--- base_model: sentence-transformers/paraphrase-MiniLM-L3-v2 library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 'Category: Milk, Buttermilk, Kefir, Goat''s milk, Non-dairy milk, Soy milk, Almond milk, Rice milk, Coconut milk, Yogurt, Chipotle dip, Dill dip, Onion dip, Ranch dip, Spinach dip, Tzatziki dip, Vegetable dip, Yogurt parfait, Frozen yogurt, Frozen yogurt sandwich' - text: 'company.sector: Software, Finance, Communications, pharmaceuticals, technology, Fashion, real estate, software, banking and insurance, groceries, construction/real estate/banking, Oil refining, Oil refining, retail, retail, casinos, food packaging, cars, cosmetics, None' - text: 'variety: Western, Eastern' - text: 'Data.Lycopene: 0, 1, 300, 7271, 6399, 4601, 4123, 1523, 1422, 1351, 11, 816, 819, 812, 1001, 769, 1365, 97, 21, 34' - text: 'Date.Month: 8, 3, 4, 5, 6, 7, 9, 10, 11, 12, 1, 2' inference: true model-index: - name: SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.7629716981132075 name: Accuracy --- # SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-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/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 128 tokens - **Number of Classes:** 53 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 | |:------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Integer | | | Country Name | | | License Plate | | | Date | | | Latitude | | | Month Number | | | Floating Point Number | | | Time | | | Place | | | Full Name | | | U.S. State Abbreviation | | | Price | | | U.S. State | | | Gender | | | Longitude | | | URL | | | Day of Week | | | Slug | | | Timestamp | | | Coordinate | | | Likert scale | | | Categorical | | | Secondary Address | | | Year | | | Zip Code | | | Region | | | AM/PM | | | Race/Ethnicity | | | Street Name | | | Day of Month | | | Boolean | | | Color | | | Location | | | Last Name | | | Company Name | | | Street Address | | | Short text | | | Occupation | | | Very short text | | | Numeric | | | URI | | | Letter grade | | | Month Name | | | Age | | | Partial timestamp | | | Abbreviation | | | Country ISO Code | | | City Name | | | Continents | | | Postal Code | | | Marital status | | | First Name | | | Currency Code | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.7630 | ## 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("quantisan/paraphrase-MiniLM-L3-v2-93dataset-v2labels") # Run inference preds = model("variety: Western, Eastern") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 2 | 22.1604 | 378 | | Label | Training Sample Count | |:------------------------|:----------------------| | Categorical | 8 | | Numeric | 8 | | Timestamp | 5 | | Date | 8 | | Integer | 8 | | Partial timestamp | 3 | | Short text | 8 | | Very short text | 3 | | AM/PM | 1 | | Boolean | 8 | | City Name | 4 | | Color | 3 | | Company Name | 1 | | Coordinate | 1 | | Country ISO Code | 3 | | Country Name | 8 | | Currency Code | 1 | | Day of Month | 3 | | Day of Week | 2 | | First Name | 1 | | Floating Point Number | 8 | | Full Name | 8 | | Last Name | 1 | | Latitude | 4 | | License Plate | 1 | | Longitude | 4 | | Month Name | 4 | | Month Number | 4 | | Occupation | 3 | | Postal Code | 1 | | Price | 1 | | Secondary Address | 1 | | Slug | 8 | | Street Address | 1 | | Street Name | 2 | | Time | 1 | | U.S. State | 8 | | U.S. State Abbreviation | 6 | | URI | 1 | | URL | 8 | | Year | 8 | | Zip Code | 3 | | Likert scale | 8 | | Gender | 8 | | Letter grade | 4 | | Race/Ethnicity | 3 | | Marital status | 2 | | Continents | 1 | | Region | 5 | | Age | 3 | | Place | 1 | | Abbreviation | 1 | | Location | 3 | ### Training Hyperparameters - batch_size: (8, 8) - num_epochs: (4, 4) - 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 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: True ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:-----:|:-------------:|:---------------:| | 0.0002 | 1 | 0.1497 | - | | 0.0092 | 50 | 0.1834 | - | | 0.0183 | 100 | 0.1917 | - | | 0.0275 | 150 | 0.1712 | - | | 0.0366 | 200 | 0.1505 | - | | 0.0458 | 250 | 0.146 | - | | 0.0549 | 300 | 0.1465 | - | | 0.0641 | 350 | 0.1297 | - | | 0.0732 | 400 | 0.1238 | - | | 0.0824 | 450 | 0.111 | - | | 0.0916 | 500 | 0.1035 | - | | 0.1007 | 550 | 0.1008 | - | | 0.1099 | 600 | 0.0914 | - | | 0.1190 | 650 | 0.0869 | - | | 0.1282 | 700 | 0.0792 | - | | 0.1373 | 750 | 0.0712 | - | | 0.1465 | 800 | 0.0709 | - | | 0.1556 | 850 | 0.0808 | - | | 0.1648 | 900 | 0.0659 | - | | 0.1740 | 950 | 0.0611 | - | | 0.1831 | 1000 | 0.0611 | - | | 0.1923 | 1050 | 0.0607 | - | | 0.2014 | 1100 | 0.0611 | - | | 0.2106 | 1150 | 0.0507 | - | | 0.2197 | 1200 | 0.0577 | - | | 0.2289 | 1250 | 0.0508 | - | | 0.2381 | 1300 | 0.0399 | - | | 0.2472 | 1350 | 0.0442 | - | | 0.2564 | 1400 | 0.0516 | - | | 0.2655 | 1450 | 0.0441 | - | | 0.2747 | 1500 | 0.0472 | - | | 0.2838 | 1550 | 0.0284 | - | | 0.2930 | 1600 | 0.0492 | - | | 0.3021 | 1650 | 0.035 | - | | 0.3113 | 1700 | 0.0338 | - | | 0.3205 | 1750 | 0.0286 | - | | 0.3296 | 1800 | 0.0296 | - | | 0.3388 | 1850 | 0.0328 | - | | 0.3479 | 1900 | 0.0277 | - | | 0.3571 | 1950 | 0.0269 | - | | 0.3662 | 2000 | 0.0262 | - | | 0.3754 | 2050 | 0.0311 | - | | 0.3845 | 2100 | 0.0277 | - | | 0.3937 | 2150 | 0.022 | - | | 0.4029 | 2200 | 0.0216 | - | | 0.4120 | 2250 | 0.0213 | - | | 0.4212 | 2300 | 0.0231 | - | | 0.4303 | 2350 | 0.0255 | - | | 0.4395 | 2400 | 0.02 | - | | 0.4486 | 2450 | 0.0181 | - | | 0.4578 | 2500 | 0.0196 | - | | 0.4669 | 2550 | 0.0182 | - | | 0.4761 | 2600 | 0.0199 | - | | 0.4853 | 2650 | 0.0171 | - | | 0.4944 | 2700 | 0.0171 | - | | 0.5036 | 2750 | 0.0169 | - | | 0.5127 | 2800 | 0.0161 | - | | 0.5219 | 2850 | 0.0104 | - | | 0.5310 | 2900 | 0.0133 | - | | 0.5402 | 2950 | 0.0137 | - | | 0.5493 | 3000 | 0.0241 | - | | 0.5585 | 3050 | 0.0156 | - | | 0.5677 | 3100 | 0.0155 | - | | 0.5768 | 3150 | 0.0158 | - | | 0.5860 | 3200 | 0.0165 | - | | 0.5951 | 3250 | 0.0141 | - | | 0.6043 | 3300 | 0.0129 | - | | 0.6134 | 3350 | 0.0129 | - | | 0.6226 | 3400 | 0.0103 | - | | 0.6318 | 3450 | 0.011 | - | | 0.6409 | 3500 | 0.0117 | - | | 0.6501 | 3550 | 0.0128 | - | | 0.6592 | 3600 | 0.0125 | - | | 0.6684 | 3650 | 0.0138 | - | | 0.6775 | 3700 | 0.0101 | - | | 0.6867 | 3750 | 0.0123 | - | | 0.6958 | 3800 | 0.0127 | - | | 0.7050 | 3850 | 0.0088 | - | | 0.7142 | 3900 | 0.0097 | - | | 0.7233 | 3950 | 0.0078 | - | | 0.7325 | 4000 | 0.0056 | - | | 0.7416 | 4050 | 0.0096 | - | | 0.7508 | 4100 | 0.0114 | - | | 0.7599 | 4150 | 0.0105 | - | | 0.7691 | 4200 | 0.0101 | - | | 0.7782 | 4250 | 0.0077 | - | | 0.7874 | 4300 | 0.0104 | - | | 0.7966 | 4350 | 0.007 | - | | 0.8057 | 4400 | 0.0112 | - | | 0.8149 | 4450 | 0.008 | - | | 0.8240 | 4500 | 0.0063 | - | | 0.8332 | 4550 | 0.0153 | - | | 0.8423 | 4600 | 0.0081 | - | | 0.8515 | 4650 | 0.007 | - | | 0.8606 | 4700 | 0.0052 | - | | 0.8698 | 4750 | 0.0054 | - | | 0.8790 | 4800 | 0.0063 | - | | 0.8881 | 4850 | 0.0131 | - | | 0.8973 | 4900 | 0.0086 | - | | 0.9064 | 4950 | 0.0086 | - | | 0.9156 | 5000 | 0.008 | - | | 0.9247 | 5050 | 0.0097 | - | | 0.9339 | 5100 | 0.0081 | - | | 0.9431 | 5150 | 0.0052 | - | | 0.9522 | 5200 | 0.008 | - | | 0.9614 | 5250 | 0.0055 | - | | 0.9705 | 5300 | 0.0048 | - | | 0.9797 | 5350 | 0.0055 | - | | 0.9888 | 5400 | 0.0064 | - | | 0.9980 | 5450 | 0.0043 | - | | 1.0 | 5461 | - | 0.0926 | | 1.0071 | 5500 | 0.0064 | - | | 1.0163 | 5550 | 0.0079 | - | | 1.0255 | 5600 | 0.0037 | - | | 1.0346 | 5650 | 0.0045 | - | | 1.0438 | 5700 | 0.0072 | - | | 1.0529 | 5750 | 0.0055 | - | | 1.0621 | 5800 | 0.0046 | - | | 1.0712 | 5850 | 0.0039 | - | | 1.0804 | 5900 | 0.0063 | - | | 1.0895 | 5950 | 0.0071 | - | | 1.0987 | 6000 | 0.005 | - | | 1.1079 | 6050 | 0.0066 | - | | 1.1170 | 6100 | 0.0041 | - | | 