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NTIS
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kepri-embedding

Text Classification
setfit
PyTorch
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
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SetFit

This is a SetFit model that can be used for Text Classification. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

  1. Fine-tuning a Sentence Transformer with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

  • Model Type: SetFit
  • Classification head: a LogisticRegression instance
  • Maximum Sequence Length: 512 tokens
  • Number of Classes: 5 classes

Model Sources

Model Labels

Label Examples
오탈자 탐지
  • '건축 프로젝트 설명 문장에서 오타나 잘못된 맞춤법을 찾아줘.'
  • '경영 보고서 내용에 대한 오탈자를 검토하고 수정해 드릴 수 있을까요?'
  • '경쟁사 분석 항목 내 문장 구성의 오류를 지적해주겠습니까?'
요약
  • '(특정 논문 제목)의 결론 및 향후 연구 방향에 대해 요점을 정리해 주세요.'
  • '(특정 특허번호)를 기반으로 한 발명의 전체적인 개념을 짧게 설명 부탁드립니다.'
  • '1장의 데이터 수집 기술에 대해 요약해주세요'
유사문서
  • '5G 통신 모듈 최적화에 관련된 프로젝트를 하고 있는데, 비슷한 내용의 프로젝트나 논문이 있는지 연결해서 말해줄래?'
  • 'AI 기반 헬스케어 솔루션 개발에 관한 문헌 조사를 하고 있습니다. 와 같은 주제를 다룬 문서를 찾아줄 수 있을까요?'
  • 'AI 연산 속도를 최적화하기 위한 반도체 설계 방식을 연구하고 있어. 관련된 유사한 논문이나 보고서를 찾고 싶어'
중복성 검토
  • '5G 통신망을 기반으로 스마트 시티 구축에 관한 연구를 시작했어. 이와 동일하거나 겹치는 연구 과제나 프로젝트가 있는지 알아봐주고, 이유도 명확하게 밝혀줘'
  • '건물의 내진 설계 강화 방안을 조사하고 있는데 이에 연관된 기존 프로젝트가 무엇이 있는지 그리고 왜 겹치는지 말해줄래?'
  • '고성능 메모리 소자의 내구성을 향상시키는 기술을 개발하고 있어. 이와 비슷한 과제가 이전에 있었는지, 그리고 어떻게 유사하거나 중복되는지 말해줘'
특화 지식정보 제공
  • '3D 금속 배선 기술(HBM, TSV)의 도입으로 인한 전력 소비 감소 방안에는 어떤 것이 있는가요?'
  • 'AI 워크로드를 처리하기 위한 반도체 아키텍처 설계에서는 어떤 전략들이 사용되나요?'
  • 'LEED 인증의 기준과 획득 과정에 대해 알고 싶습니다.'

Evaluation

Metrics

Label Accuracy
all 0.9891

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("NTIS/kepri-embedding")
# Run inference
preds = model("연구 자료의 서론 부분을 한 줄로 요약해 줄 수 있나요?")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 6 12.4709 27
Label Training Sample Count
요약 105
중복성 검토 78
특화 지식정보 제공 106
유사문서 115
오탈자 탐지 95

Training Hyperparameters

  • batch_size: (64, 64)
  • num_epochs: (10, 10)
  • max_steps: -1
  • sampling_strategy: oversampling
  • 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
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: True

