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: 11 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 |
|
4 |
|
8 |
|
9 |
|
6 |
|
1 |
|
3 |
|
7 |
|
10 |
|
2 |
|
5 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.4775 |
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_top7_test")
# Run inference
preds = model("[미샤] 코튼 믹스 블러셔 (3호 크레이프 케이크) LotteOn > 뷰티 > 색조메이크업 > 블러셔 LotteOn > 뷰티 > 색조메이크업 > 블러셔")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 10 | 24.3055 | 56 |
Label | Training Sample Count |
---|---|
0 | 50 |
1 | 50 |
2 | 50 |
3 | 50 |
4 | 50 |
5 | 50 |
6 | 50 |
7 | 50 |
8 | 50 |
9 | 50 |
10 | 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 |
---|---|---|---|
0.0012 | 1 | 0.4173 | - |
0.0581 | 50 | 0.4591 | - |
0.1163 | 100 | 0.4451 | - |
0.1744 | 150 | 0.4297 | - |
0.2326 | 200 | 0.4184 | - |
0.2907 | 250 | 0.4009 | - |
0.3488 | 300 | 0.3552 | - |
0.4070 | 350 | 0.3415 | - |
0.4651 | 400 | 0.3027 | - |
0.5233 | 450 | 0.2814 | - |
0.5814 | 500 | 0.2676 | - |
0.6395 | 550 | 0.2582 | - |
0.6977 | 600 | 0.2545 | - |
0.7558 | 650 | 0.2444 | - |
0.8140 | 700 | 0.2461 | - |
0.8721 | 750 | 0.2456 | - |
0.9302 | 800 | 0.2411 | - |
0.9884 | 850 | 0.2325 | - |
1.0465 | 900 | 0.2316 | - |
1.1047 | 950 | 0.2217 | - |
1.1628 | 1000 | 0.2281 | - |
1.2209 | 1050 | 0.2214 | - |
1.2791 | 1100 | 0.2183 | - |
1.3372 | 1150 | 0.2173 | - |
1.3953 | 1200 | 0.2149 | - |
1.4535 | 1250 | 0.2083 | - |
1.5116 | 1300 | 0.2101 | - |
1.5698 | 1350 | 0.1977 | - |
1.6279 | 1400 | 0.1886 | - |
1.6860 | 1450 | 0.1739 | - |
1.7442 | 1500 | 0.1658 | - |
1.8023 | 1550 | 0.1613 | - |
1.8605 | 1600 | 0.1518 | - |
1.9186 | 1650 | 0.1391 | - |
1.9767 | 1700 | 0.1275 | - |
2.0349 | 1750 | 0.1172 | - |
2.0930 | 1800 | 0.0992 | - |
2.1512 | 1850 | 0.0901 | - |
2.2093 | 1900 | 0.0739 | - |
2.2674 | 1950 | 0.0641 | - |
2.3256 | 2000 | 0.055 | - |
2.3837 | 2050 | 0.0451 | - |
2.4419 | 2100 | 0.0439 | - |
2.5 | 2150 | 0.036 | - |
2.5581 | 2200 | 0.0304 | - |
2.6163 | 2250 | 0.0204 | - |
2.6744 | 2300 | 0.0182 | - |
2.7326 | 2350 | 0.0143 | - |
2.7907 | 2400 | 0.0131 | - |
2.8488 | 2450 | 0.011 | - |
2.9070 | 2500 | 0.0053 | - |
2.9651 | 2550 | 0.0059 | - |
