - |
3.6917 | 8000 | 0.0001 | - |
3.7148 | 8050 | 0.0003 | - |
3.7379 | 8100 | 0.0001 | - |
3.7610 | 8150 | 0.0001 | - |
3.7840 | 8200 | 0.0003 | - |
3.8071 | 8250 | 0.0007 | - |
3.8302 | 8300 | 0.0001 | - |
3.8533 | 8350 | 0.0001 | - |
3.8763 | 8400 | 0.0001 | - |
3.8994 | 8450 | 0.0001 | - |
3.9225 | 8500 | 0.0004 | - |
3.9455 | 8550 | 0.0001 | - |
3.9686 | 8600 | 0.0005 | - |
3.9917 | 8650 | 0.0012 | - |
4.0148 | 8700 | 0.0001 | - |
4.0378 | 8750 | 0.0001 | - |
4.0609 | 8800 | 0.0006 | - |
4.0840 | 8850 | 0.0051 | - |
4.1071 | 8900 | 0.0001 | - |
4.1301 | 8950 | 0.0001 | - |
4.1532 | 9000 | 0.0 | - |
4.1763 | 9050 | 0.0001 | - |
4.1994 | 9100 | 0.0003 | - |
4.2224 | 9150 | 0.0 | - |
4.2455 | 9200 | 0.0 | - |
4.2686 | 9250 | 0.0 | - |
4.2916 | 9300 | 0.0001 | - |
4.3147 | 9350 | 0.0002 | - |
4.3378 | 9400 | 0.0002 | - |
4.3609 | 9450 | 0.0005 | - |
4.3839 | 9500 | 0.0001 | - |
4.4070 | 9550 | 0.0001 | - |
4.4301 | 9600 | 0.0 | - |
4.4532 | 9650 | 0.0 | - |
4.4762 | 9700 | 0.0001 | - |
4.4993 | 9750 | 0.0001 | - |
4.5224 | 9800 | 0.0003 | - |
4.5455 | 9850 | 0.0006 | - |
4.5685 | 9900 | 0.0001 | - |
4.5916 | 9950 | 0.0004 | - |
4.6147 | 10000 | 0.0011 | - |
4.6377 | 10050 | 0.0011 | - |
4.6608 | 10100 | 0.0004 | - |
4.6839 | 10150 | 0.0002 | - |
4.7070 | 10200 | 0.0 | - |
4.7300 | 10250 | 0.0001 | - |
4.7531 | 10300 | 0.0004 | - |
4.7762 | 10350 | 0.0 | - |
4.7993 | 10400 | 0.0 | - |
4.8223 | 10450 | 0.0 | - |
4.8454 | 10500 | 0.0 | - |
4.8685 | 10550 | 0.0 | - |
4.8916 | 10600 | 0.0 | - |
4.9146 | 10650 | 0.0 | - |
4.9377 | 10700 | 0.0 | - |
4.9608 | 10750 | 0.0004 | - |
4.9838 | 10800 | 0.0001 | - |
5.0069 | 10850 | 0.0001 | - |
5.0300 | 10900 | 0.0004 | - |
5.0531 | 10950 | 0.0001 | - |
5.0761 | 11000 | 0.0009 | - |
5.0992 | 11050 | 0.0002 | - |
5.1223 | 11100 | 0.0 | - |
5.1454 | 11150 | 0.0015 | - |
5.1684 | 11200 | 0.0002 | - |
5.1915 | 11250 | 0.0 | - |
5.2146 | 11300 | 0.0011 | - |
5.2377 | 11350 | 0.0 | - |
5.2607 | 11400 | 0.0 | - |
5.2838 | 11450 | 0.0 | - |
5.3069 | 11500 | 0.0001 | - |
5.3299 | 11550 | 0.0 | - |
5.3530 | 11600 | 0.0 | - |
5.3761 | 11650 | 0.001 | - |
5.3992 | 11700 | 0.0 | - |
5.4222 | 11750 | 0.0 | - |
5.4453 | 11800 | 0.0 | - |
5.4684 | 11850 | 0.0016 | - |
5.4915 | 11900 | 0.0016 | - |
5.5145 | 11950 | 0.0 | - |
5.5376 | 12000 | 0.0 | - |
5.5607 | 12050 | 0.0 | - |
5.5838 | 12100 | 0.0001 | - |
5.6068 | 12150 | 0.0004 | - |
5.6299 | 12200 | 0.0001 | - |
5.6530 | 12250 | 0.0001 | - |
5.6760 | 12300 | 0.0 | - |
5.6991 | 12350 | 0.0002 | - |
5.7222 | 12400 | 0.0 | - |
5.7453 | 12450 | 0.0 | - |
5.7683 | 12500 | 0.0 | - |
5.7914 | 12550 | 0.0006 | - |
5.8145 | 12600 | 0.0002 | - |
5.8376 | 12650 | 0.0 | - |
