--- 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: 'metrics.statistics.syllables: 170648.1, 7686.9, 33810.3, 106802.1, 28752.3, 132642.9, 29187.0, 92593.8, 55913.4, 33722.1, 294744.6, 137215.8, 240762.6, 363292.2, 183016.8, 4864.5, 202932.9, 71705.7, 105003.9, 787942.8' - 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.Fiber: 0.0, 0.2, 0.3, 0.4, 0.7, 0.1, 1.0, 0.6, 0.5, 1.9, 1.1, 2.3, 0.8, 1.6, 0.9, 1.2, 37.0, 4.5, 9.1, 1.5' - 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: 0.7698072805139187 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:** 58 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 | |:------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Date | | | ID | | | Likert scale | | | Structured field | | | Alphanumeric identifier | | | Longitude | | | Gender | | | Very short text | | | Color | | | Time | | | Region | | | Slug | | | Numeric | | | Timestamp | | | Country ISO Code | | | Latitude | | | Letter grade | | | U.S. State Abbreviation | | | URI | | | Floating Point Number | | | Race/Ethnicity | | | Occupation | | | Country Name | | | U.S. State | | | Short text | | | Street Address | | | City Name | | | Day of Month | | | Year | | | Month Number | | | Continents | | | Integer | | | Numeric identifier | | | Price | | | Zip Code | | | Categorical | | | Boolean | | | Day of Week | | | Percentage | | | Postal Code | | | Street Name | | | Month Name | | | Currency Code | | | Full Name | | | URL | | | Place | | | Coordinate | | | Company Name | | | Partial timestamp | | | Age | | | Secondary Address | | | Marital status | | | AM/PM | | | Last Name | | | Location | | | Abbreviation | | | First Name | | | License Plate | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.7698 | ## 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-v3labels") # Run inference preds = model("variety: Western, Eastern") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 2 | 22.2181 | 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 | | Structured field | 6 | | Alphanumeric identifier | 8 | | Percentage | 7 | | ID | 2 | | Numeric identifier | 8 | ### 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 | |:------:|:-----:|:-------------:|:---------------:| | 0.0001 | 1 | 0.4559 | - | | 0.0069 | 50 | 0.1699 | - | | 0.0139 | 100 | 0.155 | - | | 0.0208 | 150 | 0.1671 | - | | 0.0278 | 200 | 0.1705 | - | | 0.0347 | 250 | 0.1417 | - | | 0.0417 | 300 | 0.1193 | - | | 0.0486 | 350 | 0.1377 | - | | 0.0556 | 400 | 0.143 | - | | 0.0625 | 450 | 0.1344 | - | | 0.0695 | 500 | 0.1367 | - | | 0.0764 | 550 | 