--- 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: ๐Ÿฏ๊ตญ์‚ฐ์Œ€ ์กฐ์ฒญ๐Ÿฏ ์ˆ˜์ œ์˜ค๋ž€๋‹ค ์„ ๋ฌผ์„ธํŠธ ๋‹ต๋ก€ํ’ˆ 18P 9์›” 3์ผ ์ถœ๊ณ (4์ผ~5์ผ ๋„์ฐฉ์˜ˆ์ •)_๊ฒฌ๊ณผ์ŠคํŽ˜์…œ(์˜ค๋ฆฌ์ง€๋„8p+์ŠคํŽ˜์…œ10p)_๊ฐ์‚ฌ์˜ ๋งˆ์Œ์„ ์ „ํ•ฉ๋‹ˆ๋‹ค ์ƒํ”„๋ฃป(st.fruit) - text: ํฌ์นด๋ฆฌ์Šค์›จํŠธ 245ml 1๊ฐœ ์ค‘ํŒจํŠธ_๋ฝ€๋กœ๋กœ(๋ฐ€ํฌ๋ง›) 235ML X 24๋ณ‘ ์ฃผ์‹ํšŒ์‚ฌ ์†ก๋ฏผ - text: ํฌ์นด๋ฆฌ์Šค์›จํŠธ 245ml 1๊ฐœ ๋ฏธ๋‹ˆ์บ”_ํ‹ฐ์˜คํ”ผ ์Šค์œ„ํŠธ ์•„๋ฉ”๋ฆฌ์นด๋…ธ 200ml 30๊ฐœ ๋Œ€์˜์ƒ์‚ฌ - text: ์‰์ดํฌ๋ฒ ์ด๋น„ ๋‹ค์ด์–ดํŠธ ๋‹จ๋ฐฑ์งˆ ์‰์ดํฌ ๋ง›์žˆ๋Š” ์‹์‚ฌ๋Œ€์šฉ ์‹๋‹จ ์Œ์‹ ๋”ธ๊ธฐ๋ง› 750g 3+ํด๋”ฉ๋ฐ•์Šค ๊ตฌ์„ฑ_์ดˆ์ฝ”+์Šค์œ—์ฝ˜+๋งˆ์‹œ๋ฉœ๋กœ์ดˆ์ฝ”_ํด๋”ฉ๋ฐ•์Šค+ํ•‘ํฌ๋ณดํ‹€ 1๊ฐœ+ํ™”์ดํŠธ๋ณดํ‹€ 1๊ฐœ ์ฃผ์‹ํšŒ์‚ฌ ์Šฌ๋กœ์šฐ๋กœ์ผ“ - text: ํฌ๋‰ด ์ดˆ์ž„๊ณ„ ์•Œํ‹ฐ์ง€ XV ์˜ค๋ฉ”๊ฐ€3 ๋ฏธ๋‹ˆ ์นด์ œ๋กœํ…œ ํฌ๋‰ด ์ดˆ์ž„๊ณ„ ์‹๋ฌผ์„ฑ ์•Œํ‹ฐ์ง€ ์˜ค๋ฉ”๊ฐ€3โ–ณ (์ฃผ)ํฌํ„ฐ๋ธ”๋‰ดํŠธ๋ฆฌ์…˜ 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: 0.9180474602529828 name: Metric --- # SetFit with klue/roberta-base This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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](https://www.sbert.net) with contrastive learning. 2. 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](https://huggingface.co/klue/roberta-base) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 22 classes ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 9.0 | | | 0.0 | | | 2.0 | | | 12.0 | | | 6.0 | | | 18.0 | | | 4.0 | | | 21.0 | | | 17.0 | | | 20.0 | | | 15.0 | | | 14.0 | | | 10.0 | | | 16.0 | | | 1.0 | | | 13.0 | | | 3.0 | | | 8.0 | | | 11.0 | | | 7.0 | | | 5.0 | | | 19.0 | | ## Evaluation ### Metrics | Label | Metric | |:--------|:-------| | **all** | 0.9180 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the ๐Ÿค— Hub model = SetFitModel.from_pretrained("mini1013/master_item_fd") # Run inference preds = model("ํฌ์นด๋ฆฌ์Šค์›จํŠธ 245ml 1๊ฐœ ์ค‘ํŒจํŠธ_๋ฝ€๋กœ๋กœ(๋ฐ€ํฌ๋ง›) 235ML X 24๋ณ‘ ์ฃผ์‹ํšŒ์‚ฌ ์†ก๋ฏผ") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 3 | 9.1979 | 30 | | Label | Training Sample Count | |:------|:----------------------| | 0.0 | 448 | | 1.0 | 579 | | 2.0 | 800 | | 3.0 | 552 | | 4.0 | 1049 | | 5.0 | 350 | | 6.0 | 800 | | 7.0 | 100 | | 8.0 | 400 | | 9.0 | 414 | | 10.0 | 581 | | 11.0 | 275 | | 12.0 | 450 | | 13.0 | 300 | | 14.0 | 600 | | 15.0 | 422 | | 16.0 | 400 | | 17.0 | 200 | | 18.0 | 571 | | 19.0 | 50 | | 20.0 | 346 | | 21.0 | 450 | ### 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 | |:-------:|:-----:|:-------------:|:---------------:| | 0.0006 | 1 | 0.4086 | - | | 0.0316 | 50 | 0.3967 | - | | 0.0631 | 100 | 0.3705 | - | | 0.0947 | 150 | 0.3541 | - | | 0.1263 | 200 | 0.2971 | - | | 0.1578 | 250 | 0.2651 | - | | 0.1894 | 300 | 0.2404 | - | | 0.2210 | 350 | 0.1946 | - | | 0.2525 | 400 | 0.1848 | - | | 0.2841 | 450 | 0.1706 | - | | 0.3157 | 500 | 0.1394 | - | | 0.3472 | 550 | 0.1364 | - | | 0.3788 | 600 | 0.1178 | - | | 0.4104 | 650 | 0.0926 | - | | 0.4419 | 700 | 0.0949 | - | | 0.4735 | 750 | 0.0732 | - | | 0.5051 | 800 | 0.0806 | - | | 0.5366 | 850 | 0.0648 | - | | 0.5682 | 900 | 0.0707 | - | | 0.5997 | 950 | 0.0523 | - | | 0.6313 | 1000 | 0.0529 | - | | 0.6629 | 1050 | 0.0491 | - | | 0.6944 | 1100 | 0.0486 | - | | 0.7260 | 1150 | 0.0369 | - | | 0.7576 | 1200 | 0.0296 | - | | 0.7891 | 1250 | 0.0303 | - | | 0.8207 | 1300 | 0.0232 | - | | 0.8523 | 1350 | 0.0281 | - | | 0.8838 | 1400 | 0.0178 | - | | 0.9154 | 1450 | 0.0346 | - | | 0.9470 | 1500 | 0.025 | - | | 0.9785 | 1550 | 0.0218 | - | | 1.0101 | 1600 | 0.0335 | - | | 1.0417 | 1650 | 0.0206 | - | | 1.0732 | 1700 | 0.0168 | - | | 1.1048 | 1750 | 0.0294 | - | | 1.1364 | 1800 | 0.0219 | - | | 1.1679 | 1850 | 0.0176 | - | | 1.1995 | 1900 | 0.0196 | - | | 1.2311 | 1950 | 0.0141 | - | | 1.2626 | 2000 | 0.0031 | - | | 1.2942 | 2050 | 0.0131 | - | | 1.3258 | 2100 | 0.0158 | - | | 1.3573 | 2150 | 0.0121 | - | | 1.3889 | 2200 | 0.0088 | - | | 1.4205 | 2250 | 0.0047 | - | | 1.4520 | 2300 | 0.0138 | - | | 1.4836 | 2350 | 0.0029 | - | | 1.5152 | 2400 | 0.0063 | - | | 1.5467 | 2450 | 0.0042 | - | | 1.5783 | 2500 | 0.0015 | - | | 1.6098 | 2550 | 0.0078 | - | | 1.6414 | 2600 | 0.0014 | - | | 1.6730 | 2650 | 0.0055 | - | | 1.7045 | 2700 | 0.0011 | - | | 1.7361 | 2750 | 0.0052 | - | | 1.7677 | 2800 | 0.0018 | - | | 1.7992 | 2850 | 0.003 | - | | 1.8308 | 2900 | 0.004 | - | | 1.8624 | 2950 | 0.0006 | - | | 1.8939 | 3000 | 0.0058 | - | | 1.9255 | 3050 | 0.0004 | - | | 1.9571 | 3100 | 0.0028 | - | | 1.9886 | 3150 | 0.0005 | - | | 2.0202 | 3200 | 0.0006 | - | | 2.0518 | 3250 | 0.0016 | - | | 2.0833 | 3300 | 0.0036 | - | | 2.1149 | 3350 | 0.0009 | - | | 2.1465 | 3400 | 0.001 | - | | 2.1780 | 3450 | 0.0007 | - | | 2.2096 | 3500 | 0.0003 | - | | 2.2412 | 3550 | 0.0003 | - | | 2.2727 | 3600 | 0.0004 | - | | 2.3043 | 3650 | 0.0002 | - | | 2.3359 | 3700 | 0.0002 | - | | 2.3674 | 3750 | 0.0002 | - | | 2.3990 | 3800 | 0.003 | - | | 2.4306 | 3850 | 0.0004 | - | | 2.4621 | 3900 | 0.0013 | - | | 2.4937 | 3950 | 0.0003 | - | | 2.5253 | 4000 | 0.0002 | - | | 2.5568 | 4050 | 0.0001 | - | | 2.5884 | 4100 | 0.0002 | - | | 2.6199 | 4150 | 0.0001 | - | | 2.6515 | 4200 | 0.0003 | - | | 2.6831 | 4250 | 0.0003 | - | | 2.7146 | 4300 | 0.0002 | - | | 2.7462 | 4350 | 0.0001 | - | | 2.7778 | 4400 | 0.0018 | - | | 2.8093 | 4450 | 0.0005 | - | | 2.8409 | 4500 | 0.0001 | - | | 2.8725 | 4550 | 0.0003 | - | | 2.9040 | 4600 | 0.0001 | - | | 2.9356 | 4650 | 0.0002 | - | | 2.9672 | 4700 | 0.0002 | - | | 2.9987 | 4750 | 0.0002 | - | | 3.0303 | 4800 | 0.0018 | - | | 3.0619 | 4850 | 0.0001 | - | | 3.0934 | 4900 | 0.0002 | - | | 3.125 | 4950 | 0.0001 | - | | 3.1566 | 5000 | 0.0002 | - | | 3.1881 | 5050 | 0.0004 | - | | 3.2197 | 5100 | 0.0001 | - | | 3.2513 | 5150 | 0.0001 | - | | 3.2828 | 5200 | 0.0002 | - | | 3.3144 | 5250 | 0.0003 | - | | 3.3460 | 5300 | 0.0001 | - | | 3.3775 | 5350 | 0.0003 | - | | 3.4091 | 5400 | 0.0001 | - | | 3.4407 | 5450 | 0.0001 | - | | 3.4722 | 5500 | 0.0001 | - | | 3.5038 | 5550 | 0.0003 | - | | 3.5354 | 5600 | 0.0002 | - | | 3.5669 | 5650 | 0.0001 | - | | 3.5985 | 5700 | 0.0005 | - | | 3.6301 | 5750 | 0.0003 | - | | 3.6616 | 5800 | 0.0001 | - | | 3.6932 | 5850 | 0.0003 | - | | 3.7247 | 5900 | 0.0001 | - | | 3.7563 | 5950 | 0.0001 | - | | 3.7879 | 6000 | 0.0001 | - | | 3.8194 | 6050 | 0.0006 | - | | 3.8510 | 6100 | 0.0002 | - | | 3.8826 | 6150 | 0.0004 | - | | 3.9141 | 6200 | 0.0001 | - | | 3.9457 | 6250 | 0.0001 | - | | 3.9773 | 6300 | 0.0001 | - | | 4.0088 | 6350 | 0.0002 | - | | 4.0404 | 6400 | 0.0001 | - | | 4.0720 | 6450 | 0.0 | - | | 4.1035 | 6500 | 0.0001 | - | | 4.1351 | 6550 | 0.0001 | - | | 4.1667 | 6600 | 0.0 | - | | 4.1982 | 6650 | 0.0 | - | | 4.2298 | 6700 | 0.0 | - | | 4.2614 | 6750 | 0.0 | - | | 4.2929 | 6800 | 0.0001 | - | | 4.3245 | 6850 | 0.0 | - | | 4.3561 | 6900 | 0.0 | - | | 4.3876 | 6950 | 0.0002 | - | | 4.4192 | 7000 | 0.0007 | - | | 4.4508 | 7050 | 0.0018 | - | | 4.4823 | 7100 | 0.0001 | - | | 4.5139 | 7150 | 0.0001 | - | | 4.5455 | 7200 | 0.0001 | - | | 4.5770 | 7250 | 0.0003 | - | | 4.6086 | 7300 | 0.0001 | - | | 4.6402 | 7350 | 0.0008 | - | | 4.6717 | 7400 | 0.0001 | - | | 4.7033 | 7450 | 0.0 | - | | 4.7348 | 7500 | 0.0001 | - | | 4.7664 | 7550 | 0.0001 | - | | 4.7980 | 7600 | 0.0 | - | | 4.8295 | 7650 | 0.0 | - | | 4.8611 | 7700 | 0.0 | - | | 4.8927 | 7750 | 0.0019 | - | | 4.9242 | 7800 | 0.0 | - | | 4.9558 | 7850 | 0.0 | - | | 4.9874 | 7900 | 0.001 | - | | 5.0189 | 7950 | 0.0 | - | | 5.0505 | 8000 | 0.0011 | - | | 5.0821 | 8050 | 0.0002 | - | | 5.1136 | 8100 | 0.0004 | - | | 5.1452 | 8150 | 0.0 | - | | 5.1768 | 8200 | 0.0018 | - | | 5.2083 | 8250 | 0.0001 | - | | 5.2399 | 8300 | 0.0 | - | | 5.2715 | 8350 | 0.0018 | - | | 5.3030 | 8400 | 0.0 | - | | 5.3346 | 8450 | 0.0005 | - | | 5.3662 | 8500 | 0.0001 | - | | 5.3977 | 8550 | 0.0 | - | | 5.4293 | 8600 | 0.0 | - | | 5.4609 | 8650 | 0.0001 | - | | 5.4924 | 8700 | 0.0 | - | | 5.5240 | 8750 | 0.0001 | - | | 5.5556 | 8800 | 0.0 | - | | 5.5871 | 8850 | 0.0001 | - | | 5.6187 | 8900 | 0.0001 | - | | 5.6503 | 8950 | 0.0 | - | | 5.6818 | 9000 | 0.0001 | - | | 5.7134 | 9050 | 0.0008 | - | | 5.7449 | 9100 | 0.0001 | - | | 5.7765 | 9150 | 0.0 | - | | 5.8081 | 9200 | 0.0008 | - | | 5.8396 | 9250 | 0.0001 | - | | 5.8712 | 9300 | 0.0 | - | | 5.9028 | 9350 | 0.0001 | - | | 5.9343 | 9400 | 0.0 | - | | 5.9659 | 9450 | 0.0 | - | | 5.9975 | 9500 | 0.0001 | - | | 6.0290 | 9550 | 0.0 | - | | 6.0606 | 9600 | 0.0 | - | | 6.0922 | 9650 | 0.0 | - | | 6.1237 | 9700 | 0.0 | - | | 6.1553 | 9750 | 0.0 | - | | 6.1869 | 9800 | 0.0 | - | | 6.2184 | 9850 | 0.0 | - | | 6.25 | 9900 | 0.0 | - | | 6.2816 | 9950 | 0.0 | - | | 6.3131 | 10000 | 0.0 | - | | 6.3447 | 10050 | 0.0 | - | | 6.3763 | 10100 | 0.0 | - | | 6.4078 | 10150 | 0.0 | - | | 6.4394 | 10200 | 0.0 | - | | 6.4710 | 10250 | 0.0 | - | | 6.5025 | 10300 | 0.0001 | - | | 6.5341 | 10350 | 0.0 | - | | 6.5657 | 10400 | 0.0001 | - | | 6.5972 | 10450 | 0.0 | - | | 6.6288 | 10500 | 0.0 | - | | 6.6604 | 10550 | 0.0 | - | | 6.6919 | 10600 | 0.0 | - | | 6.7235 | 10650 | 0.0 | - | | 6.7551 | 10700 | 0.0 | - | | 6.7866 | 10750 | 0.0 | - | | 6.8182 | 10800 | 0.0 | - | | 6.8497 | 10850 | 0.0 | - | | 6.8813 | 10900 | 0.0002 | - | | 6.9129 | 10950 | 0.0016 | - | | 6.9444 | 11000 | 0.0 | - | | 6.9760 | 11050 | 0.0002 | - | | 7.0076 | 11100 | 0.0 | - | | 7.0391 | 11150 | 0.0006 | - | | 7.0707 | 11200 | 0.0 | - | | 7.1023 | 11250 | 0.0 | - | | 7.1338 | 11300 | 0.0 | - | | 7.1654 | 11350 | 0.0 | - | | 7.1970 | 11400 | 0.0 | - | | 7.2285 | 11450 | 0.0 | - | | 7.2601 | 11500 | 0.0 | - | | 7.2917 | 11550 | 0.0 | - | | 7.3232 | 11600 | 0.0 | - | | 7.3548 | 11650 | 0.0 | - | | 7.3864 | 11700 | 0.0 | - | | 7.4179 | 11750 | 0.0 | - | | 7.4495 | 11800 | 0.0 | - | | 7.4811 | 11850 | 0.0 | - | | 7.5126 | 11900 | 0.0 | - | | 7.5442 | 11950 | 0.0 | - | | 7.5758 | 12000 | 0.0 | - | | 7.6073 | 12050 | 0.0 | - | | 7.6389 | 12100 | 0.0 | - | | 7.6705 | 12150 | 0.0 | - | | 7.7020 | 12200 | 0.0 | - | | 7.7336 | 12250 | 0.0 | - | | 7.7652 | 12300 | 0.0 | - | | 7.7967 | 12350 | 0.0003 | - | | 7.8283 | 12400 | 0.0001 | - | | 7.8598 | 12450 | 0.0 | - | | 7.8914 | 12500 | 0.0 | - | | 7.9230 | 12550 | 0.0 | - | | 7.9545 | 12600 | 0.0 | - | | 7.9861 | 12650 | 0.0 | - | | 8.0177 | 12700 | 0.0001 | - | | 8.0492 | 12750 | 0.0 | - | | 8.0808 | 12800 | 0.0 | - | | 8.1124 | 12850 | 0.0 | - | | 8.1439 | 12900 | 0.0 | - | | 8.1755 | 12950 | 0.0 | - | | 8.2071 | 13000 | 0.0 | - | | 8.2386 | 13050 | 0.0 | - | | 8.2702 | 13100 | 0.0 | - | | 8.3018 | 13150 | 0.0 | - | | 8.3333 | 13200 | 0.0 | - | | 8.3649 | 13250 | 0.0 | - | | 8.3965 | 13300 | 0.0 | - | | 8.4280 | 13350 | 0.0 | - | | 8.4596 | 13400 | 0.0 | - | | 8.4912 | 13450 | 0.0 | - | | 8.5227 | 13500 | 0.0 | - | | 8.5543 | 13550 | 0.0 | - | | 8.5859 | 13600 | 0.0 | - | | 8.6174 | 13650 | 0.0 | - | | 8.6490 | 13700 | 0.0021 | - | | 8.6806 | 13750 | 0.0006 | - | | 8.7121 | 13800 | 0.0002 | - | | 