--- base_model: BAAI/bge-small-en-v1.5 library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 'metrics.statistics.average letter per word: 4.83, 4.41, 4.65, 4.77, 4.56, 4.36, 4.94, 4.78, 4.68, 4.61, 4.92, 4.6, 4.59, 4.95, 4.88, 4.63, 4.9, 4.31, 4.81, 4.8' - text: 'company.relationship: founder, None, founder/chairman, Relation, relation, CEO, chairman, investor, founder and CEO, founder/CEO, owner, chairman of management committee, founder and chairman, Chairman and Chief Executive Officer, general director, executive chairman, Chairman/founder, founder, chairman, ceo, former chairman and CEO, relation and chairman' - text: 'variety: Western, Eastern' - text: 'Data.Cholesterol: 14, 8, 12, 5, 2, 3, 4, 11, 15, 29, 34, 0, 6, 10, 13, 16, 17, 7, 59, 20' - text: 'Date.Full: 8/26/1990, 3/24/1991, 3/31/1991, 4/7/1991, 4/14/1991, 4/21/1991, 4/28/1991, 5/5/1991, 5/12/1991, 5/19/1991, 5/26/1991, 6/2/1991, 6/9/1991, 6/16/1991, 6/23/1991, 6/30/1991, 7/7/1991, 7/14/1991, 7/21/1991, 7/28/1991' inference: true model-index: - name: SetFit with BAAI/bge-small-en-v1.5 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.6705461056401074 name: Accuracy --- # SetFit with BAAI/bge-small-en-v1.5 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) - **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:** 43 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 | |:------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Latitude | | | Categorical | | | Day of Month | | | Year | | | Integer | | | Floating Point Number | | | Percentage | | | Secondary Address | | | U.S. State Abbreviation | | | Numeric identifier | | | Month Number | | | Date | | | Full Name | | | Day of Week | | | Timestamp | | | URL | | | Street Address | | | Country ISO Code | | | Partial timestamp | | | Longitude | | | Country Name | | | Boolean | | | Short text | | | Slug | | | Postal Code | | | Structured field | | | Alphanumeric identifier | | | Color | | | Month Name | | | Currency Code | | | Time | | | Last Name | | | U.S. State | | | Street Name | | | AM/PM | | | Occupation | | | Zip Code | | | Company Name | | | First Name | | | Very short text | | | License Plate | | | URI | | | City Name | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.6705 | ## 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/bge-small-en-v1.5-93dataset") # Run inference preds = model("variety: Western, Eastern") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 2 | 24.0542 | 111 | | Label | Training Sample Count | |:------------------------|:----------------------| | Categorical | 8 | | Timestamp | 5 | | Date | 8 | | Integer | 8 | | Partial timestamp | 4 | | Short text | 8 | | Very short text | 3 | | AM/PM | 1 | | Boolean | 8 | | City Name | 1 | | Color | 3 | | Company Name | 1 | | Country ISO Code | 2 | | Country Name | 8 | | Currency Code | 1 | | Day of Month | 4 | | Day of Week | 4 | | First Name | 1 | | Floating Point Number | 8 | | Full Name | 8 | | Last Name | 2 | | Latitude | 4 | | License Plate | 1 | | Longitude | 4 | | Month Name | 6 | | Month Number | 4 | | Occupation | 3 | | Postal Code | 1 | | Secondary Address | 1 | | Slug | 8 | | Street Address | 3 | | Street Name | 3 | | Time | 3 | | U.S. State | 8 | | U.S. State Abbreviation | 6 | | URI | 1 | | URL | 8 | | Year | 8 | | Zip Code | 4 | ### 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 | 0.1131 | - | | 0.0100 | 50 | 0.2113 | - | | 0.0200 | 100 | 0.1906 | - | | 0.0301 | 150 | 0.1843 | - | | 0.0401 | 200 | 0.161 | - | | 0.0501 | 250 | 0.1418 | - | | 0.0601 | 300 | 0.131 | - | | 0.0701 | 350 | 0.1224 | - | | 0.0802 | 400 | 0.114 | - | | 0.0902 | 450 | 0.1039 | - | | 0.1002 | 500 | 0.0825 | - | | 0.1102 | 550 | 0.0838 | - | | 0.1202 | 600 | 0.0745 | - | | 0.1303 | 650 | 0.0705 | - | | 0.1403 | 700 | 0.0586 | - | | 0.1503 | 750 | 0.0552 | - | | 0.1603 | 800 | 0.0567 | - | | 0.1703 | 850 | 0.0553 | - | | 0.1804 | 900 | 0.0456 | - | | 0.1904 | 950 | 0.0443 | - | | 0.2004 | 1000 | 0.0422 | - | | 0.2104 | 1050 | 0.0363 | - | | 0.2204 | 1100 | 0.0397 | - | | 0.2305 | 1150 | 0.0361 | - | | 0.2405 | 1200 | 0.0269 | - | | 0.2505 | 1250 | 0.0331 | - | | 0.2605 | 1300 | 0.0278 | - | | 0.2705 | 1350 | 0.0297 | - | | 0.2806 | 1400 | 0.022 | - | | 0.2906 | 1450 | 0.0265 | - | | 0.3006 | 1500 | 0.0344 | - | | 0.3106 | 1550 | 0.0218 | - | | 0.3206 | 1600 | 0.0275 | - | | 0.3307 | 1650 | 0.0275 | - | | 0.3407 | 1700 | 0.0207 | - | | 0.3507 | 1750 | 0.0156 | - | | 0.3607 | 1800 | 0.0246 | - | | 0.3707 | 1850 | 0.0154 | - | | 0.3808 | 1900 | 0.0117 | - | | 0.3908 | 1950 | 0.0201 | - | | 0.4008 | 2000 | 0.0153 | - | | 0.4108 | 2050 | 0.018 | - | | 0.4208 | 2100 | 0.017 | - | | 0.4309 | 2150 | 0.011 | - | | 0.4409 | 2200 | 0.0158 | - | | 0.4509 | 2250 | 0.015 | - | | 0.4609 | 2300 | 0.0109 | - | | 0.4709 | 2350 | 0.0151 | - | | 0.4810 | 2400 | 0.0085 | - | | 0.4910 | 2450 | 0.0121 | - | | 0.5010 | 2500 | 0.0118 | - | | 0.5110 | 2550 | 0.0083 | - | | 0.5210 | 2600 | 0.0094 | - | | 0.5311 | 2650 | 0.0078 | - | | 0.5411 | 2700 | 0.0123 | - | | 0.5511 | 2750 | 0.0085 | - | | 0.5611 | 2800 | 0.0046 | - | | 0.5711 | 2850 | 0.0081 | - | | 0.5812 | 2900 | 0.0085 | - | | 0.5912 | 2950 | 0.0064 | - | | 0.6012 | 3000 | 0.0113 | - | | 0.6112 | 3050 | 0.0087 | - | | 0.6212 | 3100 | 0.0071 | - | | 0.6313 | 3150 | 0.01 | - | | 0.6413 | 3200 | 0.0093 | - | | 0.6513 | 3250 | 0.0056 | - | | 0.6613 | 3300 | 0.007 | - | | 0.6713 | 3350 | 0.0076 | - | | 0.6814 | 3400 | 0.0077 | - | | 0.6914 | 3450 | 0.0038 | - | | 0.7014 | 3500 | 0.0051 | - | | 0.7114 | 3550 | 0.0063 | - | | 0.7214 | 3600 | 0.004 | - | | 0.7315 | 3650 | 0.0036 | - | | 0.7415 | 3700 | 0.0043 | - | | 0.7515 | 3750 | 0.0086 | - | | 0.7615 | 3800 | 0.0051 | - | | 0.7715 | 3850 | 0.0056 | - | | 0.7816 | 3900 | 0.0042 | - | | 0.7916 | 3950 | 0.0062 | - | | 0.8016 | 4000 | 0.0058 | - | | 0.8116 | 4050 | 0.0034 | - | | 0.8216 | 4100 | 0.0062 | - | | 0.8317 | 4150 | 0.0091 | - | | 0.8417 | 4200 | 0.0056 | - | | 0.8517 | 4250 | 0.0039 | - | | 0.8617 | 4300 | 0.0072 | - | | 0.8717 | 4350 | 0.0051 | - | | 0.8818 | 4400 | 0.0025 | - | | 0.8918 | 4450 | 