metadata
base_model: klue/roberta-base
library_name: setfit
metrics:
- metric
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Creative 크리에이티브 BT-W3 ATX-LL USB동글 (주)린드스토어
- text: LG전자 울트라HD 75UQ8300ENA / 스탠드형 프롬헬시
- text: 삼성전자 BESPOKE 김치플러스 4도어 490 L 매트 멜로우 화이트 RQ49C90X24W 공식인증점 삼성전자(주)
- text: '힐링쉴드 애플워치 케이스 PCC시리즈 라이트핑크 38mm '
- text: PS4 그란투리스모 스포트 한글판 / VR대응 / PlayStationHits (주)위브엔터테인먼트
inference: true
model-index:
- name: SetFit with klue/roberta-base
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: metric
value: 0.9105009566475337
name: Metric
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: 26 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 |
|---|---|
| 7 |
|
| 2 |
|
| 5 |
|
| 17 |
|
| 4 |
|
| 18 |
|
| 8 |
|
| 10 |
|
| 20 |
|
| 0 |
|
| 12 |
|
| 11 |
|
| 1 |
|
| 13 |
|
| 15 |
|
| 25 |
|
| 14 |
|
| 16 |
|
| 6 |
|
| 24 |
|
| 3 |
|
| 21 |
|
| 23 |
|
| 19 |
|
| 9 |
|
| 22 |
|
Evaluation
Metrics
| Label | Metric |
|---|---|
| all | 0.9105 |
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_el")
# Run inference
preds = model("힐링쉴드 애플워치 케이스 PCC시리즈 라이트핑크 38mm ")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 3 | 9.6062 | 34 |
| Label | Training Sample Count |
|---|---|
| 0 | 136 |
| 1 | 500 |
| 2 | 408 |
| 3 | 243 |
| 4 | 913 |
| 5 | 621 |
| 6 | 50 |
| 7 | 422 |
| 8 | 394 |
| 9 | 50 |
| 10 | 218 |
| 11 | 808 |
| 12 | 183 |
| 13 | 350 |
| 14 | 979 |
| 15 | 795 |
| 16 | 510 |
| 17 | 2159 |
| 18 | 413 |
| 19 | 129 |
| 20 | 950 |
| 21 | 50 |
| 22 | 200 |
| 23 | 181 |
| 24 | 50 |
| 25 | 699 |
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 | 0.4306 | - |
| 0.0258 | 50 | 0.3897 | - |
| 0.0515 | 100 | 0.3822 | - |
| 0.0773 | 150 | 0.3469 | - |
| 0.1031 | 200 | 0.2699 | - |
| 0.1289 | 250 | 0.2408 | - |
| 0.1546 | 300 | 0.1997 | - |
| 0.1804 | 350 | 0.1918 | - |
| 0.2062 | 400 | 0.1587 | - |
| 0.2320 | 450 | 0.1616 | - |
| 0.2577 | 500 | 0.1564 | - |
| 0.2835 | 550 | 0.1529 | - |
| 0.3093 | 600 | 0.1266 | - |
| 0.3351 | 650 | 0.1107 | - |
| 0.3608 | 700 | 0.126 | - |
| 0.3866 | 750 | 0.1178 | - |
| 0.4124 | 800 | 0.1081 | - |
| 0.4381 | 850 | 0.0919 | - |
| 0.4639 | 900 | 0.0781 | - |
| 0.4897 | 950 | 0.0776 | - |
| 0.5155 | 1000 | 0.0844 | - |
| 0.5412 | 1050 | 0.0782 | - |