8.7437 | 13850 | 0.0013 | - | | 8.7753 | 13900 | 0.0 | - | | 8.8068 | 13950 | 0.0 | - | | 8.8384 | 14000 | 0.0 | - | | 8.8699 | 14050 | 0.0 | - | | 8.9015 | 14100 | 0.0 | - | | 8.9331 | 14150 | 0.0 | - | | 8.9646 | 14200 | 0.0 | - | | 8.9962 | 14250 | 0.0 | - | | 9.0278 | 14300 | 0.0 | - | | 9.0593 | 14350 | 0.0 | - | | 9.0909 | 14400 | 0.0 | - | | 9.1225 | 14450 | 0.0 | - | | 9.1540 | 14500 | 0.0 | - | | 9.1856 | 14550 | 0.0002 | - | | 9.2172 | 14600 | 0.0 | - | | 9.2487 | 14650 | 0.0 | - | | 9.2803 | 14700 | 0.0 | - | | 9.3119 | 14750 | 0.0 | - | | 9.3434 | 14800 | 0.0 | - | | 9.375 | 14850 | 0.0 | - | | 9.4066 | 14900 | 0.0 | - | | 9.4381 | 14950 | 0.0 | - | | 9.4697 | 15000 | 0.0 | - | | 9.5013 | 15050 | 0.0 | - | | 9.5328 | 15100 | 0.0 | - | | 9.5644 | 15150 | 0.0 | - | | 9.5960 | 15200 | 0.0 | - | | 9.6275 | 15250 | 0.0 | - | | 9.6591 | 15300 | 0.0 | - | | 9.6907 | 15350 | 0.0 | - | | 9.7222 | 15400 | 0.0002 | - | | 9.7538 | 15450 | 0.0 | - | | 9.7854 | 15500 | 0.0 | - | | 9.8169 | 15550 | 0.0 | - | | 9.8485 | 15600 | 0.0 | - | | 9.8801 | 15650 | 0.0 | - | | 9.9116 | 15700 | 0.0001 | - | | 9.9432 | 15750 | 0.0 | - | | 9.9747 | 15800 | 0.0003 | - | | 10.0063 | 15850 | 0.0 | - | | 10.0379 | 15900 | 0.0 | - | | 10.0694 | 15950 | 0.0001 | - | | 10.1010 | 16000 | 0.0 | - | | 10.1326 | 16050 | 0.0 | - | | 10.1641 | 16100 | 0.0 | - | | 10.1957 | 16150 | 0.0 | - | | 10.2273 | 16200 | 0.0 | - | | 10.2588 | 16250 | 0.0 | - | | 10.2904 | 16300 | 0.0 | - | | 10.3220 | 16350 | 0.0 | - | | 10.3535 | 16400 | 0.0008 | - | | 10.3851 | 16450 | 0.0 | - | | 10.4167 | 16500 | 0.0 | - | | 10.4482 | 16550 | 0.0 | - | | 10.4798 | 16600 | 0.0 | - | | 10.5114 | 16650 | 0.0 | - | | 10.5429 | 16700 | 0.0 | - | | 10.5745 | 16750 | 0.0 | - | | 10.6061 | 16800 | 0.0 | - | | 10.6376 | 16850 | 0.0 | - | | 10.6692 | 16900 | 0.0009 | - | | 10.7008 | 16950 | 0.0 | - | | 10.7323 | 17000 | 0.0 | - | | 10.7639 | 17050 | 0.0 | - | | 10.7955 | 17100 | 0.0 | - | | 10.8270 | 17150 | 0.0 | - | | 10.8586 | 17200 | 0.0 | - | | 10.8902 | 17250 | 0.0 | - | | 10.9217 | 17300 | 0.0 | - | | 10.9533 | 17350 | 0.0 | - | | 10.9848 | 17400 | 0.0 | - | | 11.0164 | 17450 | 0.0001 | - | | 11.0480 | 17500 | 0.0 | - | | 11.0795 | 17550 | 0.0 | - | | 11.1111 | 17600 | 0.0 | - | | 11.1427 | 17650 | 0.0 | - | | 11.1742 | 17700 | 0.0 | - | | 11.2058 | 17750 | 0.0 | - | | 11.2374 | 17800 | 0.0 | - | | 11.2689 | 17850 | 0.0 | - | | 11.3005 | 17900 | 0.0 | - | | 11.3321 | 17950 | 0.0 | - | | 11.3636 | 18000 | 0.0 | - | | 11.3952 | 18050 | 0.0 | - | | 11.4268 | 18100 | 0.0 | - | | 11.4583 | 18150 | 0.0 | - | | 11.4899 | 18200 | 0.0003 | - | | 11.5215 | 18250 | 0.0 | - | | 11.5530 | 18300 | 0.0005 | - | | 11.5846 | 