0.1317 | - | | 0.0834 | 600 | 0.0951 | - | | 0.0903 | 650 | 0.1051 | - | | 0.0973 | 700 | 0.099 | - | | 0.1042 | 750 | 0.1038 | - | | 0.1112 | 800 | 0.0948 | - | | 0.1181 | 850 | 0.0924 | - | | 0.1251 | 900 | 0.0748 | - | | 0.1320 | 950 | 0.0792 | - | | 0.1389 | 1000 | 0.0805 | - | | 0.1459 | 1050 | 0.0779 | - | | 0.1528 | 1100 | 0.0689 | - | | 0.1598 | 1150 | 0.0595 | - | | 0.1667 | 1200 | 0.0639 | - | | 0.1737 | 1250 | 0.0589 | - | | 0.1806 | 1300 | 0.0649 | - | | 0.1876 | 1350 | 0.0589 | - | | 0.1945 | 1400 | 0.0548 | - | | 0.2015 | 1450 | 0.0634 | - | | 0.2084 | 1500 | 0.053 | - | | 0.2154 | 1550 | 0.0528 | - | | 0.2223 | 1600 | 0.0553 | - | | 0.2293 | 1650 | 0.0548 | - | | 0.2362 | 1700 | 0.0433 | - | | 0.2432 | 1750 | 0.0431 | - | | 0.2501 | 1800 | 0.0423 | - | | 0.2571 | 1850 | 0.0405 | - | | 0.2640 | 1900 | 0.0448 | - | | 0.2709 | 1950 | 0.0446 | - | | 0.2779 | 2000 | 0.0332 | - | | 0.2848 | 2050 | 0.0398 | - | | 0.2918 | 2100 | 0.0384 | - | | 0.2987 | 2150 | 0.0279 | - | | 0.3057 | 2200 | 0.0296 | - | | 0.3126 | 2250 | 0.0368 | - | | 0.3196 | 2300 | 0.0295 | - | | 0.3265 | 2350 | 0.0274 | - | | 0.3335 | 2400 | 0.0373 | - | | 0.3404 | 2450 | 0.0257 | - | | 0.3474 | 2500 | 0.0285 | - | | 0.3543 | 2550 | 0.0309 | - | | 0.3613 | 2600 | 0.0245 | - | | 0.3682 | 2650 | 0.0241 | - | | 0.3752 | 2700 | 0.0233 | - | | 0.3821 | 2750 | 0.0208 | - | | 0.3891 | 2800 | 0.02 | - | | 0.3960 | 2850 | 0.0219 | - | | 0.4029 | 2900 | 0.0203 | - | | 0.4099 | 2950 | 0.0213 | - | | 0.4168 | 3000 | 0.0292 | - | | 0.4238 | 3050 | 0.0204 | - | | 0.4307 | 3100 | 0.0182 | - | | 0.4377 | 3150 | 0.0239 | - | | 0.4446 | 3200 | 0.0161 | - | | 0.4516 | 3250 | 0.0243 | - | | 0.4585 | 3300 | 0.0168 | - | | 0.4655 | 3350 | 0.0195 | - | | 0.4724 | 3400 | 0.0152 | - | | 0.4794 | 3450 | 0.0178 | - | | 0.4863 | 3500 | 0.0171 | - | | 0.4933 | 3550 | 0.0126 | - | | 0.5002 | 3600 | 0.0192 | - | | 0.5072 | 3650 | 0.0132 | - | | 0.5141 | 3700 | 0.0145 | - | | 0.5211 | 3750 | 0.0139 | - | | 0.5280 | 3800 | 0.0132 | - | | 0.5349 | 3850 | 0.0147 | - | | 0.5419 | 3900 | 0.0159 | - | | 0.5488 | 3950 | 0.0132 | - | | 0.5558 | 4000 | 0.0136 | - | | 0.5627 | 4050 | 0.018 | - | | 0.5697 | 4100 | 0.0189 | - | | 0.5766 | 4150 | 0.0131 | - | | 0.5836 | 4200 | 0.0162 | - | | 0.5905 | 4250 | 0.0178 | - | | 0.5975 | 4300 | 0.0107 | - | | 0.6044 | 4350 | 0.0133 | - | | 0.6114 | 4400 | 0.0184 | - | | 0.6183 | 4450 | 0.0134 | - | | 0.6253 | 4500 | 0.0159 | - | | 0.6322 | 4550 | 0.0147 | - | | 0.6392 | 4600 | 0.0082 | - | | 0.6461 | 4650 | 0.0132 | - | | 0.6530 | 4700 | 0.0124 | - | | 