| 0.5670 | 1100 | 0.0625 | - |
| 0.5928 | 1150 | 0.0659 | - |
| 0.6186 | 1200 | 0.0621 | - |
| 0.6443 | 1250 | 0.0455 | - |
| 0.6701 | 1300 | 0.0508 | - |
| 0.6959 | 1350 | 0.0468 | - |
| 0.7216 | 1400 | 0.0539 | - |
| 0.7474 | 1450 | 0.05 | - |
| 0.7732 | 1500 | 0.0333 | - |
| 0.7990 | 1550 | 0.0359 | - |
| 0.8247 | 1600 | 0.0277 | - |
| 0.8505 | 1650 | 0.0266 | - |
| 0.8763 | 1700 | 0.03 | - |
| 0.9021 | 1750 | 0.0172 | - |
| 0.9278 | 1800 | 0.0275 | - |
| 0.9536 | 1850 | 0.0264 | - |
| 0.9794 | 1900 | 0.0195 | - |
| 1.0052 | 1950 | 0.024 | - |
| 1.0309 | 2000 | 0.0161 | - |
| 1.0567 | 2050 | 0.0131 | - |
| 1.0825 | 2100 | 0.0211 | - |
| 1.1082 | 2150 | 0.023 | - |
| 1.1340 | 2200 | 0.0174 | - |
| 1.1598 | 2250 | 0.0127 | - |
| 1.1856 | 2300 | 0.0061 | - |
| 1.2113 | 2350 | 0.0071 | - |
| 1.2371 | 2400 | 0.0164 | - |
| 1.2629 | 2450 | 0.0098 | - |
| 1.2887 | 2500 | 0.0094 | - |
| 1.3144 | 2550 | 0.0062 | - |
| 1.3402 | 2600 | 0.0044 | - |
| 1.3660 | 2650 | 0.007 | - |
| 1.3918 | 2700 | 0.0083 | - |
| 1.4175 | 2750 | 0.0081 | - |
| 1.4433 | 2800 | 0.0076 | - |
| 1.4691 | 2850 | 0.0091 | - |
| 1.4948 | 2900 | 0.0044 | - |
| 1.5206 | 2950 | 0.003 | - |
| 1.5464 | 3000 | 0.0036 | - |
| 1.5722 | 3050 | 0.0016 | - |
| 1.5979 | 3100 | 0.0025 | - |
| 1.6237 | 3150 | 0.0029 | - |
| 1.6495 | 3200 | 0.0021 | - |
| 1.6753 | 3250 | 0.0025 | - |
| 1.7010 | 3300 | 0.0032 | - |
| 1.7268 | 3350 | 0.0013 | - |
| 1.7526 | 3400 | 0.0015 | - |
| 1.7784 | 3450 | 0.0037 | - |
| 1.8041 | 3500 | 0.0062 | - |
| 1.8299 | 3550 | 0.0022 | - |
| 1.8557 | 3600 | 0.0032 | - |
| 1.8814 | 3650 | 0.0011 | - |
| 1.9072 | 3700 | 0.0022 | - |
| 1.9330 | 3750 | 0.0032 | - |
| 1.9588 | 3800 | 0.001 | - |
| 1.9845 | 3850 | 0.0012 | - |
| 2.0103 | 3900 | 0.0007 | - |
| 2.0361 | 3950 | 0.0009 | - |
| 2.0619 | 4000 | 0.0007 | - |
| 2.0876 | 4050 | 0.0004 | - |
| 2.1134 | 4100 | 0.0014 | - |
| 2.1392 | 4150 | 0.002 | - |
| 2.1649 | 4200 | 0.0008 | - |
| 2.1907 | 4250 | 0.0003 | - |
| 2.2165 | 4300 | 0.0005 | - |
| 2.2423 | 4350 | 0.001 | - |
| 2.2680 | 4400 | 0.0002 | - |
| 2.2938 | 4450 | 0.0007 | - |
| 2.3196 | 4500 | 0.0018 | - |
| 2.3454 | 4550 | 0.0002 | - |
| 2.3711 | 4600 | 0.0021 | - |
| 2.3969 | 4650 | 0.0006 | - |
| 2.4227 | 4700 | 0.0014 | - |