3.0233 | 2600 | 0.0035 | - |
3.0814 | 2650 | 0.0027 | - |
3.1395 | 2700 | 0.0046 | - |
3.1977 | 2750 | 0.0043 | - |
3.2558 | 2800 | 0.0025 | - |
3.3140 | 2850 | 0.0023 | - |
3.3721 | 2900 | 0.0016 | - |
3.4302 | 2950 | 0.0009 | - |
3.4884 | 3000 | 0.0005 | - |
3.5465 | 3050 | 0.0003 | - |
3.6047 | 3100 | 0.0004 | - |
3.6628 | 3150 | 0.0006 | - |
3.7209 | 3200 | 0.0006 | - |
3.7791 | 3250 | 0.0002 | - |
3.8372 | 3300 | 0.0001 | - |
3.8953 | 3350 | 0.0001 | - |
3.9535 | 3400 | 0.0002 | - |
4.0116 | 3450 | 0.0003 | - |
4.0698 | 3500 | 0.0003 | - |
4.1279 | 3550 | 0.0001 | - |
4.1860 | 3600 | 0.0002 | - |
4.2442 | 3650 | 0.0002 | - |
4.3023 | 3700 | 0.0001 | - |
4.3605 | 3750 | 0.0001 | - |
4.4186 | 3800 | 0.0 | - |
4.4767 | 3850 | 0.0001 | - |
4.5349 | 3900 | 0.0002 | - |
4.5930 | 3950 | 0.0001 | - |
4.6512 | 4000 | 0.0 | - |
4.7093 | 4050 | 0.0 | - |
4.7674 | 4100 | 0.0001 | - |
4.8256 | 4150 | 0.0003 | - |
4.8837 | 4200 | 0.0009 | - |
4.9419 | 4250 | 0.0043 | - |
5.0 | 4300 | 0.0217 | - |
5.0581 | 4350 | 0.0111 | - |
5.1163 | 4400 | 0.0037 | - |
5.1744 | 4450 | 0.0062 | - |
5.2326 | 4500 | 0.0052 | - |
5.2907 | 4550 | 0.0035 | - |
5.3488 | 4600 | 0.0006 | - |
5.4070 | 4650 | 0.0003 | - |
5.4651 | 4700 | 0.0002 | - |
5.5233 | 4750 | 0.0003 | - |
5.5814 | 4800 | 0.0003 | - |
5.6395 | 4850 | 0.0004 | - |
5.6977 | 4900 | 0.0002 | - |
5.7558 | 4950 | 0.0001 | - |
5.8140 | 5000 | 0.0 | - |
5.8721 | 5050 | 0.0002 | - |
5.9302 | 5100 | 0.0001 | - |
5.9884 | 5150 | 0.0003 | - |
6.0465 | 5200 | 0.0017 | - |
6.1047 | 5250 | 0.0015 | - |
6.1628 | 5300 | 0.0007 | - |
6.2209 | 5350 | 0.0009 | - |
6.2791 | 5400 | 0.0008 | - |
6.3372 | 5450 | 0.0007 | - |
6.3953 | 5500 | 0.0001 | - |
6.4535 | 5550 | 0.0001 | - |
6.5116 | 5600 | 0.0 | - |
6.5698 | 5650 | 0.0 | - |
6.6279 | 5700 | 0.0 | - |
6.6860 | 5750 | 0.0 | - |
6.7442 | 5800 | 0.0 | - |
6.8023 | 5850 | 0.0004 | - |
6.8605 | 5900 | 0.0004 | - |
6.9186 | 5950 | 0.0002 | - |
6.9767 | 6000 | 0.0002 | - |
7.0349 | 6050 | 0.0027 | - |
7.0930 | 6100 | 0.0005 | - |
7.1512 | 6150 | 0.0002 | - |
7.2093 | 6200 | 0.0008 | - |
7.2674 | 6250 | 0.0017 | - |
7.3256 | 6300 | 0.0001 | - |
7.3837 | 6350 | 0.0 | - |
7.4419 | 6400 | 0.0 | - |
