--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: - text: so the representative presentative that i talked to a nine o'clock this morning but it said that they were gonna call the customer service my service or whatever that they were gonna call to to see if they dispute it and i didn't get a call back bags i'm a mother of three kids i worked were two job i can't have a phone that that's not on and i don't have the money the money to pay a hundred and fifty six eighty six dollars you guys want me to pay right now it's restore my service - text: i understand kelly yes let me send you a little bit about be a moment okay is is that the pin number that you have the four days four digits that you have with us is - text: yeah 'cause that that's that's really that's ridiculous you know that's ridiculous for her to do that i like i have you know all the time in the world okay all right okay okay no i got my card no cathy she asked me for my last time and i did not have it i'm - text: ma'am uh thanks for holding for holding by the way everything upon checking i'm checking here a record ma'am aah for your for your current a hot spot you said you said you still have twenty gigabytes left - text: and then when i asked i didn't even get to speak to the supervisor provider i just got hung up on pipeline_tag: text-classification inference: true base_model: sentence-transformers/all-mpnet-base-v2 model-index: - name: SetFit with sentence-transformers/all-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.5463414634146342 name: Accuracy --- # SetFit with sentence-transformers/all-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 384 tokens - **Number of Classes:** 64 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 | |:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | | | 20 | | | 11 | | | 16 | | | 35 | | | 21 | | | 46 | | | 47 | | | 6 | | | 51 | | | 27 | | | 19 | | | 62 | | | 12 | | | 13 | | | 56 | | | 31 | | | 49 | | | 15 | | | 60 | | | 7 | | | 33 | | | 4 | | | 3 | | | 61 | | | 22 | | | 30 | | | 48 | | | 39 | | | 10 | | | 57 | | | 50 | | | 8 | | | 36 | | | 54 | | | 63 | | | 43 | | | 55 | | | 5 | | | 14 | | | 2 | | | 58 | | | 37 | | | 23 | | | 52 | | | 28 | | | 59 | | | 53 | | | 45 | | | 41 | | | 17 | | | 34 | | | 25 | | | 24 | | | 9 | | | 1 | | | 29 | | | 32 | | | 18 | | | 44 | | | 26 | | | 42 | | | 40 | | | 38 | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.5463 | ## 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 🤗 Hub model = SetFitModel.from_pretrained("Jalajkx/all_mpnetcric-setfit-model") # Run inference preds = model("and then when i asked i didn't even get to speak