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: 10 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 |
---|---|
9 |
|
2 |
|
0 |
|
4 |
|
8 |
|
6 |
|
3 |
|
5 |
|
7 |
|
1 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.6192 |
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_top13_test")
# Run inference
preds = model("오가니스트 히말라야 핑크솔트 샴푸 500ml X 5개 LotteOn > 뷰티 > 헤어케어 > 샴푸 > 드라이샴푸 LotteOn > 뷰티 > 헤어케어 > 샴푸 > 드라이샴푸")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 10 | 22.5992 | 68 |
Label | Training Sample Count |
---|---|
0 | 49 |
1 | 50 |
2 | 50 |
3 | 50 |
4 | 50 |
5 | 50 |
6 | 50 |
7 | 50 |
8 | 50 |
9 | 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 |
---|---|---|---|
0.0013 | 1 | 0.442 | - |
0.0641 | 50 | 0.4677 | - |
0.1282 | 100 | 0.4517 | - |
0.1923 | 150 | 0.447 | - |
0.2564 | 200 | 0.4161 | - |
0.3205 | 250 | 0.4126 | - |
0.3846 | 300 | 0.3875 | - |
0.4487 | 350 | 0.3417 | - |
0.5128 | 400 | 0.308 | - |
0.5769 | 450 | 0.2932 | - |
0.6410 | 500 | 0.2789 | - |
0.7051 | 550 | 0.2712 | - |
0.7692 | 600 | 0.2653 | - |
0.8333 | 650 | 0.2654 | - |
0.8974 | 700 | 0.2578 | - |
0.9615 | 750 | 0.2583 | - |
1.0256 | 800 | 0.2569 | - |
1.0897 | 850 | 0.2542 | - |
1.1538 | 900 | 0.256 | - |
1.2179 | 950 | 0.25 | - |
1.2821 | 1000 | 0.2544 | - |
1.3462 | 1050 | 0.2548 | - |
1.4103 | 1100 | 0.2591 | - |
1.4744 | 1150 | 0.2654 | - |
1.5385 | 1200 | 0.2493 | - |
1.6026 | 1250 | 0.2422 | - |
1.6667 | 1300 | 0.2383 | - |
1.7308 | 1350 | 0.2355 | - |
1.7949 | 1400 | 0.2281 | - |
1.8590 | 1450 | 0.2256 | - |
1.9231 | 1500 | 0.2285 | - |
1.9872 | 1550 | 0.2211 | - |
2.0513 | 1600 | 0.2143 | - |
2.1154 | 1650 | 0.2197 | - |
2.1795 | 1700 | 0.2094 | - |
2.2436 | 1750 | 0.2076 | - |
2.3077 | 1800 | 0.1998 | - |
2.3718 | 1850 | 0.1963 | - |
2.4359 | 1900 | 0.1906 | - |
2.5 | 1950 | 0.1895 | - |
2.5641 | 2000 | 0.1776 | - |
2.6282 | 2050 | 0.1537 | - |
2.6923 | 2100 | 0.1414 | - |
2.7564 | 2150 | 0.1344 | - |
2.8205 | 2200 | 0.1231 | - |
2.8846 | 2250 | 0.1119 | - |
2.9487 | 2300 | 0.107 | - |
3.0128 | 2350 | 0.0911 | - |
3.0769 | 2400 | 0.0757 | - |
3.1410 | 2450 | 0.0708 | - |
3.2051 | 2500 | 0.0621 | - |
3.2692 | 2550 | 0.0573 | - |
3.3333 | 2600 | 0.0513 | - |
3.3974 | 2650 | 0.0405 | - |
3.4615 | 2700 | 0.0311 | - |
3.5256 | 2750 | 0.0253 | - |
3.5897 | 2800 | 0.0226 | - |
3.6538 | 2850 | 0.0139 | - |