7.5 | 6450 | 0.0 | - |
7.5581 | 6500 | 0.0 | - |
7.6163 | 6550 | 0.0 | - |
7.6744 | 6600 | 0.0 | - |
7.7326 | 6650 | 0.0 | - |
7.7907 | 6700 | 0.0 | - |
7.8488 | 6750 | 0.0 | - |
7.9070 | 6800 | 0.0 | - |
7.9651 | 6850 | 0.0002 | - |
8.0233 | 6900 | 0.0006 | - |
8.0814 | 6950 | 0.0012 | - |
8.1395 | 7000 | 0.0054 | - |
8.1977 | 7050 | 0.0012 | - |
8.2558 | 7100 | 0.0004 | - |
8.3140 | 7150 | 0.001 | - |
8.3721 | 7200 | 0.0002 | - |
8.4302 | 7250 | 0.0002 | - |
8.4884 | 7300 | 0.0002 | - |
8.5465 | 7350 | 0.0002 | - |
8.6047 | 7400 | 0.0001 | - |
8.6628 | 7450 | 0.0 | - |
8.7209 | 7500 | 0.0 | - |
8.7791 | 7550 | 0.0 | - |
8.8372 | 7600 | 0.0 | - |
8.8953 | 7650 | 0.0 | - |
8.9535 | 7700 | 0.0 | - |
9.0116 | 7750 | 0.0 | - |
9.0698 | 7800 | 0.0 | - |
9.1279 | 7850 | 0.0 | - |
9.1860 | 7900 | 0.0 | - |
9.2442 | 7950 | 0.0 | - |
9.3023 | 8000 | 0.0 | - |
9.3605 | 8050 | 0.0 | - |
9.4186 | 8100 | 0.0 | - |
9.4767 | 8150 | 0.0 | - |
9.5349 | 8200 | 0.0 | - |
9.5930 | 8250 | 0.0 | - |
9.6512 | 8300 | 0.0 | - |
9.7093 | 8350 | 0.0 | - |
9.7674 | 8400 | 0.0 | - |
9.8256 | 8450 | 0.0 | - |
9.8837 | 8500 | 0.0 | - |
9.9419 | 8550 | 0.0 | - |
10.0 | 8600 | 0.0 | - |
10.0581 | 8650 | 0.0 | - |
10.1163 | 8700 | 0.0 | - |
10.1744 | 8750 | 0.0 | - |
10.2326 | 8800 | 0.0 | - |
10.2907 | 8850 | 0.0 | - |
10.3488 | 8900 | 0.0 | - |
10.4070 | 8950 | 0.0 | - |
10.4651 | 9000 | 0.0 | - |
10.5233 | 9050 | 0.0 | - |
10.5814 | 9100 | 0.0 | - |
10.6395 | 9150 | 0.0 | - |
10.6977 | 9200 | 0.0 | - |
10.7558 | 9250 | 0.0 | - |
10.8140 | 9300 | 0.0 | - |
10.8721 | 9350 | 0.0 | - |
10.9302 | 9400 | 0.0 | - |
10.9884 | 9450 | 0.0 | - |
11.0465 | 9500 | 0.0 | - |
11.1047 | 9550 | 0.0 | - |
11.1628 | 9600 | 0.0 | - |
11.2209 | 9650 | 0.0002 | - |
11.2791 | 9700 | 0.0039 | - |
11.3372 | 9750 | 0.0056 | - |
11.3953 | 9800 | 0.0016 | - |
11.4535 | 9850 | 0.0022 | - |
11.5116 | 9900 | 0.0031 | - |
11.5698 | 9950 | 0.002 | - |
11.6279 | 10000 | 0.0018 | - |
11.6860 | 10050 | 0.0008 | - |
11.7442 | 10100 | 0.0 | - |
11.8023 | 10150 | 0.0 | - |
11.8605 | 10200 | 0.0 | - |
11.9186 | 10250 | 0.0001 | - |
11.9767 | 10300 | 0.0005 | - |
12.0349 | 10350 | 0.0027 | - |
12.0930 | 10400 | 0.0004 | - |