| 2.4485 | 4750 | 0.0028 | - |
| 2.4742 | 4800 | 0.0021 | - |
| 2.5 | 4850 | 0.0001 | - |
| 2.5258 | 4900 | 0.0001 | - |
| 2.5515 | 4950 | 0.0004 | - |
| 2.5773 | 5000 | 0.0002 | - |
| 2.6031 | 5050 | 0.0006 | - |
| 2.6289 | 5100 | 0.0004 | - |
| 2.6546 | 5150 | 0.0001 | - |
| 2.6804 | 5200 | 0.0002 | - |
| 2.7062 | 5250 | 0.0005 | - |
| 2.7320 | 5300 | 0.0001 | - |
| 2.7577 | 5350 | 0.0005 | - |
| 2.7835 | 5400 | 0.0001 | - |
| 2.8093 | 5450 | 0.0016 | - |
| 2.8351 | 5500 | 0.002 | - |
| 2.8608 | 5550 | 0.0002 | - |
| 2.8866 | 5600 | 0.0014 | - |
| 2.9124 | 5650 | 0.0004 | - |
| 2.9381 | 5700 | 0.0001 | - |
| 2.9639 | 5750 | 0.0018 | - |
| 2.9897 | 5800 | 0.0003 | - |
| 3.0155 | 5850 | 0.0005 | - |
| 3.0412 | 5900 | 0.0001 | - |
| 3.0670 | 5950 | 0.0002 | - |
| 3.0928 | 6000 | 0.0013 | - |
| 3.1186 | 6050 | 0.0024 | - |
| 3.1443 | 6100 | 0.0004 | - |
| 3.1701 | 6150 | 0.0006 | - |
| 3.1959 | 6200 | 0.0004 | - |
| 3.2216 | 6250 | 0.0005 | - |
| 3.2474 | 6300 | 0.0001 | - |
| 3.2732 | 6350 | 0.0002 | - |
| 3.2990 | 6400 | 0.0003 | - |
| 3.3247 | 6450 | 0.0018 | - |
| 3.3505 | 6500 | 0.0001 | - |
| 3.3763 | 6550 | 0.0001 | - |
| 3.4021 | 6600 | 0.0001 | - |
| 3.4278 | 6650 | 0.0001 | - |
| 3.4536 | 6700 | 0.0001 | - |
| 3.4794 | 6750 | 0.002 | - |
| 3.5052 | 6800 | 0.0001 | - |
| 3.5309 | 6850 | 0.0008 | - |
| 3.5567 | 6900 | 0.0006 | - |
| 3.5825 | 6950 | 0.0008 | - |
| 3.6082 | 7000 | 0.0001 | - |
| 3.6340 | 7050 | 0.0001 | - |
| 3.6598 | 7100 | 0.0001 | - |
| 3.6856 | 7150 | 0.0011 | - |
| 3.7113 | 7200 | 0.0034 | - |
| 3.7371 | 7250 | 0.0008 | - |
| 3.7629 | 7300 | 0.0002 | - |
| 3.7887 | 7350 | 0.0001 | - |
| 3.8144 | 7400 | 0.0002 | - |
| 3.8402 | 7450 | 0.002 | - |
| 3.8660 | 7500 | 0.0007 | - |
| 3.8918 | 7550 | 0.0024 | - |
| 3.9175 | 7600 | 0.003 | - |
| 3.9433 | 7650 | 0.0001 | - |
| 3.9691 | 7700 | 0.0003 | - |
| 3.9948 | 7750 | 0.0002 | - |
| 4.0206 | 7800 | 0.0002 | - |
| 4.0464 | 7850 | 0.0001 | - |
| 4.0722 | 7900 | 0.0 | - |
| 4.0979 | 7950 | 0.0 | - |
| 4.1237 | 8000 | 0.0001 | - |
| 4.1495 | 8050 | 0.001 | - |
| 4.1753 | 8100 | 0.0001 | - |
| 4.2010 | 8150 | 0.0 | - |
| 4.2268 | 8200 | 0.0018 | - |
| 4.2526 | 8250 | 0.0001 | - |
| 4.2784 | 8300 | 0.0 | - |
| 4.3041 | 8350 | 0.0014 | - |