Training Results

Epoch Step Training Loss Validation Loss
0.0003 1 0.2062 -
0.0161 50 0.2314 -
0.0322 100 0.2008 -
0.0484 150 0.1395 -
0.0645 200 0.11 -
0.0806 250 0.0872 -
0.0967 300 0.0462 -
0.1129 350 0.0188 -
0.1290 400 0.0201 -
0.1451 450 0.025 -
0.1612 500 0.004 -
0.1774 550 0.002 -
0.1935 600 0.0153 -
0.2096 650 0.0011 -
0.2257 700 0.0007 -
0.2419 750 0.0006 -
0.2580 800 0.0006 -
0.2741 850 0.0005 -
0.2902 900 0.0004 -
0.3064 950 0.0005 -
0.3225 1000 0.0002 -
0.3386 1050 0.0002 -
0.3547 1100 0.0003 -
0.3708 1150 0.0002 -
0.3870 1200 0.0002 -
0.4031 1250 0.0002 -
0.4192 1300 0.0001 -
0.4353 1350 0.0002 -
0.4515 1400 0.0001 -
0.4676 1450 0.0001 -
0.4837 1500 0.0001 -
0.4998 1550 0.0001 -
0.5160 1600 0.0001 -
0.5321 1650 0.0001 -
0.5482 1700 0.0001 -
0.5643 1750 0.0001 -
0.5805 1800 0.0001 -
0.5966 1850 0.0001 -
0.6127 1900 0.0001 -
0.6288 1950 0.0001 -
0.6450 2000 0.0001 -
0.6611 2050 0.0001 -
0.6772 2100 0.0001 -
0.6933 2150 0.0001 -
0.7094 2200 0.0001 -
0.7256 2250 0.0001 -
0.7417 2300 0.0001 -
0.7578 2350 0.0001 -
0.7739 2400 0.0001 -
0.7901 2450 0.0001 -
0.8062 2500 0.0001 -
0.8223 2550 0.0001 -
0.8384 2600 0.0 -
0.8546 2650 0.0 -
0.8707 2700 0.0 -
0.8868 2750 0.0001 -
0.9029 2800 0.0 -
0.9191 2850 0.0001 -
0.9352 2900 0.0 -
0.9513 2950 0.0 -
0.9674 3000 0.0 -
0.9836 3050 0.0 -
0.9997 3100 0.0 -
1.0 3101 - 0.0247
1.0158 3150 0.0 -
1.0319 3200 0.0 -
1.0480 3250 0.0 -
1.0642 3300 0.0001 -
1.0803 3350 0.0 -
1.0964 3400 0.0 -
1.1125 3450 0.0 -
1.1287 3500 0.0 -
1.1448 3550 0.0 -
1.1609 3600 0.0 -
1.1770 3650 0.0 -
1.1932 3700 0.0 -
1.2093 3750 0.0 -
1.2254 3800 0.0 -
1.2415 3850 0.0 -
1.2577 3900 0.0 -
1.2738 3950 0.0 -
1.2899 4000 0.0 -
1.3060 4050 0.0 -
1.3222 4100 0.0 -
1.3383 4150 0.0 -
1.3544 4200 0.0 -
1.3705 4250 0.0 -
1.3866 4300 0.0 -
1.4028 4350 0.0 -
1.4189 4400 0.0 -
1.4350 4450 0.0 -
1.4511 4500 0.0 -
1.4673 4550 0.0 -
1.4834 4600 0.0 -
1.4995 4650 0.0 -
1.5156 4700 0.0 -
1.5318 4750 0.0 -
1.5479 4800 0.0 -
1.5640 4850 0.0 -
1.5801 4900 0.0 -
1.5963 4950 0.0 -
1.6124 5000 0.0 -
1.6285 5050 0.0 -
1.6446 5100 0.0 -
1.6608 5150 0.0 -
1.6769 5200 0.0 -
1.6930 5250 0.0 -
1.7091 5300 0.0 -
1.7252 5350 0.0 -
1.7414 5400 0.0 -
1.7575 5450 0.0 -
1.7736 5500 0.0 -
1.7897 5550 0.0 -
1.8059 5600 0.0 -
1.8220 5650 0.0 -
1.8381 5700 0.0 -
1.8542 5750 0.0 -
1.8704 5800 0.0 -
1.8865 5850 0.0 -
1.9026 5900 0.0 -
1.9187 5950 0.0 -
1.9349 6000 0.0 -
1.9510 6050 0.0 -
1.9671 6100 0.0 -
1.9832 6150 0.0 -
1.9994 6200 0.0 -