12.1512 | 10450 | 0.0003 | - |
12.2093 | 10500 | 0.0001 | - |
12.2674 | 10550 | 0.0 | - |
12.3256 | 10600 | 0.0 | - |
12.3837 | 10650 | 0.0 | - |
12.4419 | 10700 | 0.0002 | - |
12.5 | 10750 | 0.0 | - |
12.5581 | 10800 | 0.0 | - |
12.6163 | 10850 | 0.0 | - |
12.6744 | 10900 | 0.0 | - |
12.7326 | 10950 | 0.0 | - |
12.7907 | 11000 | 0.0 | - |
12.8488 | 11050 | 0.0 | - |
12.9070 | 11100 | 0.0 | - |
12.9651 | 11150 | 0.0 | - |
13.0233 | 11200 | 0.0 | - |
13.0814 | 11250 | 0.0 | - |
13.1395 | 11300 | 0.0 | - |
13.1977 | 11350 | 0.0002 | - |
13.2558 | 11400 | 0.0 | - |
13.3140 | 11450 | 0.0 | - |
13.3721 | 11500 | 0.0 | - |
13.4302 | 11550 | 0.0002 | - |
13.4884 | 11600 | 0.0 | - |
13.5465 | 11650 | 0.0 | - |
13.6047 | 11700 | 0.0 | - |
13.6628 | 11750 | 0.0 | - |
13.7209 | 11800 | 0.0 | - |
13.7791 | 11850 | 0.0 | - |
13.8372 | 11900 | 0.0 | - |
13.8953 | 11950 | 0.0 | - |
13.9535 | 12000 | 0.0 | - |
14.0116 | 12050 | 0.0 | - |
14.0698 | 12100 | 0.0 | - |
14.1279 | 12150 | 0.0 | - |
14.1860 | 12200 | 0.0 | - |
14.2442 | 12250 | 0.0 | - |
14.3023 | 12300 | 0.0 | - |
14.3605 | 12350 | 0.0 | - |
14.4186 | 12400 | 0.0 | - |
14.4767 | 12450 | 0.0 | - |
14.5349 | 12500 | 0.0 | - |
14.5930 | 12550 | 0.0 | - |
14.6512 | 12600 | 0.0 | - |
14.7093 | 12650 | 0.0 | - |
14.7674 | 12700 | 0.0 | - |
14.8256 | 12750 | 0.0 | - |
14.8837 | 12800 | 0.0 | - |
14.9419 | 12850 | 0.0 | - |
15.0 | 12900 | 0.0 | - |
15.0581 | 12950 | 0.0 | - |
15.1163 | 13000 | 0.0 | - |
15.1744 | 13050 | 0.0 | - |
15.2326 | 13100 | 0.0 | - |
15.2907 | 13150 | 0.0 | - |
15.3488 | 13200 | 0.0 | - |
15.4070 | 13250 | 0.0 | - |
15.4651 | 13300 | 0.0 | - |
15.5233 | 13350 | 0.0 | - |
15.5814 | 13400 | 0.0 | - |
15.6395 | 13450 | 0.0 | - |
15.6977 | 13500 | 0.0 | - |
15.7558 | 13550 | 0.0 | - |
15.8140 | 13600 | 0.0 | - |
15.8721 | 13650 | 0.0 | - |
15.9302 | 13700 | 0.0002 | - |
15.9884 | 13750 | 0.0003 | - |
16.0465 | 13800 | 0.0032 | - |
16.1047 | 13850 | 0.0005 | - |
16.1628 | 13900 | 0.0007 | - |
16.2209 | 13950 | 0.0001 | - |
16.2791 | 14000 | 0.0 | - |
16.3372 | 14050 | 0.0001 | - |
16.3953 | 14100 | 0.0003 | - |
16.4535 | 14150 | 0.0002 | - |
16.5116 | 14200 | 0.0 | - |
16.5698 | 14250 | 0.0 | - |