18350 | 0.0 | - | | 11.6162 | 18400 | 0.0 | - | | 11.6477 | 18450 | 0.0 | - | | 11.6793 | 18500 | 0.0 | - | | 11.7109 | 18550 | 0.0 | - | | 11.7424 | 18600 | 0.0 | - | | 11.7740 | 18650 | 0.0 | - | | 11.8056 | 18700 | 0.0 | - | | 11.8371 | 18750 | 0.0 | - | | 11.8687 | 18800 | 0.0 | - | | 11.9003 | 18850 | 0.0 | - | | 11.9318 | 18900 | 0.0 | - | | 11.9634 | 18950 | 0.0 | - | | 11.9949 | 19000 | 0.0 | - | | 12.0265 | 19050 | 0.0 | - | | 12.0581 | 19100 | 0.0 | - | | 12.0896 | 19150 | 0.0 | - | | 12.1212 | 19200 | 0.0 | - | | 12.1528 | 19250 | 0.0 | - | | 12.1843 | 19300 | 0.0 | - | | 12.2159 | 19350 | 0.0 | - | | 12.2475 | 19400 | 0.0 | - | | 12.2790 | 19450 | 0.0 | - | | 12.3106 | 19500 | 0.0 | - | | 12.3422 | 19550 | 0.0 | - | | 12.3737 | 19600 | 0.0 | - | | 12.4053 | 19650 | 0.0 | - | | 12.4369 | 19700 | 0.0 | - | | 12.4684 | 19750 | 0.0 | - | | 12.5 | 19800 | 0.0 | - | | 12.5316 | 19850 | 0.0 | - | | 12.5631 | 19900 | 0.0 | - | | 12.5947 | 19950 | 0.0 | - | | 12.6263 | 20000 | 0.0 | - | | 12.6578 | 20050 | 0.0 | - | | 12.6894 | 20100 | 0.0 | - | | 12.7210 | 20150 | 0.0 | - | | 12.7525 | 20200 | 0.0 | - | | 12.7841 | 20250 | 0.0 | - | | 12.8157 | 20300 | 0.0 | - | | 12.8472 | 20350 | 0.0 | - | | 12.8788 | 20400 | 0.0 | - | | 12.9104 | 20450 | 0.0 | - | | 12.9419 | 20500 | 0.0 | - | | 12.9735 | 20550 | 0.0 | - | | 13.0051 | 20600 | 0.0 | - | | 13.0366 | 20650 | 0.0002 | - | | 13.0682 | 20700 | 0.0 | - | | 13.0997 | 20750 | 0.0 | - | | 13.1313 | 20800 | 0.0 | - | | 13.1629 | 20850 | 0.0 | - | | 13.1944 | 20900 | 0.0 | - | | 13.2260 | 20950 | 0.0 | - | | 13.2576 | 21000 | 0.0 | - | | 13.2891 | 21050 | 0.0015 | - | | 13.3207 | 21100 | 0.0 | - | | 13.3523 | 21150 | 0.0 | - | | 13.3838 | 21200 | 0.0 | - | | 13.4154 | 21250 | 0.0 | - | | 13.4470 | 21300 | 0.0 | - | | 13.4785 | 21350 | 0.0 | - | | 13.5101 | 21400 | 0.0 | - | | 13.5417 | 21450 | 0.0 | - | | 13.5732 | 21500 | 0.0 | - | | 13.6048 | 21550 | 0.0 | - | | 13.6364 | 21600 | 0.0 | - | | 13.6679 | 21650 | 0.0 | - | | 13.6995 | 21700 | 0.0 | - | | 13.7311 | 21750 | 0.0 | - | | 13.7626 | 21800 | 0.0 | - | | 13.7942 | 21850 | 0.0 | - | | 13.8258 | 21900 | 0.0 | - | | 13.8573 | 21950 | 0.0 | - | | 13.8889 | 22000 | 0.0 | - | | 13.9205 | 22050 | 0.0 | - | | 13.9520 | 22100 | 0.0 | - | | 13.9836 | 22150 | 0.0 | - | | 14.0152 | 22200 | 0.0 | - | | 14.0467 | 22250 | 0.0 | - | | 14.0783 | 22300 | 0.0 | - | | 14.1098 | 22350 | 0.0 | - | | 14.1414 | 22400 | 0.0 | - | | 14.1730 | 22450 | 0.0 | - | | 14.2045 | 22500 | 0.0002 | - | | 14.2361 | 22550 | 0.0 | - | | 14.2677 | 22600 | 0.002 | - | | 14.2992 | 22650 | 0.0 | - | | 14.3308 | 22700 | 0.0 | - | | 14.3624 | 22750 | 0.0 | - | | 14.3939 | 22800 | 0.0 | - | | 14.4255 | 22850 | 0.0 | - | | 14.4571 | 22900 | 0.0 | - | | 14.4886 | 22950 | 0.0 | - | | 14.5202 | 23000 | 0.0 | - | | 14.5518 | 23050 | 0.0 | - | | 14.5833 | 23100 | 0.0 | - | | 14.6149 | 23150 | 0.0 | - | | 14.6465 | 23200 | 0.0 | - | | 14.6780 | 23250 | 0.0 | - | | 14.7096 | 23300 | 0.0 | - | | 14.7412 | 23350 | 0.0 | - | | 14.7727 | 23400 | 0.0 | - | | 14.8043 | 23450 | 0.0 | - | | 14.8359 | 23500 | 0.0 | - | | 14.8674 | 23550 | 0.0 | - | | 14.8990 | 23600 | 0.0 | - | | 14.9306 | 23650 | 0.0 | - | | 14.9621 | 23700 | 0.0 | - | | 14.9937 | 23750 | 0.0 | - | | 15.0253 | 23800 | 0.0 | - | | 15.0568 | 23850 | 0.0 | - | | 15.0884 | 23900 | 0.0 | - | | 15.1199 | 23950 | 0.0 | - | | 15.1515 | 24000 | 0.0 | - | | 15.1831 | 24050 | 0.0 | - | | 15.2146 | 24100 | 0.0 | - | | 15.2462 | 24150 | 0.0 | - | | 15.2778 | 24200 | 0.0 | - | | 15.3093 | 24250 | 0.0 | - | | 15.3409 | 24300 | 0.0 | - | | 15.3725 | 24350 | 0.0 | - | | 15.4040 | 24400 | 0.0 | - | | 15.4356 | 24450 | 0.0 | - | | 15.4672 | 24500 | 0.0 | - | | 15.4987 | 24550 | 0.0 | - | | 15.5303 | 24600 | 0.0 | - | | 15.5619 | 24650 | 0.0 | - | | 15.5934 | 24700 | 0.0 | - | | 15.625 | 24750 | 0.0 | - | | 15.6566 | 24800 | 0.0 | - | | 15.6881 | 24850 | 0.0 | - | | 15.7197 | 24900 | 0.0 | - | | 15.7513 | 24950 | 0.0 | - | | 15.7828 | 25000 | 0.0 | - | | 15.8144 | 25050 | 0.0 | - | | 15.8460 | 25100 | 0.0 | - | | 15.8775 | 25150 | 0.0 | - | | 15.9091 | 25200 | 0.0 | - | | 15.9407 | 25250 | 0.0 | - | | 15.9722 | 25300 | 0.0 | - | | 16.0038 | 25350 | 0.0 | - | | 16.0354 | 25400 | 0.0 | - | | 16.0669 | 25450 | 0.0 | - | | 16.0985 | 25500 | 0.0 | - | | 16.1301 | 25550 | 0.0 | - | | 16.1616 | 25600 | 0.0 | - | | 16.1932 | 25650 | 0.0 | - | | 16.2247 | 25700 | 0.0 | - | | 16.2563 | 25750 | 0.0 | - | | 16.2879 | 25800 | 0.0 | - | | 16.3194 | 25850 | 0.0 | - | | 16.3510 | 25900 | 0.0 | - | | 16.3826 | 25950 | 0.0 | - | | 16.4141 | 26000 | 0.0 | - | | 16.4457 | 26050 | 0.0 | - | | 16.4773 | 26100 | 0.0 | - | | 16.5088 | 26150 | 0.0 | - | | 16.5404 | 26200 | 0.0 | - | | 16.5720 | 26250 | 0.0 | - | | 16.6035 | 26300 | 0.0 | - | | 16.6351 | 26350 | 0.0 | - | | 16.6667 | 26400 | 0.0 | - | | 16.6982 | 26450 | 0.0 | - | | 16.7298 | 26500 | 0.0 | - | | 16.7614 | 26550 | 0.0 | - | | 16.7929 | 26600 | 0.0 | - | | 16.8245 | 26650 | 0.0 | - | | 16.8561 | 26700 | 0.0 | - | | 16.8876 | 26750 | 0.0 | - | | 16.9192 | 26800 | 0.0 | - | | 16.9508 | 26850 | 0.0 | - | | 16.9823 | 26900 | 0.0 | - | | 17.0139 | 26950 | 0.0 | - | | 17.0455 | 27000 | 0.0 | - | | 17.0770 | 27050 | 0.0 | - | | 17.1086 | 27100 | 0.0 | - | | 17.1402 | 27150 | 0.0 | - | | 17.1717 | 27200 | 0.0 | - | | 17.2033 | 27250 | 0.0 | - | | 17.2348 | 27300 | 0.0 | - | | 17.2664 | 