3.7179 | 2900 | 0.011 | - |
3.7821 | 2950 | 0.0102 | - |
3.8462 | 3000 | 0.0076 | - |
3.9103 | 3050 | 0.0065 | - |
3.9744 | 3100 | 0.0064 | - |
4.0385 | 3150 | 0.0056 | - |
4.1026 | 3200 | 0.0054 | - |
4.1667 | 3250 | 0.004 | - |
4.2308 | 3300 | 0.0022 | - |
4.2949 | 3350 | 0.0019 | - |
4.3590 | 3400 | 0.0024 | - |
4.4231 | 3450 | 0.0018 | - |
4.4872 | 3500 | 0.0014 | - |
4.5513 | 3550 | 0.0005 | - |
4.6154 | 3600 | 0.0006 | - |
4.6795 | 3650 | 0.0004 | - |
4.7436 | 3700 | 0.0006 | - |
4.8077 | 3750 | 0.0011 | - |
4.8718 | 3800 | 0.0004 | - |
4.9359 | 3850 | 0.001 | - |
5.0 | 3900 | 0.0002 | - |
5.0641 | 3950 | 0.0002 | - |
5.1282 | 4000 | 0.0006 | - |
5.1923 | 4050 | 0.0013 | - |
5.2564 | 4100 | 0.0009 | - |
5.3205 | 4150 | 0.0004 | - |
5.3846 | 4200 | 0.0001 | - |
5.4487 | 4250 | 0.0002 | - |
5.5128 | 4300 | 0.0002 | - |
5.5769 | 4350 | 0.0005 | - |
5.6410 | 4400 | 0.0041 | - |
5.7051 | 4450 | 0.0079 | - |
5.7692 | 4500 | 0.0071 | - |
5.8333 | 4550 | 0.0032 | - |
5.8974 | 4600 | 0.0045 | - |
5.9615 | 4650 | 0.0059 | - |
6.0256 | 4700 | 0.0066 | - |
6.0897 | 4750 | 0.0027 | - |
6.1538 | 4800 | 0.0006 | - |
6.2179 | 4850 | 0.0009 | - |
6.2821 | 4900 | 0.0005 | - |
6.3462 | 4950 | 0.0001 | - |
6.4103 | 5000 | 0.0002 | - |
6.4744 | 5050 | 0.0006 | - |
6.5385 | 5100 | 0.0003 | - |
6.6026 | 5150 | 0.0004 | - |
6.6667 | 5200 | 0.0004 | - |
6.7308 | 5250 | 0.0007 | - |
6.7949 | 5300 | 0.0004 | - |
6.8590 | 5350 | 0.0002 | - |
6.9231 | 5400 | 0.0002 | - |
6.9872 | 5450 | 0.0001 | - |
7.0513 | 5500 | 0.0002 | - |
7.1154 | 5550 | 0.0 | - |
7.1795 | 5600 | 0.0002 | - |
7.2436 | 5650 | 0.0001 | - |
7.3077 | 5700 | 0.0001 | - |
7.3718 | 5750 | 0.0004 | - |
7.4359 | 5800 | 0.0003 | - |
7.5 | 5850 | 0.0013 | - |
7.5641 | 5900 | 0.0026 | - |
7.6282 | 5950 | 0.002 | - |
7.6923 | 6000 | 0.0018 | - |
7.7564 | 6050 | 0.001 | - |
7.8205 | 6100 | 0.002 | - |
7.8846 | 6150 | 0.001 | - |
7.9487 | 6200 | 0.0009 | - |
8.0128 | 6250 | 0.0002 | - |
8.0769 | 6300 | 0.0 | - |
8.1410 | 6350 | 0.0 | - |
8.2051 | 6400 | 0.0 | - |
8.2692 | 6450 | 0.0 | - |
8.3333 | 6500 | 0.0 | - |
8.3974 | 6550 | 0.0 | - |
8.4615 | 6600 | 0.0 | - |
8.5256 | 6650 | 0.0 | - |
8.5897 | 6700 | 0.0 | - |
8.6538 | 6750 | 0.0 | - |
8.7179 | 6800 | 0.0 | - |
8.7821 | 6850 | 0.0 | - |
8.8462 | 6900 | 0.0019 | - |
8.9103 | 6950 | 0.0018 | - |