to the supervisor provider i just got hung up on") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 1 | 32.4224 | 283 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 36 | | 1 | 36 | | 2 | 36 | | 3 | 36 | | 4 | 36 | | 5 | 36 | | 6 | 36 | | 7 | 36 | | 8 | 36 | | 9 | 6 | | 10 | 36 | | 11 | 36 | | 12 | 36 | | 13 | 9 | | 14 | 36 | | 15 | 36 | | 16 | 17 | | 17 | 36 | | 18 | 4 | | 19 | 29 | | 20 | 30 | | 21 | 36 | | 22 | 25 | | 23 | 36 | | 24 | 36 | | 25 | 36 | | 26 | 4 | | 27 | 36 | | 28 | 36 | | 29 | 4 | | 30 | 8 | | 31 | 36 | | 32 | 4 | | 33 | 36 | | 34 | 11 | | 35 | 36 | | 36 | 36 | | 37 | 36 | | 38 | 10 | | 39 | 13 | | 40 | 2 | | 41 | 36 | | 42 | 9 | | 43 | 36 | | 44 | 10 | | 45 | 36 | | 46 | 36 | | 47 | 14 | | 48 | 36 | | 49 | 36 | | 50 | 36 | | 51 | 36 | | 52 | 36 | | 53 | 36 | | 54 | 36 | | 55 | 36 | | 56 | 36 | | 57 | 36 | | 58 | 36 | | 59 | 8 | | 60 | 36 | | 61 | 36 | | 62 | 36 | | 63 | 36 | ### Training Hyperparameters - batch_size: (4, 4) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 25 - 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 | |:------:|:-----:|:-------------:|:---------------:| | 0.0000 | 1 | 0.2196 | - | | 0.0022 | 50 | 0.2183 | - | | 0.0044 | 100 | 0.3574 | - | | 0.0065 | 150 | 0.1756 | - | | 0.0087 | 200 | 0.1396 | - | | 0.0109 | 250 | 0.2875 | - | | 0.0131 | 300 | 0.1307 | - | | 0.0152 | 350 | 0.1465 | - | | 0.0174 | 400 | 0.1503 | - | | 0.0196 | 450 | 0.1579 | - | | 0.0218 | 500 | 0.3216 | - | | 0.0240 | 550 | 0.2399 | - | | 0.0261 | 600 | 0.2824 | - | | 0.0283 | 650 | 0.1217 | - | | 0.0305 | 700 | 0.0647 | - | | 0.0327 | 750 | 0.2651 | - | | 0.0348 | 800 | 0.1792 | - | | 0.0370 | 850 | 0.1461 | - | | 0.0392 | 900 | 0.0256 | - | | 0.0414 | 950 | 0.1175 | - | | 0.0435 | 1000 | 0.2394 | - | | 0.0457 | 1050 | 0.1582 | - | | 0.0479 | 1100 | 0.2785 | - | | 0.0501 | 1150 | 0.0611 | - | | 0.0523 | 1200 | 0.1937 | - | | 0.0544 | 1250 | 0.0804 | - | | 0.0566 | 1300 | 0.0811 | - | | 0.0588 | 1350 | 0.0663 | - | | 0.0610 | 1400 | 0.2148 | - | | 0.0631 | 1450 | 0.0428 | - | | 0.0653 | 1500 | 0.0083 | - | | 0.0675 | 1550 | 0.0884 | - | | 0.0697 | 1600 | 0.1341 | - | | 0.0719 | 1650 | 0.0949 | - | | 0.0740 | 1700 | 0.1839 | - | | 0.0762 | 1750 | 0.2244 | - | | 0.0784 | 1800 | 0.0309 | - | | 0.0806 | 1850 | 0.0277 | - | | 0.0827 | 1900 | 0.2016 | - | | 0.0849 | 1950 | 0.1174 | - | | 0.0871 | 2000 | 0.0942 | - | | 0.0893 | 2050 | 0.0483 | - | | 0.0915 | 2100 | 0.2057 | - | | 0.0936 | 2150 | 0.0151 | - | | 0.0958 | 2200 | 0.023 | - | | 0.0980 | 2250 | 0.0514 | - | | 0.1002 | 2300 | 0.1541 | - | | 0.1023 | 2350 | 0.1426 | - | | 0.1045 | 2400 | 0.0187 | - | | 0.1067 | 2450 | 0.0386 | - | | 0.1089 | 2500 | 0.274 | - | | 0.1110 | 2550 | 0.0723 | - | | 0.1132 | 2600 | 0.0115 | - | | 0.1154 | 2650 | 0.053 | - | | 0.1176 | 2700 | 0.2371 | - | | 0.1198 | 2750 | 0.2472 | - | | 0.1219 | 2800 | 0.0386 | - | | 0.1241 | 2850 | 0.0159 | - | | 0.1263 | 2900 | 0.0276 | - | | 0.1285 | 2950 | 0.1229 | - | | 0.1306 | 3000 | 0.0037 | - | | 0.1328 | 3050 | 0.0029 | - | | 0.1350 | 3100 | 0.0037 | - | | 0.1372 | 3150 | 0.022 | - | | 0.1394 | 3200 | 0.0389 | - | | 0.1415 | 3250 | 0.0146 | - | | 0.1437 | 3300 | 0.0034 | - | | 0.1459 | 3350 | 0.0721 | - | | 0.1481 | 3400 | 0.0462 | - | | 0.1502 | 3450 | 0.0039 | - | | 0.1524 | 3500 | 0.1225 | - | | 0.1546 | 3550 | 0.0009 | - | | 0.1568 | 3600 | 0.1005 | - | | 0.1590 | 3650 | 0.008 | - | | 0.1611 | 3700 | 0.121 | - | | 0.1633 | 3750 | 0.2982 | - | | 0.1655 | 3800 | 0.008 | - | | 0.1677 | 3850 | 0.001 | - | | 0.1698 | 3900 | 0.216 | - | | 0.1720 | 3950 | 0.0458 | - | | 0.1742 | 4000 | 0.0155 | - | | 0.1764 | 4050 | 0.1235 | - | | 0.1785 | 4100 | 0.0059 | - | | 0.1807 | 4150 | 0.2421 | - | | 0.1829 | 4200 | 0.2232 | - | | 0.1851 | 4250 | 0.0396 | - | | 0.1873 | 4300 | 0.2164 | - | | 0.1894 | 4350 | 0.0839 | - | | 0.1916 | 4400 | 0.0116 | - | | 0.1938 | 4450 | 0.2666 | - | | 0.1960 | 4500 | 0.0648 | - | | 0.1981 | 4550 | 0.074 | - | | 0.2003 | 4600 | 0.077 | - | | 0.2025 | 4650 | 0.0739 | - | | 0.2047 | 4700 | 0.0029 | - | | 0.2069 | 4750 | 0.0679 | - | | 0.2090 | 4800 | 0.0049 | - | | 0.2112 | 4850 | 0.0281 | - | | 0.2134 | 4900 | 0.049 | - | | 0.2156 | 4950 | 0.0052 | - | | 0.2177 | 5000 | 0.1657 | - | | 0.2199 | 5050 | 0.0005 | - | | 0.2221 | 5100 | 0.0041 | - | | 0.2243 | 5150 | 0.0008 | - | | 0.2265 | 5200 | 0.0587 | - | | 0.2286 | 5250 | 0.0753 | - | | 0.2308 | 5300 | 0.1744 | - | | 0.2330 | 5350 | 0.0055 | - | | 0.2352 | 5400 | 0.0023 | - | | 0.2373 | 5450 | 0.0002 | - | | 0.2395 | 5500 | 0.0472 | - | | 0.2417 | 5550 | 0.0042 | - | | 0.2439 | 5600 | 0.0137 | - | | 0.2460 | 5650 | 0.1646 | - | | 0.2482 | 5700 | 0.0509 | - | | 0.2504 | 5750 | 0.0062 | - | | 0.2526 | 5800 | 0.0019 | - | | 0.2548 | 5850 | 0.0048 | - | | 0.2569 | 5900 | 0.0031 | - | | 0.2591 | 5950 | 0.0011 | - | | 0.2613 | 6000 | 0.004 | - | | 0.2635 | 6050 | 0.0498 | - | | 0.2656 | 6100 | 0.0042 | - | | 0.2678 | 6150 | 0.0018 | - | | 0.2700 | 6200 | 0.0061 | - | | 0.2722 | 6250 | 0.1355 | - | | 0.2744 | 6300 | 0.0039 | - | | 0.2765 | 6350 | 0.0044 | - | | 0.2787 | 6400 | 0.001 | - | | 0.2809 | 6450 | 0.0011 | - | | 0.2831 | 6500 | 0.0302 | - | | 0.2852 | 6550 | 0.1502 | - | | 0.2874 | 6600 | 0.0029 | - | | 0.2896 | 6650 | 0.0016 | - | | 0.2918 | 6700 | 0.0232 | - | | 0.2940 | 6750 | 0.176 | - | | 0.2961 | 6800 | 0.0323 | - | | 0.2983 | 6850 | 0.0818 | - | | 0.3005 | 6900 | 0.0427 | - | | 0.3027 | 6950 | 0.1716 | - | | 0.3048 | 7000 | 0.0137 | - | | 0.3070 | 7050 | 0.0032 | - | | 0.3092 | 7100 | 0.0095 | - | | 0.3114 | 7150 | 0.177 | - | | 0.3135 | 7200 | 0.0005 | - | | 0.3157 | 7250 | 0.0157 | - | | 0.3179 | 7300 | 0.0012 | - | | 0.3201 | 7350 | 0.0027 | - | | 0.3223 | 7400 | 0.1351 | - | | 0.3244 | 7450 | 0.0019 | - | | 0.3266 | 7500 | 0.0009 | - | | 0.3288 | 7550 | 0.2017 | - | | 0.3310 | 7600 | 0.0059 | - | | 0.3331 | 7650 | 0.0013 | - | | 0.3353 | 7700 | 0.0377 | - | | 0.3375 | 7750 | 0.0056 | - | | 0.3397 | 7800 | 0.0055 | - | | 0.3419 | 7850 | 0.0745 | - | | 0.3440 | 7900 | 0.0046 | - | | 0.3462 | 7950 | 0.002 | - | | 0.3484 | 8000 | 0.0355 | - | | 0.3506 | 8050 | 0.0004 | - | | 0.3527 | 8100 | 0.0004 | - | | 0.3549 | 8150 | 0.0072 | - | | 0.3571 | 8200 | 0.0013 | - | | 0.3593 | 8250 | 0.0032 | - | | 0.3615 | 8300 | 0.0006 | - | | 0.3636 | 8350 | 0.0095 | - | | 0.3658 | 8400 | 0.0006 | - | | 0.3680 | 8450 | 0.0005 | - | | 0.3702 | 8500 | 0.0004 | - | | 0.3723 | 8550 | 0.0019 | - | | 0.3745 | 8600 | 0.0002 | - | | 0.3767 | 8650 | 0.0015 | - | | 0.3789 | 8700 | 0.0117 | - | | 0.3810 | 8750 | 0.002 | - | | 0.3832 | 8800 | 0.0005 | - | | 0.3854 | 8850 | 0.0009 | - | | 0.3876 | 8900 | 0.0041 | - | | 0.3898 | 8950 | 0.0484 | - | | 0.3919 | 9000 | 0.0058 | - | | 0.3941 | 9050 | 0.0027 | - | | 0.3963 | 9100 | 0.0002 | - | | 0.3985 | 9150 | 0.2323 | - | | 0.4006 | 9200 | 0.0163 | - | | 0.4028 | 9250 | 0.0333 | - | | 0.4050 | 9300 | 0.0033 | - | | 0.4072 | 9350 | 0.0023 | - | | 0.4094 | 9400 | 0.0044 | - | | 0.4115 | 9450 | 0.0142 | - | | 0.4137 | 9500 | 0.0261 | - | | 0.4159 | 9550 | 0.004 | - | | 0.4181 | 9600 | 0.027 | - | | 0.4202 | 9650 | 0.0104 | - | | 0.4224 | 9700 | 0.0005 | - | | 0.4246 | 9750 | 0.2452 | - | | 0.4268 | 9800 | 0.0069 | - | | 0.4290 | 9850 | 0.0245 | - | | 0.4311 | 9900 | 0.0005 | - | | 0.4333 | 9950 | 0.0041 | - | | 0.4355 | 10000 | 0.1058 | - | | 0.4377 | 10050 | 0.0009 | - | | 0.4398 | 10100 | 0.0067 | - | | 0.4420 | 10150 | 0.0832 | - | | 0.4442 | 10200 | 0.0016 | - | | 0.4464 | 10250 | 0.039 | - | | 0.4485 | 10300 | 0.0078 | - | | 0.4507 | 10350 | 0.0013 | - | | 0.4529 | 10400 | 0.0003 | - | | 0.4551 | 10450 | 0.0259 | - | | 0.4573 | 10500 | 0.008 | - | | 0.4594 | 10550 | 0.2137 | - | | 0.4616 | 10600 | 0.0083 | - | | 0.4638 | 10650 | 0.0206 | - | | 0.4660 | 10700 | 0.0039 | - | | 0.4681 | 10750 | 0.2205 | - | | 0.4703 | 10800 | 0.0072 | - | | 0.4725 | 10850 | 0.0436 | - | | 0.4747 | 10900 | 0.071 | - | | 0.4769 | 10950 | 0.0004 | - | | 0.4790 | 11000 | 0.0147 | - | | 0.4812 | 11050 | 0.0095 | - | | 0.4834 | 11100 | 0.0069 | - | | 0.4856 | 11150 | 0.0027 | - | | 0.4877 | 11200 | 0.0151 | - | | 0.4899 | 11250 | 0.0076 | - | | 0.4921 | 11300 | 0.0016 | - | | 0.4943 | 11350 | 0.1457 | - | | 0.4965 | 11400 | 0.1454 | - | | 0.4986 | 11450 | 0.0013 | - | | 0.5008 | 11500 | 0.0027 | - | | 0.5030 | 11550 | 0.0583 | - | | 0.5052 | 11600 | 0.0029 | - | | 0.5073 | 11650 | 0.0139 | - | | 0.5095 | 11700 | 0.0004 | - | | 0.5117 | 11750 | 0.0098 | - | | 0.5139 | 11800 | 0.0009 | - | | 0.5160 | 11850 | 0.0003 | - | | 0.5182 | 11900 | 0.0009 | - | | 0.5204 | 11950 | 0.0088 | - | | 0.5226 | 12000 | 0.0006 | - | | 0.5248 | 12050 | 0.0014 | - | | 0.5269 | 12100 | 0.0008 | - | | 0.5291 | 12150 | 0.0008 | - | | 0.5313 | 12200 | 0.0008 | - | | 0.5335 | 12250 | 0.0005 | - | | 0.5356 | 12300 | 0.0028 | - | | 0.5378 | 12350 | 0.0011 | - | | 0.5400 | 12400 | 0.0136 | - | | 0.5422 | 12450 | 0.0318 | - | | 0.5444 | 12500 | 0.0037 | - | | 0.5465 | 12550 | 0.0029 | - | | 0.5487 | 12600 | 0.0073 | - | | 0.5509 | 12650 | 0.0099 | - | | 0.5531 | 12700 | 0.015 | - | | 0.5552 | 12750 | 0.0047 | - | | 0.5574 | 12800 | 0.0891 | - | | 0.5596 | 12850 | 0.0007 | - | | 0.5618 | 12900 | 0.0784 | - | | 0.5640 | 12950 | 0.0636 | - | | 0.5661 | 13000 | 0.0029 | - | | 0.5683 | 13050 | 0.0048 | - | | 0.5705 | 13100 | 0.0698 | - | | 0.5727 | 13150 | 0.0002 | - | | 0.5748 | 13200 | 0.0734 | - | | 0.5770 | 13250 | 0.0004 | - | | 0.5792 | 13300 | 0.0135 | - | | 0.5814 | 13350 | 0.0034 | - | | 0.5835 | 13400 | 0.0018 | - | | 0.5857 | 13450 | 0.0175 | - | | 0.5879 | 13500 | 0.0003 | - | | 0.5901 | 13550 | 0.0002 | - | | 0.5923 | 13600 | 0.0032 | - | | 0.5944 | 13650 | 0.0007 | - | | 0.5966 | 13700 | 0.0021 | - | | 0.5988 | 13750 | 0.0019 | - | | 0.6010 | 13800 | 0.0006 | - | | 0.6031 | 13850 | 0.0014 | - | | 0.6053 | 13900 | 0.0011 | - | | 0.6075 | 13950 | 0.2383 | - | | 0.6097 | 14000 | 0.0009 | - | | 0.6119 | 14050 | 0.0863 | - | | 0.6140 | 14100 | 0.0005 | - | | 0.6162 | 14150 | 0.0017 | - | | 0.6184 | 14200 | 0.0003 | - | | 0.6206 | 14250 | 0.0025 | - | | 0.6227 | 14300 | 0.0008 | - | | 0.6249 | 14350 | 0.0005 | - | | 0.6271 | 14400 | 0.0006 | - | | 0.6293 | 14450 | 0.0517 | - | | 0.6315 | 14500 | 0.0005 | - | | 0.6336 | 14550 | 0.0075 | - | | 0.6358 | 14600 | 0.0004 | - | | 0.6380 | 14650 | 0.0003 | - | | 0.6402 | 14700 | 0.0003 | - | | 0.6423 | 14750 | 0.0045 | - | | 0.6445 | 14800 | 0.0005 | - | | 0.6467 | 14850 | 0.0002 | - | | 0.6489 | 14900 | 0.0125 | - | | 0.6510 | 14950 | 0.0015 | - | | 0.6532 | 15000 | 0.0017 | - | | 0.6554 | 15050 | 0.0011 | - | | 