2.0 6202 - 0.0262
2.0155 6250 0.0 -
2.0316 6300 0.0 -
2.0477 6350 0.0 -
2.0639 6400 0.0 -
2.0800 6450 0.0 -
2.0961 6500 0.0 -
2.1122 6550 0.0 -
2.1283 6600 0.0 -
2.1445 6650 0.0 -
2.1606 6700 0.0 -
2.1767 6750 0.0 -
2.1928 6800 0.0 -
2.2090 6850 0.0 -
2.2251 6900 0.0 -
2.2412 6950 0.0 -
2.2573 7000 0.0 -
2.2735 7050 0.0 -
2.2896 7100 0.0 -
2.3057 7150 0.0 -
2.3218 7200 0.0 -
2.3380 7250 0.0 -
2.3541 7300 0.0 -
2.3702 7350 0.0 -
2.3863 7400 0.0 -
2.4025 7450 0.0 -
2.4186 7500 0.0 -
2.4347 7550 0.0 -
2.4508 7600 0.0 -
2.4669 7650 0.0 -
2.4831 7700 0.0 -
2.4992 7750 0.0 -
2.5153 7800 0.0 -
2.5314 7850 0.0 -
2.5476 7900 0.0 -
2.5637 7950 0.0 -
2.5798 8000 0.0 -
2.5959 8050 0.0 -
2.6121 8100 0.0 -
2.6282 8150 0.0 -
2.6443 8200 0.0 -
2.6604 8250 0.0 -
2.6766 8300 0.0 -
2.6927 8350 0.0 -
2.7088 8400 0.0 -
2.7249 8450 0.0 -
2.7411 8500 0.0 -
2.7572 8550 0.0 -
2.7733 8600 0.0 -
2.7894 8650 0.0 -
2.8055 8700 0.0 -
2.8217 8750 0.0 -
2.8378 8800 0.0 -
2.8539 8850 0.0 -
2.8700 8900 0.0 -
2.8862 8950 0.0 -
2.9023 9000 0.0 -
2.9184 9050 0.0 -
2.9345 9100 0.0 -
2.9507 9150 0.0 -
2.9668 9200 0.0 -
2.9829 9250 0.0 -
2.9990 9300 0.0 -
3.0 9303 - 0.025
3.0152 9350 0.0 -
3.0313 9400 0.0 -
3.0474 9450 0.0 -
3.0635 9500 0.0 -
3.0797 9550 0.0 -
3.0958 9600 0.0 -
3.1119 9650 0.0 -
3.1280 9700 0.0 -
3.1441 9750 0.0 -
3.1603 9800 0.0 -
3.1764 9850 0.0 -
3.1925 9900 0.0 -
3.2086 9950 0.0 -
3.2248 10000 0.0 -
3.2409 10050 0.0 -
3.2570 10100 0.0 -
3.2731 10150 0.0 -
3.2893 10200 0.0 -
3.3054 10250 0.0 -
3.3215 10300 0.0 -
3.3376 10350 0.0 -
3.3538 10400 0.0 -
3.3699 10450 0.0 -
3.3860 10500 0.0 -
3.4021 10550 0.0 -
3.4183 10600 0.0 -
3.4344 10650 0.0 -
3.4505 10700 0.0 -
3.4666 10750 0.0083 -
3.4827 10800 0.0019 -
3.4989 10850 0.0001 -
3.5150 10900 0.0 -
3.5311 10950 0.001 -
3.5472 11000 0.0 -
3.5634 11050 0.0 -
3.5795 11100 0.0 -
3.5956 11150 0.0 -
3.6117 11200 0.0 -
3.6279 11250 0.0 -
3.6440 11300 0.0 -
3.6601 11350 0.0 -
3.6762 11400 0.0 -
3.6924 11450 0.0 -
3.7085 11500 0.0 -
3.7246 11550 0.0 -
3.7407 11600 0.0 -
3.7569 11650 0.0 -
3.7730 11700 0.0 -
3.7891 11750 0.0 -
3.8052 11800 0.0 -
3.8213 11850 0.0 -
3.8375 11900 0.0 -
3.8536 11950 0.0 -
3.8697 12000 0.0 -
3.8858 12050 0.0 -
3.9020 12100 0.0 -
3.9181 12150 0.0 -
3.9342 12200 0.0 -
3.9503 12250 0.0 -
3.9665 12300 0.0 -
3.9826 12350 0.0 -
3.9987 12400 0.0 -
4.0 12404 - 0.0253
4.0148 12450 0.0 -
4.0310 12500 0.0 -
4.0471 12550 0.0 -
4.0632 12600 0.0 -
4.0793 12650 0.0 -
4.0955 12700 0.0 -