16.6279 | 14300 | 0.0 | - |
16.6860 | 14350 | 0.0 | - |
16.7442 | 14400 | 0.0 | - |
16.8023 | 14450 | 0.0 | - |
16.8605 | 14500 | 0.0 | - |
16.9186 | 14550 | 0.0 | - |
16.9767 | 14600 | 0.0 | - |
17.0349 | 14650 | 0.0 | - |
17.0930 | 14700 | 0.0 | - |
17.1512 | 14750 | 0.0 | - |
17.2093 | 14800 | 0.0 | - |
17.2674 | 14850 | 0.0 | - |
17.3256 | 14900 | 0.0 | - |
17.3837 | 14950 | 0.0 | - |
17.4419 | 15000 | 0.0 | - |
17.5 | 15050 | 0.0 | - |
17.5581 | 15100 | 0.0 | - |
17.6163 | 15150 | 0.0 | - |
17.6744 | 15200 | 0.0 | - |
17.7326 | 15250 | 0.0 | - |
17.7907 | 15300 | 0.0 | - |
17.8488 | 15350 | 0.0 | - |
17.9070 | 15400 | 0.0 | - |
17.9651 | 15450 | 0.0 | - |
18.0233 | 15500 | 0.0 | - |
18.0814 | 15550 | 0.0 | - |
18.1395 | 15600 | 0.0 | - |
18.1977 | 15650 | 0.0 | - |
18.2558 | 15700 | 0.0 | - |
18.3140 | 15750 | 0.0 | - |
18.3721 | 15800 | 0.0 | - |
18.4302 | 15850 | 0.0 | - |
18.4884 | 15900 | 0.0 | - |
18.5465 | 15950 | 0.0 | - |
18.6047 | 16000 | 0.0 | - |
18.6628 | 16050 | 0.0 | - |
18.7209 | 16100 | 0.0 | - |
18.7791 | 16150 | 0.0 | - |
18.8372 | 16200 | 0.0 | - |
18.8953 | 16250 | 0.0 | - |
18.9535 | 16300 | 0.0 | - |
19.0116 | 16350 | 0.0 | - |
19.0698 | 16400 | 0.0 | - |
19.1279 | 16450 | 0.0 | - |
19.1860 | 16500 | 0.0 | - |
19.2442 | 16550 | 0.0 | - |
19.3023 | 16600 | 0.0 | - |
19.3605 | 16650 | 0.0014 | - |
19.4186 | 16700 | 0.0016 | - |
19.4767 | 16750 | 0.002 | - |
19.5349 | 16800 | 0.0025 | - |
19.5930 | 16850 | 0.0004 | - |
19.6512 | 16900 | 0.0001 | - |
19.7093 | 16950 | 0.0 | - |
19.7674 | 17000 | 0.0 | - |
19.8256 | 17050 | 0.0 | - |
19.8837 | 17100 | 0.0 | - |
19.9419 | 17150 | 0.0 | - |
20.0 | 17200 | 0.0 | - |
20.0581 | 17250 | 0.0 | - |
20.1163 | 17300 | 0.0 | - |
20.1744 | 17350 | 0.0 | - |
20.2326 | 17400 | 0.0 | - |
20.2907 | 17450 | 0.0 | - |
20.3488 | 17500 | 0.0 | - |
20.4070 | 17550 | 0.0 | - |
20.4651 | 17600 | 0.0 | - |
20.5233 | 17650 | 0.0 | - |
20.5814 | 17700 | 0.0 | - |
20.6395 | 17750 | 0.0 | - |
20.6977 | 17800 | 0.0 | - |
20.7558 | 17850 | 0.0 | - |
20.8140 | 17900 | 0.0 | - |
20.8721 | 17950 | 0.0 | - |
20.9302 | 18000 | 0.0 | - |
20.9884 | 18050 | 0.0 | - |
21.0465 | 18100 | 0.0 | - |
21.1047 | 18150 | 0.0 | - |