0.6576 | 15100 | 0.0207 | - | | 0.6598 | 15150 | 0.0002 | - | | 0.6619 | 15200 | 0.0252 | - | | 0.6641 | 15250 | 0.0006 | - | | 0.6663 | 15300 | 0.0015 | - | | 0.6685 | 15350 | 0.0018 | - | | 0.6706 | 15400 | 0.0386 | - | | 0.6728 | 15450 | 0.0011 | - | | 0.6750 | 15500 | 0.0003 | - | | 0.6772 | 15550 | 0.0007 | - | | 0.6794 | 15600 | 0.0028 | - | | 0.6815 | 15650 | 0.0056 | - | | 0.6837 | 15700 | 0.0005 | - | | 0.6859 | 15750 | 0.0002 | - | | 0.6881 | 15800 | 0.0305 | - | | 0.6902 | 15850 | 0.0005 | - | | 0.6924 | 15900 | 0.0018 | - | | 0.6946 | 15950 | 0.0011 | - | | 0.6968 | 16000 | 0.0006 | - | | 0.6990 | 16050 | 0.0072 | - | | 0.7011 | 16100 | 0.0224 | - | | 0.7033 | 16150 | 0.0011 | - | | 0.7055 | 16200 | 0.0005 | - | | 0.7077 | 16250 | 0.0007 | - | | 0.7098 | 16300 | 0.0005 | - | | 0.7120 | 16350 | 0.0028 | - | | 0.7142 | 16400 | 0.0017 | - | | 0.7164 | 16450 | 0.2294 | - | | 0.7185 | 16500 | 0.0253 | - | | 0.7207 | 16550 | 0.0122 | - | | 0.7229 | 16600 | 0.0001 | - | | 0.7251 | 16650 | 0.0327 | - | | 0.7273 | 16700 | 0.0042 | - | | 0.7294 | 16750 | 0.0008 | - | | 0.7316 | 16800 | 0.0004 | - | | 0.7338 | 16850 | 0.0003 | - | | 0.7360 | 16900 | 0.0005 | - | | 0.7381 | 16950 | 0.0003 | - | | 0.7403 | 17000 | 0.0021 | - | | 0.7425 | 17050 | 0.2041 | - | | 0.7447 | 17100 | 0.0002 | - | | 0.7469 | 17150 | 0.0006 | - | | 0.7490 | 17200 | 0.0002 | - | | 0.7512 | 17250 | 0.0008 | - | | 0.7534 | 17300 | 0.068 | - | | 0.7556 | 17350 | 0.0016 | - | | 0.7577 | 17400 | 0.0006 | - | | 0.7599 | 17450 | 0.0005 | - | | 0.7621 | 17500 | 0.0011 | - | | 0.7643 | 17550 | 0.2192 | - | | 0.7665 | 17600 | 0.0006 | - | | 0.7686 | 17650 | 0.0003 | - | | 0.7708 | 17700 | 0.0017 | - | | 0.7730 | 17750 | 0.0033 | - | | 0.7752 | 17800 | 0.0001 | - | | 0.7773 | 17850 | 0.0011 | - | | 0.7795 | 17900 | 0.0302 | - | | 0.7817 | 17950 | 0.0004 | - | | 0.7839 | 18000 | 0.2921 | - | | 0.7860 | 18050 | 0.0001 | - | | 0.7882 | 18100 | 0.006 | - | | 0.7904 | 18150 | 0.0164 | - | | 0.7926 | 18200 | 0.0003 | - | | 0.7948 | 18250 | 0.0021 | - | | 0.7969 | 18300 | 0.0094 | - | | 0.7991 | 18350 | 0.002 | - | | 0.8013 | 18400 | 0.0405 | - | | 0.8035 | 18450 | 0.001 | - | | 0.8056 | 18500 | 0.2594 | - | | 0.8078 | 18550 | 0.0075 | - | | 0.8100 | 18600 | 0.0003 | - | | 0.8122 | 18650 | 0.0009 | - | | 0.8144 | 18700 | 0.0018 | - | | 0.8165 | 18750 | 0.0007 | - | | 0.8187 | 18800 | 0.0006 | - | | 0.8209 | 18850 | 0.0009 | - | | 0.8231 | 18900 | 0.0003 | - | | 0.8252 | 18950 | 0.0006 | - | | 0.8274 | 19000 | 0.0002 | - | | 0.8296 | 19050 | 0.0004 | - | | 0.8318 | 19100 | 0.0018 | - | | 0.8340 | 19150 | 0.0007 | - | | 0.8361 | 19200 | 