4.1116 12750 0.0 -
4.1277 12800 0.0 -
4.1438 12850 0.0 -
4.1599 12900 0.0 -
4.1761 12950 0.0 -
4.1922 13000 0.0 -
4.2083 13050 0.0 -
4.2244 13100 0.0 -
4.2406 13150 0.0 -
4.2567 13200 0.0 -
4.2728 13250 0.0 -
4.2889 13300 0.0 -
4.3051 13350 0.0 -
4.3212 13400 0.0 -
4.3373 13450 0.0 -
4.3534 13500 0.0 -
4.3696 13550 0.0 -
4.3857 13600 0.0 -
4.4018 13650 0.0 -
4.4179 13700 0.0 -
4.4341 13750 0.0 -
4.4502 13800 0.0 -
4.4663 13850 0.0 -
4.4824 13900 0.0 -
4.4985 13950 0.0 -
4.5147 14000 0.0 -
4.5308 14050 0.0 -
4.5469 14100 0.0 -
4.5630 14150 0.0 -
4.5792 14200 0.0 -
4.5953 14250 0.0 -
4.6114 14300 0.0 -
4.6275 14350 0.0 -
4.6437 14400 0.0 -
4.6598 14450 0.0 -
4.6759 14500 0.0 -
4.6920 14550 0.0 -
4.7082 14600 0.0 -
4.7243 14650 0.0 -
4.7404 14700 0.0 -
4.7565 14750 0.0 -
4.7727 14800 0.0 -
4.7888 14850 0.0 -
4.8049 14900 0.0 -
4.8210 14950 0.0 -
4.8371 15000 0.0 -
4.8533 15050 0.0 -
4.8694 15100 0.0 -
4.8855 15150 0.0 -
4.9016 15200 0.0 -
4.9178 15250 0.0 -
4.9339 15300 0.0 -
4.9500 15350 0.0 -
4.9661 15400 0.0 -
4.9823 15450 0.0 -
4.9984 15500 0.0 -
5.0 15505 - 0.0259
5.0145 15550 0.0 -
5.0306 15600 0.0 -
5.0468 15650 0.0 -
5.0629 15700 0.0 -
5.0790 15750 0.0 -
5.0951 15800 0.0 -
5.1113 15850 0.0 -
5.1274 15900 0.0 -
5.1435 15950 0.0 -
5.1596 16000 0.0 -
5.1757 16050 0.0 -
5.1919 16100 0.0 -
5.2080 16150 0.0 -
5.2241 16200 0.0 -
5.2402 16250 0.0 -
5.2564 16300 0.0 -
5.2725 16350 0.0 -
5.2886 16400 0.0 -
5.3047 16450 0.0 -
5.3209 16500 0.0 -
5.3370 16550 0.0 -
5.3531 16600 0.0 -
5.3692 16650 0.0 -
5.3854 16700 0.0 -
5.4015 16750 0.0 -
5.4176 16800 0.0 -
5.4337 16850 0.0 -
5.4499 16900 0.0 -
5.4660 16950 0.0 -
5.4821 17000 0.0 -
5.4982 17050 0.0 -
5.5144 17100 0.0 -
5.5305 17150 0.0 -
5.5466 17200 0.0 -
5.5627 17250 0.0 -
5.5788 17300 0.0 -
5.5950 17350 0.0 -
5.6111 17400 0.0 -
5.6272 17450 0.0 -
5.6433 17500 0.0 -
5.6595 17550 0.0 -
5.6756 17600 0.0 -
5.6917 17650 0.0 -
5.7078 17700 0.0 -
5.7240 17750 0.0 -
5.7401 17800 0.0 -
5.7562 17850 0.0 -
5.7723 17900 0.0 -
5.7885 17950 0.0 -
5.8046 18000 0.0 -
5.8207 18050 0.0 -
5.8368 18100 0.0 -
5.8530 18150 0.0 -
5.8691 18200 0.0 -
5.8852 18250 0.0 -
5.9013 18300 0.0 -
5.9174 18350 0.0 -
5.9336 18400 0.0 -
5.9497 18450 0.0 -
5.9658 18500 0.0 -
5.9819 18550 0.0 -
5.9981 18600 0.0 -
6.0 18606 - 0.0255
6.0142 18650 0.0 -
6.0303 18700 0.0 -
6.0464 18750 0.0 -
6.0626 18800 0.0 -
6.0787 18850 0.0 -
6.0948 18900 0.0 -
6.1109 18950 0.0 -
6.1271 19000 0.0 -
6.1432 19050 0.0 -