5.8606 | 12700 | 0.0013 | - |
5.8837 | 12750 | 0.0 | - |
5.9068 | 12800 | 0.0001 | - |
5.9299 | 12850 | 0.0001 | - |
5.9529 | 12900 | 0.0 | - |
5.9760 | 12950 | 0.0001 | - |
5.9991 | 13000 | 0.0001 | - |
6.0222 | 13050 | 0.0003 | - |
6.0452 | 13100 | 0.0001 | - |
6.0683 | 13150 | 0.0 | - |
6.0914 | 13200 | 0.0 | - |
6.1144 | 13250 | 0.0 | - |
6.1375 | 13300 | 0.0 | - |
6.1606 | 13350 | 0.0 | - |
6.1837 | 13400 | 0.0 | - |
6.2067 | 13450 | 0.0 | - |
6.2298 | 13500 | 0.0 | - |
6.2529 | 13550 | 0.0 | - |
6.2760 | 13600 | 0.0 | - |
6.2990 | 13650 | 0.0 | - |
6.3221 | 13700 | 0.0 | - |
6.3452 | 13750 | 0.0 | - |
6.3683 | 13800 | 0.0 | - |
6.3913 | 13850 | 0.0 | - |
6.4144 | 13900 | 0.0 | - |
6.4375 | 13950 | 0.0 | - |
6.4605 | 14000 | 0.0 | - |
6.4836 | 14050 | 0.0001 | - |
6.5067 | 14100 | 0.0 | - |
6.5298 | 14150 | 0.0 | - |
6.5528 | 14200 | 0.0 | - |
6.5759 | 14250 | 0.0 | - |
6.5990 | 14300 | 0.0 | - |
6.6221 | 14350 | 0.0001 | - |
6.6451 | 14400 | 0.0001 | - |
6.6682 | 14450 | 0.0 | - |
6.6913 | 14500 | 0.0001 | - |
6.7144 | 14550 | 0.0002 | - |
6.7374 | 14600 | 0.0017 | - |
6.7605 | 14650 | 0.0 | - |
6.7836 | 14700 | 0.0 | - |
6.8066 | 14750 | 0.0 | - |
6.8297 | 14800 | 0.0 | - |
6.8528 | 14850 | 0.0 | - |
6.8759 | 14900 | 0.0001 | - |
6.8989 | 14950 | 0.0 | - |
6.9220 | 15000 | 0.0 | - |
6.9451 | 15050 | 0.0 | - |
6.9682 | 15100 | 0.0 | - |
6.9912 | 15150 | 0.0008 | - |
7.0143 | 15200 | 0.0002 | - |
7.0374 | 15250 | 0.0033 | - |
7.0605 | 15300 | 0.002 | - |
7.0835 | 15350 | 0.0012 | - |
7.1066 | 15400 | 0.0 | - |
7.1297 | 15450 | 0.0 | - |
7.1527 | 15500 | 0.0019 | - |
7.1758 | 15550 | 0.0 | - |
7.1989 | 15600 | 0.0 | - |
7.2220 | 15650 | 0.0009 | - |
7.2450 | 15700 | 0.0003 | - |
7.2681 | 15750 | 0.0 | - |
7.2912 | 15800 | 0.0015 | - |
7.3143 | 15850 | 0.0 | - |
7.3373 | 15900 | 0.0 | - |
7.3604 | 15950 | 0.0 | - |
7.3835 | 16000 | 0.0 | - |
7.4066 | 16050 | 0.0 | - |
7.4296 | 16100 | 0.0 | - |
7.4527 | 16150 | 0.0 | - |
7.4758 | 16200 | 0.0 | - |
7.4988 | 16250 | 0.0001 | - |
7.5219 | 16300 | 0.0 | - |
7.5450 | 16350 | 0.0 | - |
7.5681 | 16400 | 0.0 | - |
7.5911 | 16450 | 0.0 | - |
7.6142 | 16500 | 0.0 | - |
7.6373 | 16550 | 0.0 | - |
7.6604 | 16600 | 0.0 | - |
7.6834 | 16650 | 0.0 | - |
7.7065 | 16700 | 0.0001 | - |
7.7296 | 16750 | 0.0 | - |
7.7527 | 16800 | 0.0 | - |
7.7757 | 16850 | 0.0 | - |
7.7988 | 16900 | 0.0 | - |
7.8219 | 16950 | 0.0 | - |
7.8449 | 17000 | 0.0 | - |
7.8680 | 17050 | 0.0 | - |
7.8911 | 17100 | 0.0 | - |
7.9142 | 17150 | 0.0 | - |
7.9372 | 17200 | 0.0 | - |
7.9603 | 17250 | 0.0 | - |
7.9834 | 17300 | 0.0 | - |
8.0065 | 17350 | 0.0 | - |
8.0295 | 17400 | 0.0 | - |
8.0526 | 17450 | 0.0 | - |