8.9744 | 7000 | 0.0007 | - |
9.0385 | 7050 | 0.001 | - |
9.1026 | 7100 | 0.0031 | - |
9.1667 | 7150 | 0.0018 | - |
9.2308 | 7200 | 0.0014 | - |
9.2949 | 7250 | 0.0017 | - |
9.3590 | 7300 | 0.0002 | - |
9.4231 | 7350 | 0.0003 | - |
9.4872 | 7400 | 0.0001 | - |
9.5513 | 7450 | 0.0001 | - |
9.6154 | 7500 | 0.0002 | - |
9.6795 | 7550 | 0.0002 | - |
9.7436 | 7600 | 0.0002 | - |
9.8077 | 7650 | 0.0003 | - |
9.8718 | 7700 | 0.0001 | - |
9.9359 | 7750 | 0.0 | - |
10.0 | 7800 | 0.0 | - |
10.0641 | 7850 | 0.0 | - |
10.1282 | 7900 | 0.0 | - |
10.1923 | 7950 | 0.0 | - |
10.2564 | 8000 | 0.0 | - |
10.3205 | 8050 | 0.0 | - |
10.3846 | 8100 | 0.0002 | - |
10.4487 | 8150 | 0.0 | - |
10.5128 | 8200 | 0.0 | - |
10.5769 | 8250 | 0.0 | - |
10.6410 | 8300 | 0.0 | - |
10.7051 | 8350 | 0.0 | - |
10.7692 | 8400 | 0.0 | - |
10.8333 | 8450 | 0.0 | - |
10.8974 | 8500 | 0.0 | - |
10.9615 | 8550 | 0.0 | - |
11.0256 | 8600 | 0.0 | - |
11.0897 | 8650 | 0.0 | - |
11.1538 | 8700 | 0.0 | - |
11.2179 | 8750 | 0.0 | - |
11.2821 | 8800 | 0.0 | - |
11.3462 | 8850 | 0.0 | - |
11.4103 | 8900 | 0.0 | - |
11.4744 | 8950 | 0.0 | - |
11.5385 | 9000 | 0.0 | - |
11.6026 | 9050 | 0.0 | - |
11.6667 | 9100 | 0.0001 | - |
11.7308 | 9150 | 0.0014 | - |
11.7949 | 9200 | 0.0 | - |
11.8590 | 9250 | 0.0002 | - |
11.9231 | 9300 | 0.0021 | - |
11.9872 | 9350 | 0.0043 | - |
12.0513 | 9400 | 0.0054 | - |
12.1154 | 9450 | 0.0068 | - |
12.1795 | 9500 | 0.0051 | - |
12.2436 | 9550 | 0.0023 | - |
12.3077 | 9600 | 0.0007 | - |
12.3718 | 9650 | 0.0002 | - |
12.4359 | 9700 | 0.0001 | - |
12.5 | 9750 | 0.0 | - |
12.5641 | 9800 | 0.0 | - |
12.6282 | 9850 | 0.0006 | - |
12.6923 | 9900 | 0.0005 | - |
12.7564 | 9950 | 0.0001 | - |
12.8205 | 10000 | 0.0 | - |
12.8846 | 10050 | 0.0 | - |
12.9487 | 10100 | 0.0 | - |
13.0128 | 10150 | 0.0 | - |
13.0769 | 10200 | 0.0 | - |
13.1410 | 10250 | 0.0 | - |
13.2051 | 10300 | 0.0 | - |
13.2692 | 10350 | 0.0 | - |
13.3333 | 10400 | 0.0 | - |
13.3974 | 10450 | 0.0 | - |
13.4615 | 10500 | 0.0 | - |
13.5256 | 10550 | 0.0 | - |
13.5897 | 10600 | 0.0 | - |
13.6538 | 10650 | 0.0 | - |
13.7179 | 10700 | 0.0 | - |
13.7821 | 10750 | 0.0 | - |
13.8462 | 10800 | 0.0 | - |
13.9103 | 10850 | 0.0 | - |
13.9744 | 10900 | 0.0 | - |
14.0385 | 10950 | 0.0 | - |
14.1026 | 11000 | 0.0 | - |
14.1667 | 11050 | 0.0 | - |
14.2308 | 11100 | 0.0 | - |
14.2949 | 11150 | 0.0 | - |