1.1262 | 6150 | 0.0056 | - | | 1.1353 | 6200 | 0.0063 | - | | 1.1445 | 6250 | 0.0057 | - | | 1.1536 | 6300 | 0.004 | - | | 1.1628 | 6350 | 0.0058 | - | | 1.1719 | 6400 | 0.0067 | - | | 1.1811 | 6450 | 0.0058 | - | | 1.1903 | 6500 | 0.0081 | - | | 1.1994 | 6550 | 0.0062 | - | | 1.2086 | 6600 | 0.0062 | - | | 1.2177 | 6650 | 0.0034 | - | | 1.2269 | 6700 | 0.0031 | - | | 1.2360 | 6750 | 0.0048 | - | | 1.2452 | 6800 | 0.006 | - | | 1.2543 | 6850 | 0.0054 | - | | 1.2635 | 6900 | 0.007 | - | | 1.2727 | 6950 | 0.0064 | - | | 1.2818 | 7000 | 0.0055 | - | | 1.2910 | 7050 | 0.0049 | - | | 1.3001 | 7100 | 0.0063 | - | | 1.3093 | 7150 | 0.0044 | - | | 1.3184 | 7200 | 0.0063 | - | | 1.3276 | 7250 | 0.003 | - | | 1.3368 | 7300 | 0.0049 | - | | 1.3459 | 7350 | 0.0047 | - | | 1.3551 | 7400 | 0.0043 | - | | 1.3642 | 7450 | 0.0023 | - | | 1.3734 | 7500 | 0.0025 | - | | 1.3825 | 7550 | 0.0047 | - | | 1.3917 | 7600 | 0.0027 | - | | 1.4008 | 7650 | 0.0036 | - | | 1.4100 | 7700 | 0.0026 | - | | 1.4192 | 7750 | 0.0019 | - | | 1.4283 | 7800 | 0.0048 | - | | 1.4375 | 7850 | 0.0047 | - | | 1.4466 | 7900 | 0.0041 | - | | 1.4558 | 7950 | 0.0073 | - | | 1.4649 | 8000 | 0.0023 | - | | 1.4741 | 8050 | 0.0054 | - | | 1.4832 | 8100 | 0.0042 | - | | 1.4924 | 8150 | 0.0078 | - | | 1.5016 | 8200 | 0.0063 | - | | 1.5107 | 8250 | 0.0033 | - | | 1.5199 | 8300 | 0.0055 | - | | 1.5290 | 8350 | 0.0043 | - | | 1.5382 | 8400 | 0.0027 | - | | 1.5473 | 8450 | 0.0021 | - | | 1.5565 | 8500 | 0.0022 | - | | 1.5656 | 8550 | 0.0063 | - | | 1.5748 | 8600 | 0.0049 | - | | 1.5840 | 8650 | 0.0049 | - | | 1.5931 | 8700 | 0.0057 | - | | 1.6023 | 8750 | 0.0035 | - | | 1.6114 | 8800 | 0.0022 | - | | 1.6206 | 8850 | 0.0029 | - | | 1.6297 | 8900 | 0.0062 | - | | 1.6389 | 8950 | 0.0022 | - | | 1.6480 | 9000 | 0.0047 | - | | 1.6572 | 9050 | 0.0024 | - | | 1.6664 | 9100 | 0.0053 | - | | 1.6755 | 9150 | 0.0021 | - | | 1.6847 | 9200 | 0.0029 | - | | 1.6938 | 9250 | 0.0031 | - | | 1.7030 | 9300 | 0.0024 | - | | 1.7121 | 9350 | 0.0034 | - | | 1.7213 | 9400 | 0.0021 | - | | 1.7305 | 9450 | 0.0025 | - | | 1.7396 | 9500 | 0.0023 | - | | 1.7488 | 9550 | 0.0029 | - | | 1.7579 | 9600 | 0.0025 | - | | 1.7671 | 9650 | 0.0021 | - | | 1.7762 | 9700 | 0.0019 | - | | 1.7854 | 9750 | 0.0034 | - | | 1.7945 | 9800 | 0.0016 | - | | 1.8037 | 9850 | 0.0019 | - | | 1.8129 | 9900 | 0.0024 | - | | 1.8220 | 9950 | 0.002 | - | | 1.8312 | 10000 | 0.0021 | - | | 1.8403 | 10050 | 0.0061 | - | | 1.8495 | 10100 | 0.0019 | - | | 1.8586 | 10150 | 0.0014 | - | | 1.8678 | 10200 | 0.0021 | - | | 1.8769 | 10250 | 0.0031 | - | | 