0.6600 | 4750 | 0.012 | - | | 0.6669 | 4800 | 0.0159 | - | | 0.6739 | 4850 | 0.0146 | - | | 0.6808 | 4900 | 0.0118 | - | | 0.6878 | 4950 | 0.0146 | - | | 0.6947 | 5000 | 0.0121 | - | | 0.7017 | 5050 | 0.0121 | - | | 0.7086 | 5100 | 0.009 | - | | 0.7156 | 5150 | 0.0093 | - | | 0.7225 | 5200 | 0.0132 | - | | 0.7295 | 5250 | 0.0115 | - | | 0.7364 | 5300 | 0.0122 | - | | 0.7434 | 5350 | 0.0125 | - | | 0.7503 | 5400 | 0.011 | - | | 0.7573 | 5450 | 0.0116 | - | | 0.7642 | 5500 | 0.0139 | - | | 0.7712 | 5550 | 0.0134 | - | | 0.7781 | 5600 | 0.0088 | - | | 0.7850 | 5650 | 0.0116 | - | | 0.7920 | 5700 | 0.0095 | - | | 0.7989 | 5750 | 0.0093 | - | | 0.8059 | 5800 | 0.0076 | - | | 0.8128 | 5850 | 0.0088 | - | | 0.8198 | 5900 | 0.0094 | - | | 0.8267 | 5950 | 0.0077 | - | | 0.8337 | 6000 | 0.0102 | - | | 0.8406 | 6050 | 0.0113 | - | | 0.8476 | 6100 | 0.0086 | - | | 0.8545 | 6150 | 0.0147 | - | | 0.8615 | 6200 | 0.0074 | - | | 0.8684 | 6250 | 0.0066 | - | | 0.8754 | 6300 | 0.0055 | - | | 0.8823 | 6350 | 0.0082 | - | | 0.8893 | 6400 | 0.0077 | - | | 0.8962 | 6450 | 0.0055 | - | | 0.9032 | 6500 | 0.0072 | - | | 0.9101 | 6550 | 0.0098 | - | | 0.9170 | 6600 | 0.0095 | - | | 0.9240 | 6650 | 0.0085 | - | | 0.9309 | 6700 | 0.0111 | - | | 0.9379 | 6750 | 0.0069 | - | | 0.9448 | 6800 | 0.0109 | - | | 0.9518 | 6850 | 0.0077 | - | | 0.9587 | 6900 | 0.0081 | - | | 0.9657 | 6950 | 0.0046 | - | | 0.9726 | 7000 | 0.0068 | - | | 0.9796 | 7050 | 0.0072 | - | | 0.9865 | 7100 | 0.0044 | - | | 0.9935 | 7150 | 0.0037 | - | | 1.0 | 7197 | - | 0.1627 | | 1.0004 | 7200 | 0.0067 | - | | 1.0074 | 7250 | 0.0054 | - | | 1.0143 | 7300 | 0.0093 | - | | 1.0213 | 7350 | 0.0052 | - | | 1.0282 | 7400 | 0.0066 | - | | 1.0352 | 7450 | 0.007 | - | | 1.0421 | 7500 | 0.0037 | - | | 1.0490 | 7550 | 0.0116 | - | | 1.0560 | 7600 | 0.0085 | - | | 1.0629 | 7650 | 0.0075 | - | | 1.0699 | 7700 | 0.0042 | - | | 1.0768 | 7750 | 0.0048 | - | | 1.0838 | 7800 | 0.0039 | - | | 1.0907 | 7850 | 0.0056 | - | | 1.0977 | 7900 | 0.0075 | - | | 1.1046 | 7950 | 0.0052 | - | | 1.1116 | 8000 | 0.0038 | - | | 1.1185 | 8050 | 0.008 | - | | 1.1255 | 8100 | 0.0041 | - | | 1.1324 | 8150 | 0.0032 | - | | 1.1394 | 8200 | 0.0084 | - | | 1.1463 | 8250 | 0.0107 | - | | 1.1533 | 8300 | 0.0044 | - | | 1.1602 | 8350 | 0.0036 | - | | 1.1672 | 8400 | 0.0049 | - | | 1.1741 | 8450 | 0.0036 | - | | 1.1810 | 8500 | 0.0066 | - | | 1.1880 | 8550 | 0.0046 | - | | 1.1949 | 8600 | 0.0044 | - | | 1.2019 | 8650 | 0.0039 | - | | 1.2088 | 8700 | 0.0069 | - | | 1.2158 | 8750 | 0.0029 | - | | 1.2227 | 8800 | 0.0076 | - | | 1.2297 | 8850 | 0.0034 | - | | 1.2366 | 8900 | 0.0066 | - | | 1.2436 | 8950 | 0.0029 | - | | 1.2505 | 9000 | 0.0033 | - | | 1.2575 | 9050 | 0.0051 | - | | 1.2644 | 9100 | 0.0042 | - | | 1.2714 | 9150 | 0.0034 | - | | 1.2783 | 9200 | 0.0038 | - | | 1.2853 | 9250 | 0.0045 | - | | 1.2922 | 9300 | 0.0031 | - | | 1.2992 | 9350 | 0.0031 | - | | 1.3061 | 9400 | 0.0026 | - | | 1.3130 | 9450 | 0.0025 | - | | 1.3200 | 9500 | 0.0025 | - | | 1.3269 | 9550 | 0.0029 | - | | 1.3339 | 9600 | 0.0059 | - | | 1.3408 | 9650 | 0.0044 | - | | 1.3478 | 9700 | 0.0023 | - | | 1.3547 | 9750 | 0.0047 | - | | 1.3617 | 9800 | 0.0025 | - | | 1.3686 | 9850 | 0.0021 | - | | 1.3756 | 9900 | 0.0048 | - | | 1.3825 | 9950 | 0.0028 | - | | 1.3895 | 10000 | 0.0022 | - | | 1.3964 | 10050 | 0.004 | - | | 1.4034 | 10100 | 0.0049 | - | | 1.4103 | 10150 | 0.004 | - | | 1.4173 | 10200 | 0.0039 | - | | 1.4242 | 10250 | 0.0023 | - | | 1.4312 | 10300 | 0.0021 | - | | 1.4381 | 10350 | 0.0035 | - | | 1.4450 | 10400 | 0.0019 | - | | 1.4520 | 10450 | 0.0026 | - | | 1.4589 | 10500 | 0.002 | - | | 1.4659 | 10550 | 0.0022 | - | | 1.4728 | 10600 | 0.0066 | - | | 1.4798 | 10650 | 0.0058 | - | | 1.4867 | 10700 | 0.0026 | - | | 1.4937 | 10750 | 0.0018 | - | | 1.5006 | 10800 | 0.0021 | - | | 1.5076 | 10850 | 0.0039 | - | | 1.5145 | 10900 | 0.0025 | - | | 1.5215 | 10950 | 0.0018 | - | | 1.5284 | 11000 | 0.0051 | - | | 1.5354 | 11050 | 0.0028 | - | | 1.5423 | 11100 | 0.0018 | - | | 1.5493 | 11150 | 0.0019 | - | | 1.5562 | 11200 | 0.0046 | - | | 1.5632 | 11250 | 0.0024 | - | | 1.5701 | 11300 | 0.0031 | - | | 1.5770 | 11350 | 0.0028 | - | | 1.5840 | 11400 | 0.0027 | - | | 1.5909 | 11450 | 0.0023 | - | | 1.5979 | 11500 | 0.002 | - | | 1.6048 | 11550 | 0.0021 | - | | 1.6118 | 11600 | 0.0016 | - | | 1.6187 | 11650 | 0.003 | - | | 1.6257 | 11700 | 0.0028 | - | | 1.6326 | 11750 | 0.0019 | - | | 1.6396 | 11800 | 0.002 | - | | 1.6465 | 11850 | 0.002 | - | | 1.6535 | 11900 | 0.0016 | - | | 1.6604 | 11950 | 0.0016 | - | | 1.6674 | 12000 | 0.0017 | - | | 1.6743 | 12050 | 0.0015 | - | | 1.6813 | 12100 | 0.0017 | - | | 1.6882 | 12150 | 0.0028 | - | | 1.6952 | 12200 | 0.002 | - | | 1.7021 | 12250 | 0.0018 | - | | 1.7090 | 12300 | 0.0019 | - | | 1.7160 | 12350 | 0.0046 | - | | 1.7229 | 12400 | 0.0014 | - | | 1.7299 | 12450 | 0.0028 | - | | 1.7368 | 12500 | 0.0017 | - | | 1.7438 | 12550 | 0.0017 | - | | 1.7507 | 12600 | 0.0015 | - | | 1.7577 | 12650 | 0.0029 | - | | 1.7646 | 12700 | 0.0046 | - | | 1.7716 | 12750 | 0.0035 | - | | 1.7785 | 12800 | 0.0035 | - | | 1.7855 | 12850 | 0.0017 | - | | 1.7924 | 12900 | 0.0016 | - | | 1.7994 | 12950 | 0.0017 | - | | 1.8063 | 13000 | 0.0012 | - | | 1.8133 | 13050 | 0.0035 | - | | 1.8202 | 13100 | 0.0019 | - | | 1.8272 | 13150 | 0.0037 | - | | 1.8341 | 13200 | 0.0016 | - | | 1.8410 | 13250 | 0.0032 | - | | 1.8480 | 13300 | 0.0018 | - | | 1.8549 | 13350 | 0.0013 | - | | 1.8619 | 13400 | 0.0034 | - | | 1.8688 | 13450 | 0.0016 | - | | 1.8758 | 13500 | 0.002 | - | | 1.8827 | 13550 | 0.0013 | - | | 1.8897 | 13600 | 0.0016 | - | | 1.8966 | 13650 | 0.0013 | - | | 1.9036 | 13700 | 0.0016 | - | | 1.9105 | 13750 | 0.0012 | - | | 1.9175 | 13800 | 0.0029 | - | | 1.9244 | 13850 | 0.0013 | - | | 1.9314 | 13900 | 0.0028 | - | | 1.9383 | 13950 | 0.0012 | - | | 1.9453 | 14000 | 0.0012 | - | | 1.9522 | 14050 | 0.002 | - | | 1.9591 | 14100 | 0.0013 | - | | 1.9661 | 14150 | 0.0032 | - | | 1.9730 | 14200 | 0.0016 | - | | 1.9800 | 14250 | 0.0013 | - | | 1.9869 | 14300 | 0.0014 | - | | 1.9939 | 14350 | 0.0028 | - | | 2.0 | 14394 | - | 0.1425 | | 2.0008 | 14400 | 0.0012 | - | | 2.0078 | 14450 | 0.0013 | - | | 2.0147 | 14500 | 0.0029 | - | | 2.0217 | 14550 | 0.0012 | - | | 2.0286 | 14600 | 0.0012 | - | | 2.0356 | 14650 | 0.0011 | - | | 2.0425 | 14700 | 0.0011 | - | | 2.0495 | 14750 | 0.0036 | - | | 2.0564 | 14800 | 0.0013 | - | | 2.0634 | 14850 | 0.0013 | - | | 2.0703 | 14900 | 0.0029 | - | | 2.0773 | 14950 | 0.0018 | - | | 2.0842 | 15000 | 0.0012 | - | | 2.0911 | 15050 | 0.0012 | - | | 2.0981 | 15100 | 0.0029 | - | | 2.1050 | 15150 | 0.0027 | - | | 2.1120 | 15200 | 0.0015 | - | | 2.1189 | 15250 | 0.0015 | - | | 2.1259 | 15300 | 0.0015 | - | | 2.1328 | 15350 | 0.001 | - | | 2.1398 | 15400 | 0.0028 | - | | 2.1467 | 15450 | 0.0028 | - | | 2.1537 | 15500 | 0.0013 | - | | 2.1606 | 15550 | 0.0012 | - | | 2.1676 | 15600 | 0.001 | - | | 2.1745 | 15650 | 0.0026 | - | | 2.1815 | 15700 | 0.0014 | - | | 2.1884 | 15750 | 0.0011 | - | | 2.1954 | 15800 | 0.0008 | - | | 2.2023 | 15850 | 0.0012 | - | | 2.2093 | 15900 | 0.0014 | - | | 2.2162 | 15950 | 0.0012 | - | | 2.2231 | 16000 | 0.0026 | - | | 2.2301 | 16050 | 0.0011 | - | | 2.2370 | 16100 | 0.001 | - | | 2.2440 | 16150 | 0.001 | - | | 2.2509 | 16200 | 0.001 | - | | 2.2579 | 16250 | 0.001 | - | | 2.2648 | 16300 | 0.0028 | - | | 2.2718 | 16350 | 0.0014 | - | | 2.2787 | 16400 | 0.0046 | - | | 2.2857 | 16450 | 0.001 | - | | 2.2926 | 16500 | 0.0012 | - | | 2.2996 | 16550 | 0.001 | - | | 2.3065 | 16600 | 0.0011 | - | | 2.3135 | 16650 | 0.0009 | - | | 2.3204 | 16700 | 0.001 | - | | 2.3274 | 16750 | 0.0009 | - | | 2.3343 | 16800 | 0.001 | - | | 2.3413 | 16850 | 0.001 | - | | 2.3482 | 