6.1593 19100 0.0 -
6.1754 19150 0.0 -
6.1916 19200 0.0 -
6.2077 19250 0.0 -
6.2238 19300 0.0 -
6.2399 19350 0.0 -
6.2560 19400 0.0 -
6.2722 19450 0.0 -
6.2883 19500 0.0 -
6.3044 19550 0.0 -
6.3205 19600 0.0 -
6.3367 19650 0.0 -
6.3528 19700 0.0 -
6.3689 19750 0.0 -
6.3850 19800 0.0 -
6.4012 19850 0.0 -
6.4173 19900 0.0 -
6.4334 19950 0.0 -
6.4495 20000 0.0 -
6.4657 20050 0.0 -
6.4818 20100 0.0 -
6.4979 20150 0.0 -
6.5140 20200 0.0 -
6.5302 20250 0.0 -
6.5463 20300 0.0 -
6.5624 20350 0.0 -
6.5785 20400 0.0 -
6.5946 20450 0.0 -
6.6108 20500 0.0 -
6.6269 20550 0.0 -
6.6430 20600 0.0 -
6.6591 20650 0.0 -
6.6753 20700 0.0 -
6.6914 20750 0.0 -
6.7075 20800 0.0 -
6.7236 20850 0.0 -
6.7398 20900 0.0 -
6.7559 20950 0.0 -
6.7720 21000 0.0 -
6.7881 21050 0.0 -
6.8043 21100 0.0 -
6.8204 21150 0.0 -
6.8365 21200 0.0 -
6.8526 21250 0.0 -
6.8688 21300 0.0 -
6.8849 21350 0.0 -
6.9010 21400 0.0 -
6.9171 21450 0.0 -
6.9332 21500 0.0 -
6.9494 21550 0.0 -
6.9655 21600 0.0 -
6.9816 21650 0.0 -
6.9977 21700 0.0 -
7.0 21707 - 0.0264
7.0139 21750 0.0 -
7.0300 21800 0.0 -
7.0461 21850 0.0 -
7.0622 21900 0.0 -
7.0784 21950 0.0 -
7.0945 22000 0.0 -
7.1106 22050 0.0 -
7.1267 22100 0.0 -
7.1429 22150 0.0 -
7.1590 22200 0.0 -
7.1751 22250 0.0 -
7.1912 22300 0.0 -
7.2074 22350 0.0 -
7.2235 22400 0.0 -
7.2396 22450 0.0 -
7.2557 22500 0.0 -
7.2718 22550 0.0 -
7.2880 22600 0.0 -
7.3041 22650 0.0 -
7.3202 22700 0.0 -
7.3363 22750 0.0 -
7.3525 22800 0.0 -
7.3686 22850 0.0 -
7.3847 22900 0.0 -
7.4008 22950 0.0 -
7.4170 23000 0.0 -
7.4331 23050 0.0 -
7.4492 23100 0.0 -
7.4653 23150 0.0 -
7.4815 23200 0.0 -
7.4976 23250 0.0 -
7.5137 23300 0.0 -
7.5298 23350 0.0 -
7.5460 23400 0.0 -
7.5621 23450 0.0 -
7.5782 23500 0.0 -
7.5943 23550 0.0 -
7.6104 23600 0.0 -
7.6266 23650 0.0 -
7.6427 23700 0.0 -
7.6588 23750 0.0 -
7.6749 23800 0.0 -
7.6911 23850 0.0 -
7.7072 23900 0.0 -
7.7233 23950 0.0 -
7.7394 24000 0.0 -
7.7556 24050 0.0 -
7.7717 24100 0.0 -
7.7878 24150 0.0 -
7.8039 24200 0.0 -
7.8201 24250 0.0 -
7.8362 24300 0.0 -
7.8523 24350 0.0 -
7.8684 24400 0.0 -
7.8846 24450 0.0 -
7.9007 24500 0.0 -
7.9168 24550 0.0 -
7.9329 24600 0.0 -
7.9490 24650 0.0 -
7.9652 24700 0.0 -
7.9813 24750 0.0 -
7.9974 24800 0.0 -
8.0 24808 - 0.0252
8.0135 24850 0.0 -
8.0297 24900 0.0 -
8.0458 24950 0.0 -
8.0619 25000 0.0 -
8.0780 25050 0.0 -
8.0942 25100 0.0 -
8.1103 25150 0.0 -
8.1264 25200 0.0 -
8.1425 25250 0.0 -
8.1587 25300 0.0 -
8.1748 25350 0.0 -
8.1909 25400 0.0 -
8.2070 25450 0.0 -