8.0757 | 17500 | 0.0 | - |
8.0988 | 17550 | 0.0012 | - |
8.1218 | 17600 | 0.0 | - |
8.1449 | 17650 | 0.0022 | - |
8.1680 | 17700 | 0.0006 | - |
8.1910 | 17750 | 0.0003 | - |
8.2141 | 17800 | 0.0 | - |
8.2372 | 17850 | 0.0 | - |
8.2603 | 17900 | 0.0 | - |
8.2833 | 17950 | 0.0007 | - |
8.3064 | 18000 | 0.0 | - |
8.3295 | 18050 | 0.0 | - |
8.3526 | 18100 | 0.0 | - |
8.3756 | 18150 | 0.0 | - |
8.3987 | 18200 | 0.0 | - |
8.4218 | 18250 | 0.0 | - |
8.4449 | 18300 | 0.0 | - |
8.4679 | 18350 | 0.0 | - |
8.4910 | 18400 | 0.0 | - |
8.5141 | 18450 | 0.0 | - |
8.5371 | 18500 | 0.0 | - |
8.5602 | 18550 | 0.0005 | - |
8.5833 | 18600 | 0.0 | - |
8.6064 | 18650 | 0.0 | - |
8.6294 | 18700 | 0.0 | - |
8.6525 | 18750 | 0.0 | - |
8.6756 | 18800 | 0.0 | - |
8.6987 | 18850 | 0.0 | - |
8.7217 | 18900 | 0.0 | - |
8.7448 | 18950 | 0.0 | - |
8.7679 | 19000 | 0.0 | - |
8.7910 | 19050 | 0.0 | - |
8.8140 | 19100 | 0.0 | - |
8.8371 | 19150 | 0.0 | - |
8.8602 | 19200 | 0.0 | - |
8.8832 | 19250 | 0.0 | - |
8.9063 | 19300 | 0.0001 | - |
8.9294 | 19350 | 0.0 | - |
8.9525 | 19400 | 0.0 | - |
8.9755 | 19450 | 0.0 | - |
8.9986 | 19500 | 0.0002 | - |
9.0217 | 19550 | 0.0 | - |
9.0448 | 19600 | 0.0 | - |
9.0678 | 19650 | 0.0 | - |
9.0909 | 19700 | 0.0 | - |
9.1140 | 19750 | 0.0 | - |
9.1371 | 19800 | 0.0 | - |
9.1601 | 19850 | 0.0 | - |
9.1832 | 19900 | 0.0 | - |
9.2063 | 19950 | 0.0001 | - |
9.2293 | 20000 | 0.0 | - |
9.2524 | 20050 | 0.0 | - |
9.2755 | 20100 | 0.0 | - |
9.2986 | 20150 | 0.0 | - |
9.3216 | 20200 | 0.0 | - |
9.3447 | 20250 | 0.0 | - |
9.3678 | 20300 | 0.0 | - |
9.3909 | 20350 | 0.0 | - |
9.4139 | 20400 | 0.0 | - |
9.4370 | 20450 | 0.0 | - |
9.4601 | 20500 | 0.0 | - |
9.4832 | 20550 | 0.0 | - |
9.5062 | 20600 | 0.0 | - |
9.5293 | 20650 | 0.0 | - |
9.5524 | 20700 | 0.0 | - |
9.5754 | 20750 | 0.0 | - |
9.5985 | 20800 | 0.0 | - |
9.6216 | 20850 | 0.0 | - |
9.6447 | 20900 | 0.0 | - |
9.6677 | 20950 | 0.0 | - |
9.6908 | 21000 | 0.0 | - |
9.7139 | 21050 | 0.0 | - |
9.7370 | 21100 | 0.0 | - |
9.7600 | 21150 | 0.0 | - |
9.7831 | 21200 | 0.0 | - |
9.8062 | 21250 | 0.0 | - |
9.8293 | 21300 | 0.0 | - |
9.8523 | 21350 | 0.0 | - |
9.8754 | 21400 | 0.0001 | - |
9.8985 | 21450 | 0.0 | - |
9.9216 | 21500 | 0.0 | - |
9.9446 | 21550 | 0.0 | - |
9.9677 | 21600 | 0.0017 | - |
9.9908 | 21650 | 0.0 | - |
10.0138 | 21700 | 0.0005 | - |
10.0369 | 21750 | 0.0032 | - |
10.0600 | 21800 | 0.0001 | - |
10.0831 | 21850 | 0.0 | - |
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Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.1.1
- Transformers: 4.46.1
- 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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