16900 | 0.0009 | - | | 2.3551 | 16950 | 0.0026 | - | | 2.3621 | 17000 | 0.0022 | - | | 2.3690 | 17050 | 0.0009 | - | | 2.3760 | 17100 | 0.001 | - | | 2.3829 | 17150 | 0.0008 | - | | 2.3899 | 17200 | 0.0029 | - | | 2.3968 | 17250 | 0.001 | - | | 2.4038 | 17300 | 0.001 | - | | 2.4107 | 17350 | 0.001 | - | | 2.4177 | 17400 | 0.0013 | - | | 2.4246 | 17450 | 0.0011 | - | | 2.4316 | 17500 | 0.0008 | - | | 2.4385 | 17550 | 0.0009 | - | | 2.4455 | 17600 | 0.0007 | - | | 2.4524 | 17650 | 0.0033 | - | | 2.4594 | 17700 | 0.0009 | - | | 2.4663 | 17750 | 0.0009 | - | | 2.4733 | 17800 | 0.0014 | - | | 2.4802 | 17850 | 0.0027 | - | | 2.4871 | 17900 | 0.001 | - | | 2.4941 | 17950 | 0.0017 | - | | 2.5010 | 18000 | 0.001 | - | | 2.5080 | 18050 | 0.0023 | - | | 2.5149 | 18100 | 0.0031 | - | | 2.5219 | 18150 | 0.001 | - | | 2.5288 | 18200 | 0.0008 | - | | 2.5358 | 18250 | 0.0008 | - | | 2.5427 | 18300 | 0.0009 | - | | 2.5497 | 18350 | 0.0011 | - | | 2.5566 | 18400 | 0.0009 | - | | 2.5636 | 18450 | 0.0012 | - | | 2.5705 | 18500 | 0.0011 | - | | 2.5775 | 18550 | 0.0008 | - | | 2.5844 | 18600 | 0.0025 | - | | 2.5914 | 18650 | 0.0009 | - | | 2.5983 | 18700 | 0.0009 | - | | 2.6053 | 18750 | 0.0008 | - | | 2.6122 | 18800 | 0.0008 | - | | 2.6191 | 18850 | 0.0008 | - | | 2.6261 | 18900 | 0.0008 | - | | 2.6330 | 18950 | 0.0008 | - | | 2.6400 | 19000 | 0.0009 | - | | 2.6469 | 19050 | 0.0007 | - | | 2.6539 | 19100 | 0.0008 | - | | 2.6608 | 19150 | 0.0007 | - | | 2.6678 | 19200 | 0.0008 | - | | 2.6747 | 19250 | 0.0008 | - | | 2.6817 | 19300 | 0.0009 | - | | 2.6886 | 19350 | 0.0007 | - | | 2.6956 | 19400 | 0.0009 | - | | 2.7025 | 19450 | 0.0009 | - | | 2.7095 | 19500 | 0.0012 | - | | 2.7164 | 19550 | 0.0007 | - | | 2.7234 | 19600 | 0.0008 | - | | 2.7303 | 19650 | 0.0009 | - | | 2.7373 | 19700 | 0.0023 | - | | 2.7442 | 19750 | 0.001 | - | | 2.7511 | 19800 | 0.0041 | - | | 2.7581 | 19850 | 0.0011 | - | | 2.7650 | 19900 | 0.0009 | - | | 2.7720 | 19950 | 0.0008 | - | | 2.7789 | 20000 | 0.0009 | - | | 2.7859 | 20050 | 0.0009 | - | | 2.7928 | 20100 | 0.0009 | - | | 2.7998 | 20150 | 0.0012 | - | | 2.8067 | 20200 | 0.0007 | - | | 2.8137 | 20250 | 0.0008 | - | | 2.8206 | 20300 | 0.0007 | - | | 2.8276 | 20350 | 0.001 | - | | 2.8345 | 20400 | 0.0006 | - | | 2.8415 | 20450 | 0.0021 | - | | 2.8484 | 20500 | 0.0007 | - | | 2.8554 | 20550 | 0.0013 | - | | 2.8623 | 20600 | 0.0007 | - | | 2.8693 | 20650 | 0.0008 | - | | 2.8762 | 20700 | 0.0009 | - | | 2.8831 | 20750 | 0.0007 | - | | 2.8901 | 20800 | 0.0007 | - | | 2.8970 | 20850 | 0.0006 | - | | 2.9040 | 20900 | 0.0022 | - | | 