1.8861 | 10300 | 0.002 | - | | 1.8953 | 10350 | 0.0014 | - | | 1.9044 | 10400 | 0.0015 | - | | 1.9136 | 10450 | 0.0014 | - | | 1.9227 | 10500 | 0.0018 | - | | 1.9319 | 10550 | 0.0014 | - | | 1.9410 | 10600 | 0.0015 | - | | 1.9502 | 10650 | 0.0014 | - | | 1.9593 | 10700 | 0.0013 | - | | 1.9685 | 10750 | 0.0032 | - | | 1.9777 | 10800 | 0.0017 | - | | 1.9868 | 10850 | 0.0015 | - | | 1.9960 | 10900 | 0.0012 | - | | 2.0 | 10922 | - | 0.1071 | | 2.0051 | 10950 | 0.0013 | - | | 2.0143 | 11000 | 0.0013 | - | | 2.0234 | 11050 | 0.0015 | - | | 2.0326 | 11100 | 0.0013 | - | | 2.0418 | 11150 | 0.0013 | - | | 2.0509 | 11200 | 0.0011 | - | | 2.0601 | 11250 | 0.0013 | - | | 2.0692 | 11300 | 0.0013 | - | | 2.0784 | 11350 | 0.0034 | - | | 2.0875 | 11400 | 0.0012 | - | | 2.0967 | 11450 | 0.0012 | - | | 2.1058 | 11500 | 0.0025 | - | | 2.1150 | 11550 | 0.0026 | - | | 2.1242 | 11600 | 0.0031 | - | | 2.1333 | 11650 | 0.0012 | - | | 2.1425 | 11700 | 0.0011 | - | | 2.1516 | 11750 | 0.0013 | - | | 2.1608 | 11800 | 0.0012 | - | | 2.1699 | 11850 | 0.0013 | - | | 2.1791 | 11900 | 0.0011 | - | | 2.1882 | 11950 | 0.0011 | - | | 2.1974 | 12000 | 0.0012 | - | | 2.2066 | 12050 | 0.0014 | - | | 2.2157 | 12100 | 0.003 | - | | 2.2249 | 12150 | 0.001 | - | | 2.2340 | 12200 | 0.0011 | - | | 2.2432 | 12250 | 0.0028 | - | | 2.2523 | 12300 | 0.0027 | - | | 2.2615 | 12350 | 0.0013 | - | | 2.2706 | 12400 | 0.0024 | - | | 2.2798 | 12450 | 0.0011 | - | | 2.2890 | 12500 | 0.001 | - | | 2.2981 | 12550 | 0.0011 | - | | 2.3073 | 12600 | 0.0011 | - | | 2.3164 | 12650 | 0.0029 | - | | 2.3256 | 12700 | 0.0029 | - | | 2.3347 | 12750 | 0.0009 | - | | 2.3439 | 12800 | 0.0013 | - | | 2.3530 | 12850 | 0.0009 | - | | 2.3622 | 12900 | 0.001 | - | | 2.3714 | 12950 | 0.0011 | - | | 2.3805 | 13000 | 0.0027 | - | | 2.3897 | 13050 | 0.0009 | - | | 2.3988 | 13100 | 0.0011 | - | | 2.4080 | 13150 | 0.0012 | - | | 2.4171 | 13200 | 0.0024 | - | | 2.4263 | 13250 | 0.0039 | - | | 2.4355 | 13300 | 0.001 | - | | 2.4446 | 13350 | 0.0017 | - | | 2.4538 | 13400 | 0.0012 | - | | 2.4629 | 13450 | 0.0021 | - | | 2.4721 | 13500 | 0.0021 | - | | 2.4812 | 13550 | 0.0032 | - | | 2.4904 | 13600 | 0.0012 | - | | 2.4995 | 13650 | 0.0012 | - | | 2.5087 | 13700 | 0.0014 | - | | 2.5179 | 13750 | 0.001 | - | | 2.5270 | 13800 | 0.0011 | - | | 2.5362 | 13850 | 0.0009 | - | | 2.5453 | 13900 | 0.0034 | - | | 2.5545 | 13950 | 0.0015 | - | | 2.5636 | 14000 | 0.0013 | - | | 2.5728 | 14050 | 0.0069 | - | | 2.5819 | 14100 | 0.001 | - | | 2.5911 | 14150 | 0.0034 | - | | 2.6003 | 14200 | 0.0028 | - | | 2.6094 | 14250 | 