14.3590 | 11200 | 0.0 | - |
14.4231 | 11250 | 0.0 | - |
14.4872 | 11300 | 0.0 | - |
14.5513 | 11350 | 0.0 | - |
14.6154 | 11400 | 0.0 | - |
14.6795 | 11450 | 0.0 | - |
14.7436 | 11500 | 0.0 | - |
14.8077 | 11550 | 0.0 | - |
14.8718 | 11600 | 0.0 | - |
14.9359 | 11650 | 0.0 | - |
15.0 | 11700 | 0.0 | - |
15.0641 | 11750 | 0.0 | - |
15.1282 | 11800 | 0.0 | - |
15.1923 | 11850 | 0.0 | - |
15.2564 | 11900 | 0.0 | - |
15.3205 | 11950 | 0.0 | - |
15.3846 | 12000 | 0.0 | - |
15.4487 | 12050 | 0.0 | - |
15.5128 | 12100 | 0.0 | - |
15.5769 | 12150 | 0.0 | - |
15.6410 | 12200 | 0.0 | - |
15.7051 | 12250 | 0.0 | - |
15.7692 | 12300 | 0.0 | - |
15.8333 | 12350 | 0.0 | - |
15.8974 | 12400 | 0.0 | - |
15.9615 | 12450 | 0.0 | - |
16.0256 | 12500 | 0.0 | - |
16.0897 | 12550 | 0.0003 | - |
16.1538 | 12600 | 0.0022 | - |
16.2179 | 12650 | 0.0041 | - |
16.2821 | 12700 | 0.0006 | - |
16.3462 | 12750 | 0.0005 | - |
16.4103 | 12800 | 0.0002 | - |
16.4744 | 12850 | 0.0003 | - |
16.5385 | 12900 | 0.0002 | - |
16.6026 | 12950 | 0.0003 | - |
16.6667 | 13000 | 0.0 | - |
16.7308 | 13050 | 0.0 | - |
16.7949 | 13100 | 0.0 | - |
16.8590 | 13150 | 0.0002 | - |
16.9231 | 13200 | 0.0 | - |
16.9872 | 13250 | 0.0 | - |
17.0513 | 13300 | 0.0 | - |
17.1154 | 13350 | 0.0 | - |
17.1795 | 13400 | 0.0 | - |
17.2436 | 13450 | 0.0 | - |
17.3077 | 13500 | 0.0001 | - |
17.3718 | 13550 | 0.0 | - |
17.4359 | 13600 | 0.0002 | - |
17.5 | 13650 | 0.0 | - |
17.5641 | 13700 | 0.0 | - |
17.6282 | 13750 | 0.0 | - |
17.6923 | 13800 | 0.0 | - |
17.7564 | 13850 | 0.0 | - |
17.8205 | 13900 | 0.0 | - |
17.8846 | 13950 | 0.0 | - |
17.9487 | 14000 | 0.0 | - |
18.0128 | 14050 | 0.0 | - |
18.0769 | 14100 | 0.0 | - |
18.1410 | 14150 | 0.0 | - |
18.2051 | 14200 | 0.0 | - |
18.2692 | 14250 | 0.0 | - |
18.3333 | 14300 | 0.0 | - |
18.3974 | 14350 | 0.0 | - |
18.4615 | 14400 | 0.0 | - |
18.5256 | 14450 | 0.0 | - |
18.5897 | 14500 | 0.0 | - |
18.6538 | 14550 | 0.0 | - |
18.7179 | 14600 | 0.0 | - |
18.7821 | 14650 | 0.0 | - |
18.8462 | 14700 | 0.0 | - |
18.9103 | 14750 | 0.0 | - |
18.9744 | 14800 | 0.0 | - |
19.0385 | 14850 | 0.0 | - |
19.1026 | 14900 | 0.0 | - |
19.1667 | 14950 | 0.0 | - |
19.2308 | 15000 | 0.0 | - |
19.2949 | 15050 | 0.0 | - |
19.3590 | 15100 | 0.0 | - |
19.4231 | 15150 | 0.0002 | - |
19.4872 | 15200 | 0.0 | - |
19.5513 | 15250 | 0.0 | - |