2.9109 | 20950 | 0.0006 | - | | 2.9179 | 21000 | 0.0007 | - | | 2.9248 | 21050 | 0.0008 | - | | 2.9318 | 21100 | 0.0007 | - | | 2.9387 | 21150 | 0.0006 | - | | 2.9457 | 21200 | 0.0023 | - | | 2.9526 | 21250 | 0.0009 | - | | 2.9596 | 21300 | 0.0038 | - | | 2.9665 | 21350 | 0.0008 | - | | 2.9735 | 21400 | 0.0008 | - | | 2.9804 | 21450 | 0.0007 | - | | 2.9874 | 21500 | 0.0008 | - | | 2.9943 | 21550 | 0.0006 | - | | 3.0 | 21591 | - | 0.1358 | | 3.0013 | 21600 | 0.0012 | - | | 3.0082 | 21650 | 0.0007 | - | | 3.0151 | 21700 | 0.0018 | - | | 3.0221 | 21750 | 0.0005 | - | | 3.0290 | 21800 | 0.0008 | - | | 3.0360 | 21850 | 0.0006 | - | | 3.0429 | 21900 | 0.0006 | - | | 3.0499 | 21950 | 0.0019 | - | | 3.0568 | 22000 | 0.0007 | - | | 3.0638 | 22050 | 0.0007 | - | | 3.0707 | 22100 | 0.0006 | - | | 3.0777 | 22150 | 0.0005 | - | | 3.0846 | 22200 | 0.0006 | - | | 3.0916 | 22250 | 0.0021 | - | | 3.0985 | 22300 | 0.0022 | - | | 3.1055 | 22350 | 0.0007 | - | | 3.1124 | 22400 | 0.0007 | - | | 3.1194 | 22450 | 0.0007 | - | | 3.1263 | 22500 | 0.0006 | - | | 3.1332 | 22550 | 0.0006 | - | | 3.1402 | 22600 | 0.0016 | - | | 3.1471 | 22650 | 0.003 | - | | 3.1541 | 22700 | 0.0007 | - | | 3.1610 | 22750 | 0.0008 | - | | 3.1680 | 22800 | 0.0006 | - | | 3.1749 | 22850 | 0.0006 | - | | 3.1819 | 22900 | 0.0005 | - | | 3.1888 | 22950 | 0.0018 | - | | 3.1958 | 23000 | 0.0007 | - | | 3.2027 | 23050 | 0.0009 | - | | 3.2097 | 23100 | 0.0006 | - | | 3.2166 | 23150 | 0.0006 | - | | 3.2236 | 23200 | 0.0008 | - | | 3.2305 | 23250 | 0.0005 | - | | 3.2375 | 23300 | 0.0006 | - | | 3.2444 | 23350 | 0.0007 | - | | 3.2514 | 23400 | 0.0006 | - | | 3.2583 | 23450 | 0.0006 | - | | 3.2652 | 23500 | 0.0007 | - | | 3.2722 | 23550 | 0.0005 | - | | 3.2791 | 23600 | 0.0015 | - | | 3.2861 | 23650 | 0.0006 | - | | 3.2930 | 23700 | 0.0007 | - | | 3.3000 | 23750 | 0.0018 | - | | 3.3069 | 23800 | 0.0008 | - | | 3.3139 | 23850 | 0.0005 | - | | 3.3208 | 23900 | 0.0021 | - | | 3.3278 | 23950 | 0.0006 | - | | 3.3347 | 24000 | 0.0011 | - | | 3.3417 | 24050 | 0.0007 | - | | 3.3486 | 24100 | 0.0007 | - | | 3.3556 | 24150 | 0.0009 | - | | 3.3625 | 24200 | 0.0006 | - | | 3.3695 | 24250 | 0.0007 | - | | 3.3764 | 24300 | 0.0015 | - | | 3.3834 | 24350 | 0.0008 | - | | 3.3903 | 24400 | 0.0006 | - | | 3.3972 | 24450 | 0.0007 | - | | 3.4042 | 24500 | 0.0005 | - | | 3.4111 | 24550 | 0.0005 | - | | 3.4181 | 24600 | 0.0006 | - | | 3.4250 | 24650 | 0.0005 | - | | 3.4320 | 24700 | 0.0007 | - | | 3.4389 | 24750 | 0.0006 | - | | 3.4459 | 24800 | 0.0006 | - | | 3.4528 | 24850 | 0.0007 | - | | 3.4598 | 24900 | 0.0018 | - | | 3.4667 | 24950 | 0.0006 | - | | 3.4737 | 25000 | 0.0006 | - | | 3.4806 | 25050 | 0.0005 | - | | 3.4876 | 25100 | 0.0006 | - | | 3.4945 | 25150 | 0.0007 | - | | 3.5015 | 25200 | 0.0006 | - | | 3.5084 | 25250 | 0.0005 | - | | 3.5154 | 25300 | 0.0006 | - | | 3.5223 | 25350 | 0.0022 | - | | 3.5292 | 25400 | 0.0034 | - | | 3.5362 | 25450 | 0.0005 | - | | 3.5431 | 25500 | 0.0006 | - | | 3.5501 | 25550 | 0.0005 | - | | 3.5570 | 25600 | 0.0006 | - | | 3.5640 | 25650 | 0.0007 | - | | 3.5709 | 25700 | 0.0006 | - | | 3.5779 | 25750 | 0.0005 | - | | 3.5848 | 25800 | 0.0006 | - | | 3.5918 | 25850 | 0.0005 | - | | 3.5987 | 25900 | 0.0013 | - | | 3.6057 | 25950 | 0.0006 | - | | 3.6126 | 26000 | 0.0006 | - | | 3.6196 | 26050 | 0.0006 | - | | 3.6265 | 26100 | 0.0006 | - | | 3.6335 | 26150 | 0.0005 | - | | 3.6404 | 26200 | 0.0006 | - | | 3.6474 | 26250 | 0.0016 | - | | 3.6543 | 26300 | 0.0009 | - | | 3.6612 | 26350 | 0.0005 | - | | 3.6682 | 26400 | 0.0006 | - | | 3.6751 | 26450 | 0.0005 | - | | 3.6821 | 26500 | 0.0005 | - | | 3.6890 | 26550 | 0.0007 | - | | 3.6960 | 26600 | 0.0005 | - | | 3.7029 | 26650 | 0.0037 | - | | 3.7099 | 26700 | 0.0006 | - | | 3.7168 | 26750 | 0.0007 | - | | 3.7238 | 26800 | 0.0007 | - | | 3.7307 | 26850 | 0.0017 | - | | 3.7377 | 26900 | 0.0005 | - | | 3.7446 | 26950 | 0.0006 | - | | 3.7516 | 27000 | 0.0005 | - | | 3.7585 | 27050 | 0.0006 | - | | 3.7655 | 27100 | 0.0006 | - | | 3.7724 | 27150 | 0.0017 | - | | 3.7794 | 27200 | 0.0007 | - | | 3.7863 | 27250 | 0.0014 | - | | 3.7932 | 27300 | 0.0021 | - | | 3.8002 | 27350 | 0.0005 | - | | 3.8071 | 27400 | 0.0007 | - | | 3.8141 | 27450 | 0.0006 | - | | 3.8210 | 27500 | 0.0017 | - | | 3.8280 | 27550 | 0.0006 | - | | 3.8349 | 27600 | 0.0006 | - | | 3.8419 | 27650 | 0.0005 | - | | 3.8488 | 27700 | 0.0007 | - | | 3.8558 | 27750 | 0.0018 | - | | 3.8627 | 27800 | 0.001 | - | | 3.8697 | 27850 | 0.0005 | - | | 3.8766 | 27900 | 0.0004 | - | | 3.8836 | 27950 | 0.0005 | - | | 3.8905 | 28000 | 0.0006 | - | | 3.8975 | 28050 | 0.0006 | - | | 3.9044 | 28100 | 0.0005 | - | | 3.9114 | 28150 | 0.0005 | - | | 3.9183 | 28200 | 0.0005 | - | | 3.9252 | 28250 | 0.0006 | - | | 3.9322 | 28300 | 0.0005 | - | | 3.9391 | 28350 | 0.0011 | - | | 3.9461 | 28400 | 0.0006 | - | | 3.9530 | 28450 | 0.0004 | - | | 3.9600 | 28500 | 0.0006 | - | | 3.9669 | 28550 | 0.0005 | - | | 3.9739 | 28600 | 0.0005 | - | | 3.9808 | 28650 | 0.0015 | - | | 3.9878 | 28700 | 0.0005 | - | | 3.9947 | 28750 | 0.0005 | - | | 4.0 | 28788 | - | 0.1340 | ### 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} } ```