0.001 | - | | 2.6186 | 14300 | 0.0012 | - | | 2.6277 | 14350 | 0.0013 | - | | 2.6369 | 14400 | 0.0011 | - | | 2.6460 | 14450 | 0.0009 | - | | 2.6552 | 14500 | 0.001 | - | | 2.6643 | 14550 | 0.0009 | - | | 2.6735 | 14600 | 0.0012 | - | | 2.6827 | 14650 | 0.0041 | - | | 2.6918 | 14700 | 0.0008 | - | | 2.7010 | 14750 | 0.0019 | - | | 2.7101 | 14800 | 0.001 | - | | 2.7193 | 14850 | 0.0012 | - | | 2.7284 | 14900 | 0.0013 | - | | 2.7376 | 14950 | 0.0012 | - | | 2.7467 | 15000 | 0.0019 | - | | 2.7559 | 15050 | 0.0009 | - | | 2.7651 | 15100 | 0.0009 | - | | 2.7742 | 15150 | 0.0008 | - | | 2.7834 | 15200 | 0.0028 | - | | 2.7925 | 15250 | 0.0009 | - | | 2.8017 | 15300 | 0.0011 | - | | 2.8108 | 15350 | 0.0029 | - | | 2.8200 | 15400 | 0.0008 | - | | 2.8292 | 15450 | 0.001 | - | | 2.8383 | 15500 | 0.0019 | - | | 2.8475 | 15550 | 0.0011 | - | | 2.8566 | 15600 | 0.0022 | - | | 2.8658 | 15650 | 0.0011 | - | | 2.8749 | 15700 | 0.0009 | - | | 2.8841 | 15750 | 0.0008 | - | | 2.8932 | 15800 | 0.0009 | - | | 2.9024 | 15850 | 0.0009 | - | | 2.9116 | 15900 | 0.0011 | - | | 2.9207 | 15950 | 0.0011 | - | | 2.9299 | 16000 | 0.0017 | - | | 2.9390 | 16050 | 0.001 | - | | 2.9482 | 16100 | 0.0008 | - | | 2.9573 | 16150 | 0.0009 | - | | 2.9665 | 16200 | 0.0008 | - | | 2.9756 | 16250 | 0.0009 | - | | 2.9848 | 16300 | 0.0007 | - | | 2.9940 | 16350 | 0.0011 | - | | 3.0 | 16383 | - | 0.0990 | | 3.0031 | 16400 | 0.0008 | - | | 3.0123 | 16450 | 0.0008 | - | | 3.0214 | 16500 | 0.0008 | - | | 3.0306 | 16550 | 0.0008 | - | | 3.0397 | 16600 | 0.0015 | - | | 3.0489 | 16650 | 0.0007 | - | | 3.0580 | 16700 | 0.0008 | - | | 3.0672 | 16750 | 0.0009 | - | | 3.0764 | 16800 | 0.0008 | - | | 3.0855 | 16850 | 0.0008 | - | | 3.0947 | 16900 | 0.0023 | - | | 3.1038 | 16950 | 0.0007 | - | | 3.1130 | 17000 | 0.0006 | - | | 3.1221 | 17050 | 0.0024 | - | | 3.1313 | 17100 | 0.0008 | - | | 3.1405 | 17150 | 0.0017 | - | | 3.1496 | 17200 | 0.0011 | - | | 3.1588 | 17250 | 0.0008 | - | | 3.1679 | 17300 | 0.0008 | - | | 3.1771 | 17350 | 0.0007 | - | | 3.1862 | 17400 | 0.0014 | - | | 3.1954 | 17450 | 0.0008 | - | | 3.2045 | 17500 | 0.0007 | - | | 3.2137 | 17550 | 0.0007 | - | | 3.2229 | 17600 | 0.0006 | - | | 3.2320 | 17650 | 0.0007 | - | | 3.2412 | 17700 | 0.0021 | - | | 3.2503 | 17750 | 0.0006 | - | | 3.2595 | 17800 | 0.0006 | - | | 3.2686 | 17850 | 0.0007 | - | | 3.2778 | 17900 | 0.0006 | - | | 3.2869 | 17950 | 0.0008 | - | | 3.2961 | 18000 | 0.0008 | - | | 3.3053 | 18050 | 0.0008 | - | | 3.3144 | 18100 | 0.0027 | - | | 3.3236 | 18150 | 0.0008 | - | | 3.3327 | 18200 | 0.0007 | - | | 