| 4.3299 | 8400 | 0.0007 | - |
| 4.3557 | 8450 | 0.0003 | - |
| 4.3814 | 8500 | 0.0002 | - |
| 4.4072 | 8550 | 0.0002 | - |
| 4.4330 | 8600 | 0.0011 | - |
| 4.4588 | 8650 | 0.0002 | - |
| 4.4845 | 8700 | 0.0001 | - |
| 4.5103 | 8750 | 0.0003 | - |
| 4.5361 | 8800 | 0.0001 | - |
| 4.5619 | 8850 | 0.0 | - |
| 4.5876 | 8900 | 0.0 | - |
| 4.6134 | 8950 | 0.0 | - |
| 4.6392 | 9000 | 0.0001 | - |
| 4.6649 | 9050 | 0.0001 | - |
| 4.6907 | 9100 | 0.0001 | - |
| 4.7165 | 9150 | 0.0 | - |
| 4.7423 | 9200 | 0.0 | - |
| 4.7680 | 9250 | 0.0003 | - |
| 4.7938 | 9300 | 0.0012 | - |
| 4.8196 | 9350 | 0.0004 | - |
| 4.8454 | 9400 | 0.0005 | - |
| 4.8711 | 9450 | 0.0019 | - |
| 4.8969 | 9500 | 0.0001 | - |
| 4.9227 | 9550 | 0.0013 | - |
| 4.9485 | 9600 | 0.0001 | - |
| 4.9742 | 9650 | 0.0002 | - |
| 5.0 | 9700 | 0.0001 | - |
| 5.0258 | 9750 | 0.0 | - |
| 5.0515 | 9800 | 0.0 | - |
| 5.0773 | 9850 | 0.0009 | - |
| 5.1031 | 9900 | 0.0 | - |
| 5.1289 | 9950 | 0.0 | - |
| 5.1546 | 10000 | 0.0 | - |
| 5.1804 | 10050 | 0.0 | - |
| 5.2062 | 10100 | 0.002 | - |
| 5.2320 | 10150 | 0.0003 | - |
| 5.2577 | 10200 | 0.0001 | - |
| 5.2835 | 10250 | 0.0 | - |
| 5.3093 | 10300 | 0.0025 | - |
| 5.3351 | 10350 | 0.0 | - |
| 5.3608 | 10400 | 0.0001 | - |
| 5.3866 | 10450 | 0.0 | - |
| 5.4124 | 10500 | 0.0 | - |
| 5.4381 | 10550 | 0.0 | - |
| 5.4639 | 10600 | 0.0 | - |
| 5.4897 | 10650 | 0.0 | - |
| 5.5155 | 10700 | 0.0 | - |
| 5.5412 | 10750 | 0.0 | - |
| 5.5670 | 10800 | 0.0 | - |
| 5.5928 | 10850 | 0.0 | - |
| 5.6186 | 10900 | 0.0001 | - |
| 5.6443 | 10950 | 0.0 | - |
| 5.6701 | 11000 | 0.0001 | - |
| 5.6959 | 11050 | 0.0 | - |
| 5.7216 | 11100 | 0.0 | - |
| 5.7474 | 11150 | 0.0016 | - |
| 5.7732 | 11200 | 0.0033 | - |
| 5.7990 | 11250 | 0.001 | - |
| 5.8247 | 11300 | 0.0001 | - |
| 5.8505 | 11350 | 0.0 | - |
| 5.8763 | 11400 | 0.0 | - |
| 5.9021 | 11450 | 0.0 | - |
| 5.9278 | 11500 | 0.0002 | - |
| 5.9536 | 11550 | 0.0 | - |
| 5.9794 | 11600 | 0.0001 | - |
| 6.0052 | 11650 | 0.0012 | - |
| 6.0309 | 11700 | 0.0001 | - |
| 6.0567 | 11750 | 0.0001 | - |
| 6.0825 | 11800 | 0.0018 | - |
| 6.1082 | 11850 | 0.0006 | - |
| 6.1340 | 11900 | 0.0001 | - |
| 6.1598 | 11950 | 0.0 | - |
| 6.1856 | 12000 | 0.0042 | - |
| 6.2113 | 12050 | 0.0001 | - |