8.2232 25500 0.0 -
8.2393 25550 0.0 -
8.2554 25600 0.0 -
8.2715 25650 0.0 -
8.2876 25700 0.0 -
8.3038 25750 0.0 -
8.3199 25800 0.0 -
8.3360 25850 0.0 -
8.3521 25900 0.0 -
8.3683 25950 0.0 -
8.3844 26000 0.0 -
8.4005 26050 0.0 -
8.4166 26100 0.0 -
8.4328 26150 0.0 -
8.4489 26200 0.0 -
8.4650 26250 0.0 -
8.4811 26300 0.0 -
8.4973 26350 0.0 -
8.5134 26400 0.0 -
8.5295 26450 0.0 -
8.5456 26500 0.0 -
8.5618 26550 0.0 -
8.5779 26600 0.0 -
8.5940 26650 0.0 -
8.6101 26700 0.0 -
8.6262 26750 0.0 -
8.6424 26800 0.0 -
8.6585 26850 0.0 -
8.6746 26900 0.0 -
8.6907 26950 0.0 -
8.7069 27000 0.0 -
8.7230 27050 0.0 -
8.7391 27100 0.0 -
8.7552 27150 0.0 -
8.7714 27200 0.0 -
8.7875 27250 0.0 -
8.8036 27300 0.0 -
8.8197 27350 0.0 -
8.8359 27400 0.0 -
8.8520 27450 0.0 -
8.8681 27500 0.0 -
8.8842 27550 0.0 -
8.9004 27600 0.0 -
8.9165 27650 0.0 -
8.9326 27700 0.0 -
8.9487 27750 0.0 -
8.9649 27800 0.0 -
8.9810 27850 0.0 -
8.9971 27900 0.0 -
9.0 27909 - 0.0255
9.0132 27950 0.0 -
9.0293 28000 0.0 -
9.0455 28050 0.0 -
9.0616 28100 0.0 -
9.0777 28150 0.0 -
9.0938 28200 0.0 -
9.1100 28250 0.0 -
9.1261 28300 0.0 -
9.1422 28350 0.0 -
9.1583 28400 0.0 -
9.1745 28450 0.0 -
9.1906 28500 0.0 -
9.2067 28550 0.0 -
9.2228 28600 0.0 -
9.2390 28650 0.0 -
9.2551 28700 0.0 -
9.2712 28750 0.0 -
9.2873 28800 0.0 -
9.3035 28850 0.0 -
9.3196 28900 0.0 -
9.3357 28950 0.0 -
9.3518 29000 0.0 -
9.3679 29050 0.0 -
9.3841 29100 0.0 -
9.4002 29150 0.0 -
9.4163 29200 0.0 -
9.4324 29250 0.0 -
9.4486 29300 0.0 -
9.4647 29350 0.0 -
9.4808 29400 0.0 -
9.4969 29450 0.0 -
9.5131 29500 0.0 -
9.5292 29550 0.0 -
9.5453 29600 0.0 -
9.5614 29650 0.0 -
9.5776 29700 0.0 -
9.5937 29750 0.0 -
9.6098 29800 0.0 -
9.6259 29850 0.0 -
9.6421 29900 0.0 -
9.6582 29950 0.0 -
9.6743 30000 0.0 -
9.6904 30050 0.0 -
9.7065 30100 0.0 -
9.7227 30150 0.0 -
9.7388 30200 0.0 -
9.7549 30250 0.0 -
9.7710 30300 0.0 -
9.7872 30350 0.0 -
9.8033 30400 0.0 -
9.8194 30450 0.0 -
9.8355 30500 0.0 -
9.8517 30550 0.0 -
9.8678 30600 0.0 -
9.8839 30650 0.0 -
9.9000 30700 0.0 -
9.9162 30750 0.0 -
9.9323 30800 0.0 -
9.9484 30850 0.0 -
9.9645 30900 0.0 -
9.9807 30950 0.0 -
9.9968 31000 0.0 -
10.0 31010 - 0.0264
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.9.18
  • SetFit: 1.0.3
  • Sentence Transformers: 2.2.1
  • Transformers: 4.32.1
  • PyTorch: 1.10.0
  • Datasets: 2.20.0
  • Tokenizers: 0.13.3

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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