19.6154 | 15300 | 0.0 | - |
19.6795 | 15350 | 0.0 | - |
19.7436 | 15400 | 0.0 | - |
19.8077 | 15450 | 0.0 | - |
19.8718 | 15500 | 0.0002 | - |
19.9359 | 15550 | 0.0 | - |
20.0 | 15600 | 0.0 | - |
20.0641 | 15650 | 0.0 | - |
20.1282 | 15700 | 0.0 | - |
20.1923 | 15750 | 0.0 | - |
20.2564 | 15800 | 0.0 | - |
20.3205 | 15850 | 0.0 | - |
20.3846 | 15900 | 0.0 | - |
20.4487 | 15950 | 0.0 | - |
20.5128 | 16000 | 0.0 | - |
20.5769 | 16050 | 0.0 | - |
20.6410 | 16100 | 0.0 | - |
20.7051 | 16150 | 0.0 | - |
20.7692 | 16200 | 0.0 | - |
20.8333 | 16250 | 0.0001 | - |
20.8974 | 16300 | 0.0002 | - |
20.9615 | 16350 | 0.0001 | - |
21.0256 | 16400 | 0.0 | - |
21.0897 | 16450 | 0.0011 | - |
21.1538 | 16500 | 0.0009 | - |
21.2179 | 16550 | 0.0006 | - |
21.2821 | 16600 | 0.0009 | - |
21.3462 | 16650 | 0.0001 | - |
21.4103 | 16700 | 0.0 | - |
21.4744 | 16750 | 0.0002 | - |
21.5385 | 16800 | 0.0 | - |
21.6026 | 16850 | 0.0 | - |
21.6667 | 16900 | 0.0002 | - |
21.7308 | 16950 | 0.0 | - |
21.7949 | 17000 | 0.0002 | - |
21.8590 | 17050 | 0.0002 | - |
21.9231 | 17100 | 0.0 | - |
21.9872 | 17150 | 0.0 | - |
22.0513 | 17200 | 0.0001 | - |
22.1154 | 17250 | 0.0 | - |
22.1795 | 17300 | 0.0 | - |
22.2436 | 17350 | 0.0 | - |
22.3077 | 17400 | 0.0 | - |
22.3718 | 17450 | 0.0 | - |
22.4359 | 17500 | 0.0 | - |
22.5 | 17550 | 0.0 | - |
22.5641 | 17600 | 0.0 | - |
22.6282 | 17650 | 0.0 | - |
22.6923 | 17700 | 0.0 | - |
22.7564 | 17750 | 0.0 | - |
22.8205 | 17800 | 0.0 | - |
22.8846 | 17850 | 0.0 | - |
22.9487 | 17900 | 0.0 | - |
23.0128 | 17950 | 0.0 | - |
23.0769 | 18000 | 0.0 | - |
23.1410 | 18050 | 0.0001 | - |
23.2051 | 18100 | 0.0 | - |
23.2692 | 18150 | 0.0 | - |
23.3333 | 18200 | 0.0001 | - |
23.3974 | 18250 | 0.0 | - |
23.4615 | 18300 | 0.0 | - |
23.5256 | 18350 | 0.0 | - |
23.5897 | 18400 | 0.0 | - |
23.6538 | 18450 | 0.0 | - |
23.7179 | 18500 | 0.0 | - |
23.7821 | 18550 | 0.0 | - |
23.8462 | 18600 | 0.0 | - |
23.9103 | 18650 | 0.0 | - |
23.9744 | 18700 | 0.0 | - |
24.0385 | 18750 | 0.0002 | - |
24.1026 | 18800 | 0.0 | - |
24.1667 | 18850 | 0.0 | - |
24.2308 | 18900 | 0.0 | - |
24.2949 | 18950 | 0.0001 | - |
24.3590 | 19000 | 0.0 | - |
24.4231 | 19050 | 0.0 | - |
24.4872 | 19100 | 0.0001 | - |
24.5513 | 19150 | 0.0 | - |
24.6154 | 19200 | 0.0 | - |
24.6795 | 19250 | 0.0 | - |
24.7436 | 19300 | 0.0 | - |
24.8077 | 19350 | 0.0 | - |