| 6.2371 | 12100 | 0.0001 | - |
| 6.2629 | 12150 | 0.0 | - |
| 6.2887 | 12200 | 0.0 | - |
| 6.3144 | 12250 | 0.0 | - |
| 6.3402 | 12300 | 0.0006 | - |
| 6.3660 | 12350 | 0.0001 | - |
| 6.3918 | 12400 | 0.0 | - |
| 6.4175 | 12450 | 0.0 | - |
| 6.4433 | 12500 | 0.0 | - |
| 6.4691 | 12550 | 0.0 | - |
| 6.4948 | 12600 | 0.0001 | - |
| 6.5206 | 12650 | 0.0 | - |
| 6.5464 | 12700 | 0.0 | - |
| 6.5722 | 12750 | 0.0 | - |
| 6.5979 | 12800 | 0.0 | - |
| 6.6237 | 12850 | 0.0 | - |
| 6.6495 | 12900 | 0.0 | - |
| 6.6753 | 12950 | 0.0 | - |
| 6.7010 | 13000 | 0.0 | - |
| 6.7268 | 13050 | 0.0 | - |
| 6.7526 | 13100 | 0.0001 | - |
| 6.7784 | 13150 | 0.0 | - |
| 6.8041 | 13200 | 0.0 | - |
| 6.8299 | 13250 | 0.0 | - |
| 6.8557 | 13300 | 0.0 | - |
| 6.8814 | 13350 | 0.0002 | - |
| 6.9072 | 13400 | 0.0001 | - |
| 6.9330 | 13450 | 0.0 | - |
| 6.9588 | 13500 | 0.0 | - |
| 6.9845 | 13550 | 0.0012 | - |
| 7.0103 | 13600 | 0.0 | - |
| 7.0361 | 13650 | 0.0001 | - |
| 7.0619 | 13700 | 0.0047 | - |
| 7.0876 | 13750 | 0.002 | - |
| 7.1134 | 13800 | 0.0001 | - |
| 7.1392 | 13850 | 0.0012 | - |
| 7.1649 | 13900 | 0.0 | - |
| 7.1907 | 13950 | 0.0015 | - |
| 7.2165 | 14000 | 0.0 | - |
| 7.2423 | 14050 | 0.0 | - |
| 7.2680 | 14100 | 0.0 | - |
| 7.2938 | 14150 | 0.0 | - |
| 7.3196 | 14200 | 0.0 | - |
| 7.3454 | 14250 | 0.0 | - |
| 7.3711 | 14300 | 0.0 | - |
| 7.3969 | 14350 | 0.0 | - |
| 7.4227 | 14400 | 0.0 | - |
| 7.4485 | 14450 | 0.0 | - |
| 7.4742 | 14500 | 0.0 | - |
| 7.5 | 14550 | 0.0 | - |
| 7.5258 | 14600 | 0.0001 | - |
| 7.5515 | 14650 | 0.0 | - |
| 7.5773 | 14700 | 0.0001 | - |
| 7.6031 | 14750 | 0.0035 | - |
| 7.6289 | 14800 | 0.0 | - |
| 7.6546 | 14850 | 0.0 | - |
| 7.6804 | 14900 | 0.0003 | - |
| 7.7062 | 14950 | 0.0 | - |
| 7.7320 | 15000 | 0.0 | - |
| 7.7577 | 15050 | 0.0 | - |
| 7.7835 | 15100 | 0.0 | - |
| 7.8093 | 15150 | 0.0 | - |
| 7.8351 | 15200 | 0.0 | - |
| 7.8608 | 15250 | 0.0 | - |
| 7.8866 | 15300 | 0.0019 | - |
| 7.9124 | 15350 | 0.0018 | - |
| 7.9381 | 15400 | 0.0 | - |
| 7.9639 | 15450 | 0.0001 | - |
| 7.9897 | 15500 | 0.0 | - |
| 8.0155 | 15550 | 0.0 | - |
| 8.0412 | 15600 | 0.0 | - |
| 8.0670 | 15650 | 0.0 | - |
| 8.0928 | 15700 | 0.0 | - |
| 8.1186 | 15750 | 0.0001 | - |
| 8.1443 | 15800 | 0.0001 | - |