0.0005 | - | | 0.8383 | 19250 | 0.0206 | - | | 0.8405 | 19300 | 0.0005 | - | | 0.8427 | 19350 | 0.1918 | - | | 0.8448 | 19400 | 0.0093 | - | | 0.8470 | 19450 | 0.0032 | - | | 0.8492 | 19500 | 0.0004 | - | | 0.8514 | 19550 | 0.1727 | - | | 0.8535 | 19600 | 0.2034 | - | | 0.8557 | 19650 | 0.0007 | - | | 0.8579 | 19700 | 0.0004 | - | | 0.8601 | 19750 | 0.0001 | - | | 0.8623 | 19800 | 0.0024 | - | | 0.8644 | 19850 | 0.0122 | - | | 0.8666 | 19900 | 0.0003 | - | | 0.8688 | 19950 | 0.0093 | - | | 0.8710 | 20000 | 0.0003 | - | | 0.8731 | 20050 | 0.0007 | - | | 0.8753 | 20100 | 0.0044 | - | | 0.8775 | 20150 | 0.0006 | - | | 0.8797 | 20200 | 0.0002 | - | | 0.8819 | 20250 | 0.0003 | - | | 0.8840 | 20300 | 0.0024 | - | | 0.8862 | 20350 | 0.0051 | - | | 0.8884 | 20400 | 0.0767 | - | | 0.8906 | 20450 | 0.0004 | - | | 0.8927 | 20500 | 0.0002 | - | | 0.8949 | 20550 | 0.0007 | - | | 0.8971 | 20600 | 0.0012 | - | | 0.8993 | 20650 | 0.0004 | - | | 0.9015 | 20700 | 0.0003 | - | | 0.9036 | 20750 | 0.0002 | - | | 0.9058 | 20800 | 0.0005 | - | | 0.9080 | 20850 | 0.0007 | - | | 0.9102 | 20900 | 0.0006 | - | | 0.9123 | 20950 | 0.2469 | - | | 0.9145 | 21000 | 0.0002 | - | | 0.9167 | 21050 | 0.0009 | - | | 0.9189 | 21100 | 0.002 | - | | 0.9210 | 21150 | 0.0027 | - | | 0.9232 | 21200 | 0.0007 | - | | 0.9254 | 21250 | 0.0008 | - | | 0.9276 | 21300 | 0.0265 | - | | 0.9298 | 21350 | 0.0019 | - | | 0.9319 | 21400 | 0.0003 | - | | 0.9341 | 21450 | 0.0064 | - | | 0.9363 | 21500 | 0.0003 | - | | 0.9385 | 21550 | 0.0015 | - | | 0.9406 | 21600 | 0.0002 | - | | 0.9428 | 21650 | 0.0015 | - | | 0.9450 | 21700 | 0.1497 | - | | 0.9472 | 21750 | 0.1422 | - | | 0.9494 | 21800 | 0.0001 | - | | 0.9515 | 21850 | 0.0007 | - | | 0.9537 | 21900 | 0.0053 | - | | 0.9559 | 21950 | 0.0002 | - | | 0.9581 | 22000 | 0.0003 | - | | 0.9602 | 22050 | 0.1234 | - | | 0.9624 | 22100 | 0.2087 | - | | 0.9646 | 22150 | 0.0005 | - | | 0.9668 | 22200 | 0.0001 | - | | 0.9690 | 22250 | 0.0003 | - | | 0.9711 | 22300 | 0.0004 | - | | 0.9733 | 22350 | 0.0014 | - | | 0.9755 | 22400 | 0.0021 | - | | 0.9777 | 22450 | 0.0105 | - | | 0.9798 | 22500 | 0.0009 | - | | 0.9820 | 22550 | 0.0003 | - | | 0.9842 | 22600 | 0.0006 | - | | 0.9864 | 22650 | 0.0007 | - | | 0.9885 | 22700 | 0.0021 | - | | 0.9907 | 22750 | 0.003 | - | | 0.9929 | 22800 | 0.0099 | - | | 0.9951 | 22850 | 0.001 | - | | 0.9973 | 22900 | 0.0521 | - | | 0.9994 | 22950 | 0.0003 | - | ### Framework Versions - Python: 3.10.13 - SetFit: 1.0.1 - Sentence Transformers: 2.2.2 - Transformers: 4.36.2 - PyTorch: 2.0.1 - Datasets: 2.16.1 - Tokenizers: 0.15.0 ## 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} } ```