21.1628 | 18200 | 0.0 | - |
21.2209 | 18250 | 0.0 | - |
21.2791 | 18300 | 0.0 | - |
21.3372 | 18350 | 0.0 | - |
21.3953 | 18400 | 0.0 | - |
21.4535 | 18450 | 0.0 | - |
21.5116 | 18500 | 0.0 | - |
21.5698 | 18550 | 0.0 | - |
21.6279 | 18600 | 0.0 | - |
21.6860 | 18650 | 0.0 | - |
21.7442 | 18700 | 0.0 | - |
21.8023 | 18750 | 0.0 | - |
21.8605 | 18800 | 0.0 | - |
21.9186 | 18850 | 0.0 | - |
21.9767 | 18900 | 0.0 | - |
22.0349 | 18950 | 0.0 | - |
22.0930 | 19000 | 0.0 | - |
22.1512 | 19050 | 0.0 | - |
22.2093 | 19100 | 0.0 | - |
22.2674 | 19150 | 0.0 | - |
22.3256 | 19200 | 0.0 | - |
22.3837 | 19250 | 0.0 | - |
22.4419 | 19300 | 0.0 | - |
22.5 | 19350 | 0.0 | - |
22.5581 | 19400 | 0.0 | - |
22.6163 | 19450 | 0.0 | - |
22.6744 | 19500 | 0.0002 | - |
22.7326 | 19550 | 0.0002 | - |
22.7907 | 19600 | 0.001 | - |
22.8488 | 19650 | 0.0 | - |
22.9070 | 19700 | 0.0 | - |
22.9651 | 19750 | 0.0002 | - |
23.0233 | 19800 | 0.0 | - |
23.0814 | 19850 | 0.0 | - |
23.1395 | 19900 | 0.0 | - |
23.1977 | 19950 | 0.0 | - |
23.2558 | 20000 | 0.0001 | - |
23.3140 | 20050 | 0.0 | - |
23.3721 | 20100 | 0.0 | - |
23.4302 | 20150 | 0.0 | - |
23.4884 | 20200 | 0.0 | - |
23.5465 | 20250 | 0.0 | - |
23.6047 | 20300 | 0.0 | - |
23.6628 | 20350 | 0.0 | - |
23.7209 | 20400 | 0.0 | - |
23.7791 | 20450 | 0.0 | - |
23.8372 | 20500 | 0.0 | - |
23.8953 | 20550 | 0.0 | - |
23.9535 | 20600 | 0.0 | - |
24.0116 | 20650 | 0.0 | - |
24.0698 | 20700 | 0.0 | - |
24.1279 | 20750 | 0.0 | - |
24.1860 | 20800 | 0.0 | - |
24.2442 | 20850 | 0.0 | - |
24.3023 | 20900 | 0.0 | - |
24.3605 | 20950 | 0.0 | - |
24.4186 | 21000 | 0.0 | - |
24.4767 | 21050 | 0.0 | - |
24.5349 | 21100 | 0.0 | - |
24.5930 | 21150 | 0.0 | - |
24.6512 | 21200 | 0.0 | - |
24.7093 | 21250 | 0.0 | - |
24.7674 | 21300 | 0.0 | - |
24.8256 | 21350 | 0.0 | - |
24.8837 | 21400 | 0.0 | - |
24.9419 | 21450 | 0.0 | - |
25.0 | 21500 | 0.0 | - |
25.0581 | 21550 | 0.0 | - |
25.1163 | 21600 | 0.0 | - |
25.1744 | 21650 | 0.0 | - |
25.2326 | 21700 | 0.0 | - |
25.2907 | 21750 | 0.0 | - |
25.3488 | 21800 | 0.0 | - |
25.4070 | 21850 | 0.0 | - |
25.4651 | 21900 | 0.0 | - |
25.5233 | 21950 | 0.0 | - |
25.5814 | 22000 | 0.0 | - |
25.6395 | 22050 | 0.0 | - |