| 8.1701 | 15850 | 0.0 | - |
| 8.1959 | 15900 | 0.0 | - |
| 8.2216 | 15950 | 0.0 | - |
| 8.2474 | 16000 | 0.0 | - |
| 8.2732 | 16050 | 0.0 | - |
| 8.2990 | 16100 | 0.0 | - |
| 8.3247 | 16150 | 0.0039 | - |
| 8.3505 | 16200 | 0.0003 | - |
| 8.3763 | 16250 | 0.0 | - |
| 8.4021 | 16300 | 0.0 | - |
| 8.4278 | 16350 | 0.0 | - |
| 8.4536 | 16400 | 0.0001 | - |
| 8.4794 | 16450 | 0.0 | - |
| 8.5052 | 16500 | 0.0 | - |
| 8.5309 | 16550 | 0.0 | - |
| 8.5567 | 16600 | 0.0 | - |
| 8.5825 | 16650 | 0.0 | - |
| 8.6082 | 16700 | 0.0 | - |
| 8.6340 | 16750 | 0.0003 | - |
| 8.6598 | 16800 | 0.0 | - |
| 8.6856 | 16850 | 0.0 | - |
| 8.7113 | 16900 | 0.0019 | - |
| 8.7371 | 16950 | 0.0 | - |
| 8.7629 | 17000 | 0.0 | - |
| 8.7887 | 17050 | 0.0 | - |
| 8.8144 | 17100 | 0.0 | - |
| 8.8402 | 17150 | 0.0 | - |
| 8.8660 | 17200 | 0.0 | - |
| 8.8918 | 17250 | 0.0 | - |
| 8.9175 | 17300 | 0.0 | - |
| 8.9433 | 17350 | 0.0 | - |
| 8.9691 | 17400 | 0.0 | - |
| 8.9948 | 17450 | 0.0 | - |
| 9.0206 | 17500 | 0.0 | - |
| 9.0464 | 17550 | 0.0 | - |
| 9.0722 | 17600 | 0.0 | - |
| 9.0979 | 17650 | 0.0 | - |
| 9.1237 | 17700 | 0.0 | - |
| 9.1495 | 17750 | 0.0 | - |
| 9.1753 | 17800 | 0.0 | - |
| 9.2010 | 17850 | 0.0 | - |
| 9.2268 | 17900 | 0.0 | - |
| 9.2526 | 17950 | 0.0 | - |
| 9.2784 | 18000 | 0.0004 | - |
| 9.3041 | 18050 | 0.0 | - |
| 9.3299 | 18100 | 0.0013 | - |
| 9.3557 | 18150 | 0.0 | - |
| 9.3814 | 18200 | 0.0 | - |
| 9.4072 | 18250 | 0.0001 | - |
| 9.4330 | 18300 | 0.0 | - |
| 9.4588 | 18350 | 0.0 | - |
| 9.4845 | 18400 | 0.0 | - |
| 9.5103 | 18450 | 0.0 | - |
| 9.5361 | 18500 | 0.0 | - |
| 9.5619 | 18550 | 0.0 | - |
| 9.5876 | 18600 | 0.0 | - |
| 9.6134 | 18650 | 0.0 | - |
| 9.6392 | 18700 | 0.0 | - |
| 9.6649 | 18750 | 0.0 | - |
| 9.6907 | 18800 | 0.0 | - |
| 9.7165 | 18850 | 0.0 | - |
| 9.7423 | 18900 | 0.0 | - |
| 9.7680 | 18950 | 0.0002 | - |
| 9.7938 | 19000 | 0.0002 | - |
| 9.8196 | 19050 | 0.0 | - |
| 9.8454 | 19100 | 0.0 | - |
| 9.8711 | 19150 | 0.0 | - |
| 9.8969 | 19200 | 0.0 | - |
| 9.9227 | 19250 | 0.0 | - |
| 9.9485 | 19300 | 0.0003 | - |
| 9.9742 | 19350 | 0.0 | - |
| 10.0 | 19400 | 0.0 | - |
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| 20.0 | 38800 | 0.0 | - |
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}
}