27350 | 0.0 | - | | 17.2980 | 27400 | 0.0 | - | | 17.3295 | 27450 | 0.0 | - | | 17.3611 | 27500 | 0.0 | - | | 17.3927 | 27550 | 0.0 | - | | 17.4242 | 27600 | 0.0 | - | | 17.4558 | 27650 | 0.0 | - | | 17.4874 | 27700 | 0.0 | - | | 17.5189 | 27750 | 0.0 | - | | 17.5505 | 27800 | 0.0 | - | | 17.5821 | 27850 | 0.0 | - | | 17.6136 | 27900 | 0.0 | - | | 17.6452 | 27950 | 0.0 | - | | 17.6768 | 28000 | 0.0 | - | | 17.7083 | 28050 | 0.0 | - | | 17.7399 | 28100 | 0.0 | - | | 17.7715 | 28150 | 0.0 | - | | 17.8030 | 28200 | 0.0 | - | | 17.8346 | 28250 | 0.0 | - | | 17.8662 | 28300 | 0.0 | - | | 17.8977 | 28350 | 0.0 | - | | 17.9293 | 28400 | 0.0 | - | | 17.9609 | 28450 | 0.0 | - | | 17.9924 | 28500 | 0.0 | - | | 18.0240 | 28550 | 0.0 | - | | 18.0556 | 28600 | 0.0 | - | | 18.0871 | 28650 | 0.0 | - | | 18.1187 | 28700 | 0.0 | - | | 18.1503 | 28750 | 0.0 | - | | 18.1818 | 28800 | 0.0 | - | | 18.2134 | 28850 | 0.0 | - | | 18.2449 | 28900 | 0.0 | - | | 18.2765 | 28950 | 0.0 | - | | 18.3081 | 29000 | 0.0 | - | | 18.3396 | 29050 | 0.0 | - | | 18.3712 | 29100 | 0.0 | - | | 18.4028 | 29150 | 0.0 | - | | 18.4343 | 29200 | 0.0 | - | | 18.4659 | 29250 | 0.0 | - | | 18.4975 | 29300 | 0.0 | - | | 18.5290 | 29350 | 0.0 | - | | 18.5606 | 29400 | 0.0 | - | | 18.5922 | 29450 | 0.0 | - | | 18.6237 | 29500 | 0.0 | - | | 18.6553 | 29550 | 0.0 | - | | 18.6869 | 29600 | 0.0 | - | | 18.7184 | 29650 | 0.0 | - | | 18.75 | 29700 | 0.0 | - | | 18.7816 | 29750 | 0.0 | - | | 18.8131 | 29800 | 0.0 | - | | 18.8447 | 29850 | 0.0 | - | | 18.8763 | 29900 | 0.0 | - | | 18.9078 | 29950 | 0.0 | - | | 18.9394 | 30000 | 0.0 | - | | 18.9710 | 30050 | 0.0 | - | | 19.0025 | 30100 | 0.0 | - | | 19.0341 | 30150 | 0.0 | - | | 19.0657 | 30200 | 0.0 | - | | 19.0972 | 30250 | 0.0 | - | | 19.1288 | 30300 | 0.0 | - | | 19.1604 | 30350 | 0.0 | - | | 19.1919 | 30400 | 0.0 | - | | 19.2235 | 30450 | 0.0 | - | | 19.2551 | 30500 | 0.0 | - | | 19.2866 | 30550 | 0.0 | - | | 19.3182 | 30600 | 0.0 | - | | 19.3497 | 30650 | 0.0 | - | | 19.3813 | 30700 | 0.0 | - | | 19.4129 | 30750 | 0.0 | - | | 19.4444 | 30800 | 0.0 | - | | 19.4760 | 30850 | 0.0 | - | | 19.5076 | 30900 | 0.0 | - | | 19.5391 | 30950 | 0.0 | - | | 19.5707 | 31000 | 0.0 | - | | 19.6023 | 31050 | 0.0 | - | | 19.6338 | 31100 | 0.0 | - | | 19.6654 | 31150 | 0.0 | - | | 19.6970 | 31200 | 0.0 | - | | 19.7285 | 31250 | 0.0 | - | | 19.7601 | 31300 | 0.0 | - | | 19.7917 | 31350 | 0.0 | - | | 19.8232 | 31400 | 0.0 | - | | 19.8548 | 31450 | 0.0 | - | | 19.8864 | 31500 | 0.0 | - | | 19.9179 | 31550 | 0.0 | - | | 19.9495 | 31600 | 0.0 | - | | 19.9811 | 31650 | 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 ```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} } ```