25.6977 | 22100 | 0.0 | - |
25.7558 | 22150 | 0.0 | - |
25.8140 | 22200 | 0.0 | - |
25.8721 | 22250 | 0.0 | - |
25.9302 | 22300 | 0.0 | - |
25.9884 | 22350 | 0.0 | - |
26.0465 | 22400 | 0.0 | - |
26.1047 | 22450 | 0.0 | - |
26.1628 | 22500 | 0.0 | - |
26.2209 | 22550 | 0.0 | - |
26.2791 | 22600 | 0.0002 | - |
26.3372 | 22650 | 0.0 | - |
26.3953 | 22700 | 0.0 | - |
26.4535 | 22750 | 0.0 | - |
26.5116 | 22800 | 0.0 | - |
26.5698 | 22850 | 0.0 | - |
26.6279 | 22900 | 0.0 | - |
26.6860 | 22950 | 0.0 | - |
26.7442 | 23000 | 0.0 | - |
26.8023 | 23050 | 0.0 | - |
26.8605 | 23100 | 0.0 | - |
26.9186 | 23150 | 0.0 | - |
26.9767 | 23200 | 0.0 | - |
27.0349 | 23250 | 0.0 | - |
27.0930 | 23300 | 0.0 | - |
27.1512 | 23350 | 0.0 | - |
27.2093 | 23400 | 0.0 | - |
27.2674 | 23450 | 0.0 | - |
27.3256 | 23500 | 0.0 | - |
27.3837 | 23550 | 0.0 | - |
27.4419 | 23600 | 0.0 | - |
27.5 | 23650 | 0.0 | - |
27.5581 | 23700 | 0.0 | - |
27.6163 | 23750 | 0.0 | - |
27.6744 | 23800 | 0.0 | - |
27.7326 | 23850 | 0.0 | - |
27.7907 | 23900 | 0.0 | - |
27.8488 | 23950 | 0.0 | - |
27.9070 | 24000 | 0.0 | - |
27.9651 | 24050 | 0.0 | - |
28.0233 | 24100 | 0.0 | - |
28.0814 | 24150 | 0.0 | - |
28.1395 | 24200 | 0.0 | - |
28.1977 | 24250 | 0.0 | - |
28.2558 | 24300 | 0.0 | - |
28.3140 | 24350 | 0.0 | - |
28.3721 | 24400 | 0.0 | - |
28.4302 | 24450 | 0.0 | - |
28.4884 | 24500 | 0.0 | - |
28.5465 | 24550 | 0.0 | - |
28.6047 | 24600 | 0.0 | - |
28.6628 | 24650 | 0.0 | - |
28.7209 | 24700 | 0.0 | - |
28.7791 | 24750 | 0.0 | - |
28.8372 | 24800 | 0.0 | - |
28.8953 | 24850 | 0.0 | - |
28.9535 | 24900 | 0.0 | - |
29.0116 | 24950 | 0.0 | - |
29.0698 | 25000 | 0.0 | - |
29.1279 | 25050 | 0.0 | - |
29.1860 | 25100 | 0.0 | - |
29.2442 | 25150 | 0.0 | - |
29.3023 | 25200 | 0.0 | - |
29.3605 | 25250 | 0.0 | - |
29.4186 | 25300 | 0.0 | - |
29.4767 | 25350 | 0.0 | - |
29.5349 | 25400 | 0.0 | - |
29.5930 | 25450 | 0.0 | - |
29.6512 | 25500 | 0.0 | - |
29.7093 | 25550 | 0.0 | - |
29.7674 | 25600 | 0.0 | - |
29.8256 | 25650 | 0.0 | - |
29.8837 | 25700 | 0.0 | - |
29.9419 | 25750 | 0.0 | - |
30.0 | 25800 | 0.0 | - |
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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