3.3419 | 18250 | 0.0007 | - | | 3.3510 | 18300 | 0.0008 | - | | 3.3602 | 18350 | 0.0007 | - | | 3.3693 | 18400 | 0.0022 | - | | 3.3785 | 18450 | 0.0007 | - | | 3.3877 | 18500 | 0.0014 | - | | 3.3968 | 18550 | 0.0006 | - | | 3.4060 | 18600 | 0.0016 | - | | 3.4151 | 18650 | 0.0007 | - | | 3.4243 | 18700 | 0.0015 | - | | 3.4334 | 18750 | 0.0006 | - | | 3.4426 | 18800 | 0.001 | - | | 3.4517 | 18850 | 0.0008 | - | | 3.4609 | 18900 | 0.0008 | - | | 3.4701 | 18950 | 0.0007 | - | | 3.4792 | 19000 | 0.0015 | - | | 3.4884 | 19050 | 0.0007 | - | | 3.4975 | 19100 | 0.0006 | - | | 3.5067 | 19150 | 0.0007 | - | | 3.5158 | 19200 | 0.0014 | - | | 3.5250 | 19250 | 0.0006 | - | | 3.5342 | 19300 | 0.0011 | - | | 3.5433 | 19350 | 0.0008 | - | | 3.5525 | 19400 | 0.0007 | - | | 3.5616 | 19450 | 0.0008 | - | | 3.5708 | 19500 | 0.0021 | - | | 3.5799 | 19550 | 0.0007 | - | | 3.5891 | 19600 | 0.0007 | - | | 3.5982 | 19650 | 0.0006 | - | | 3.6074 | 19700 | 0.0007 | - | | 3.6166 | 19750 | 0.0007 | - | | 3.6257 | 19800 | 0.0007 | - | | 3.6349 | 19850 | 0.001 | - | | 3.6440 | 19900 | 0.0011 | - | | 3.6532 | 19950 | 0.0007 | - | | 3.6623 | 20000 | 0.0006 | - | | 3.6715 | 20050 | 0.0022 | - | | 3.6806 | 20100 | 0.0011 | - | | 3.6898 | 20150 | 0.0007 | - | | 3.6990 | 20200 | 0.0006 | - | | 3.7081 | 20250 | 0.0007 | - | | 3.7173 | 20300 | 0.0006 | - | | 3.7264 | 20350 | 0.0006 | - | | 3.7356 | 20400 | 0.0013 | - | | 3.7447 | 20450 | 0.0009 | - | | 3.7539 | 20500 | 0.0006 | - | | 3.7630 | 20550 | 0.001 | - | | 3.7722 | 20600 | 0.0007 | - | | 3.7814 | 20650 | 0.0007 | - | | 3.7905 | 20700 | 0.0006 | - | | 3.7997 | 20750 | 0.0006 | - | | 3.8088 | 20800 | 0.0015 | - | | 3.8180 | 20850 | 0.0009 | - | | 3.8271 | 20900 | 0.0009 | - | | 3.8363 | 20950 | 0.0005 | - | | 3.8454 | 21000 | 0.0008 | - | | 3.8546 | 21050 | 0.0006 | - | | 3.8638 | 21100 | 0.0008 | - | | 3.8729 | 21150 | 0.0006 | - | | 3.8821 | 21200 | 0.0006 | - | | 3.8912 | 21250 | 0.0005 | - | | 3.9004 | 21300 | 0.0006 | - | | 3.9095 | 21350 | 0.0015 | - | | 3.9187 | 21400 | 0.0017 | - | | 3.9279 | 21450 | 0.0006 | - | | 3.9370 | 21500 | 0.0007 | - | | 3.9462 | 21550 | 0.0014 | - | | 3.9553 | 21600 | 0.0012 | - | | 3.9645 | 21650 | 0.0017 | - | | 3.9736 | 21700 | 0.0008 | - | | 3.9828 | 21750 | 0.0006 | - | | 3.9919 | 21800 | 0.0006 | - | | 4.0 | 21844 | - | 0.1004 | ### Framework Versions - Python: 3.11.10 - SetFit: 1.1.0 - Sentence Transformers: 3.2.0 - Transformers: 4.45.2 - PyTorch: 2.4.1+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## 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} } ```