24.8718 | 19400 | 0.0 | - |
24.9359 | 19450 | 0.0 | - |
25.0 | 19500 | 0.0 | - |
25.0641 | 19550 | 0.0 | - |
25.1282 | 19600 | 0.0 | - |
25.1923 | 19650 | 0.0 | - |
25.2564 | 19700 | 0.0 | - |
25.3205 | 19750 | 0.0 | - |
25.3846 | 19800 | 0.0 | - |
25.4487 | 19850 | 0.0 | - |
25.5128 | 19900 | 0.0 | - |
25.5769 | 19950 | 0.0 | - |
25.6410 | 20000 | 0.0 | - |
25.7051 | 20050 | 0.0 | - |
25.7692 | 20100 | 0.0 | - |
25.8333 | 20150 | 0.0 | - |
25.8974 | 20200 | 0.0 | - |
25.9615 | 20250 | 0.0 | - |
26.0256 | 20300 | 0.0 | - |
26.0897 | 20350 | 0.0 | - |
26.1538 | 20400 | 0.0 | - |
26.2179 | 20450 | 0.0 | - |
26.2821 | 20500 | 0.0 | - |
26.3462 | 20550 | 0.0 | - |
26.4103 | 20600 | 0.0 | - |
26.4744 | 20650 | 0.0 | - |
26.5385 | 20700 | 0.0 | - |
26.6026 | 20750 | 0.0 | - |
26.6667 | 20800 | 0.0 | - |
26.7308 | 20850 | 0.0 | - |
26.7949 | 20900 | 0.0 | - |
26.8590 | 20950 | 0.0 | - |
26.9231 | 21000 | 0.0 | - |
26.9872 | 21050 | 0.0 | - |
27.0513 | 21100 | 0.0 | - |
27.1154 | 21150 | 0.0 | - |
27.1795 | 21200 | 0.0 | - |
27.2436 | 21250 | 0.0 | - |
27.3077 | 21300 | 0.0 | - |
27.3718 | 21350 | 0.0 | - |
27.4359 | 21400 | 0.0 | - |
27.5 | 21450 | 0.0 | - |
27.5641 | 21500 | 0.0 | - |
27.6282 | 21550 | 0.0 | - |
27.6923 | 21600 | 0.0 | - |
27.7564 | 21650 | 0.0 | - |
27.8205 | 21700 | 0.0 | - |
27.8846 | 21750 | 0.0 | - |
27.9487 | 21800 | 0.0 | - |
28.0128 | 21850 | 0.0 | - |
28.0769 | 21900 | 0.0 | - |
28.1410 | 21950 | 0.0 | - |
28.2051 | 22000 | 0.0 | - |
28.2692 | 22050 | 0.0 | - |
28.3333 | 22100 | 0.0 | - |
28.3974 | 22150 | 0.0 | - |
28.4615 | 22200 | 0.0 | - |
28.5256 | 22250 | 0.0 | - |
28.5897 | 22300 | 0.0 | - |
28.6538 | 22350 | 0.0 | - |
28.7179 | 22400 | 0.0 | - |
28.7821 | 22450 | 0.0 | - |
28.8462 | 22500 | 0.0 | - |
28.9103 | 22550 | 0.0 | - |
28.9744 | 22600 | 0.0 | - |
29.0385 | 22650 | 0.0 | - |
29.1026 | 22700 | 0.0 | - |
29.1667 | 22750 | 0.0 | - |
29.2308 | 22800 | 0.0 | - |
29.2949 | 22850 | 0.0 | - |
29.3590 | 22900 | 0.0 | - |
29.4231 | 22950 | 0.0 | - |
29.4872 | 23000 | 0.0 | - |
29.5513 | 23050 | 0.0 | - |
29.6154 | 23100 | 0.0 | - |
29.6795 | 23150 | 0.0 | - |
29.7436 | 23200 | 0.0 | - |
29.8077 | 23250 | 0.0 | - |
29.8718 | 23300 | 0.0 | - |
29.9359 | 23350 | 0.0 | - |
30.0 | 23400 | 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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