0.0051 | - | | 0.9018 | 4500 | 0.0049 | - | | 0.9118 | 4550 | 0.0024 | - | | 0.9218 | 4600 | 0.0026 | - | | 0.9319 | 4650 | 0.0046 | - | | 0.9419 | 4700 | 0.0024 | - | | 0.9519 | 4750 | 0.0026 | - | | 0.9619 | 4800 | 0.0045 | - | | 0.9719 | 4850 | 0.0022 | - | | 0.9820 | 4900 | 0.0042 | - | | 0.9920 | 4950 | 0.0067 | - | | 1.0 | 4990 | - | 0.0996 | | 1.0020 | 5000 | 0.0044 | - | | 1.0120 | 5050 | 0.0023 | - | | 1.0220 | 5100 | 0.0025 | - | | 1.0321 | 5150 | 0.004 | - | | 1.0421 | 5200 | 0.002 | - | | 1.0521 | 5250 | 0.0042 | - | | 1.0621 | 5300 | 0.0028 | - | | 1.0721 | 5350 | 0.006 | - | | 1.0822 | 5400 | 0.0043 | - | | 1.0922 | 5450 | 0.0065 | - | | 1.1022 | 5500 | 0.0042 | - | | 1.1122 | 5550 | 0.004 | - | | 1.1222 | 5600 | 0.0045 | - | | 1.1323 | 5650 | 0.0049 | - | | 1.1423 | 5700 | 0.0042 | - | | 1.1523 | 5750 | 0.0044 | - | | 1.1623 | 5800 | 0.002 | - | | 1.1723 | 5850 | 0.0037 | - | | 1.1824 | 5900 | 0.0038 | - | | 1.1924 | 5950 | 0.0071 | - | | 1.2024 | 6000 | 0.0044 | - | | 1.2124 | 6050 | 0.0031 | - | | 1.2224 | 6100 | 0.0021 | - | | 1.2325 | 6150 | 0.0019 | - | | 1.2425 | 6200 | 0.002 | - | | 1.2525 | 6250 | 0.0059 | - | | 1.2625 | 6300 | 0.002 | - | | 1.2725 | 6350 | 0.0036 | - | | 1.2826 | 6400 | 0.0019 | - | | 1.2926 | 6450 | 0.0041 | - | | 1.3026 | 6500 | 0.0042 | - | | 1.3126 | 6550 | 0.0062 | - | | 1.3226 | 6600 | 0.002 | - | | 1.3327 | 6650 | 0.0016 | - | | 1.3427 | 6700 | 0.0019 | - | | 1.3527 | 6750 | 0.0055 | - | | 1.3627 | 6800 | 0.0042 | - | | 1.3727 | 6850 | 0.0023 | - | | 1.3828 | 6900 | 0.0018 | - | | 1.3928 | 6950 | 0.0041 | - | | 1.4028 | 7000 | 0.008 | - | | 1.4128 | 7050 | 0.0021 | - | | 1.4228 | 7100 | 0.0017 | - | | 1.4329 | 7150 | 0.0021 | - | | 1.4429 | 7200 | 0.0017 | - | | 1.4529 | 7250 | 0.0035 | - | | 1.4629 | 7300 | 0.002 | - | | 1.4729 | 7350 | 0.0016 | - | | 1.4830 | 7400 | 0.0014 | - | | 1.4930 | 7450 | 0.0041 | - | | 1.5030 | 7500 | 0.0053 | - | | 1.5130 | 7550 | 0.0026 | - | | 1.5230 | 7600 | 0.002 | - | | 1.5331 | 7650 | 0.0017 | - | | 1.5431 | 7700 | 0.0017 | - | | 1.5531 | 7750 | 0.0016 | - | | 1.5631 | 7800 | 0.0021 | - | | 1.5731 | 7850 | 0.0039 | - | | 1.5832 | 7900 | 0.0034 | - | | 1.5932 | 7950 | 0.0061 | - | | 1.6032 | 8000 | 0.0025 | - | | 1.6132 | 8050 | 0.002 | - | | 1.6232 | 8100 | 0.0017 | - | | 1.6333 | 8150 | 0.0016 | - | | 1.6433 | 8200 | 0.0015 | - | | 1.6533 | 8250 | 0.0037 | - | | 1.6633 | 8300 | 0.0015 | - | | 1.6733 | 8350 | 0.0035 | - | | 1.6834 | 8400 | 0.0023 | - | | 1.6934 | 8450 | 0.0051 | - | | 1.7034 | 8500 | 0.0041 | - | | 1.7134 | 8550 | 0.0018 | - | | 1.7234 | 8600 | 0.0016 | - | | 1.7335 | 8650 | 0.0016 | - | | 1.7435 | 8700 | 0.0013 | - | | 1.7535 | 8750 | 0.0022 | - | | 1.7635 | 8800 | 0.0015 | - | | 1.7735 | 8850 | 0.0017 | - | | 1.7836 | 8900 | 0.0035 | - | | 1.7936 | 8950 | 0.0013 | - | | 1.8036 | 9000 | 0.0015 | - | | 1.8136 | 9050 | 0.0034 | - | | 1.8236 | 9100 | 0.0013 | - | | 1.8337 | 9150 | 0.0037 | - | | 1.8437 | 9200 | 0.0037 | - | | 1.8537 | 9250 | 0.0014 | - | | 1.8637 | 9300 | 0.0014 | - | | 1.8737 | 9350 | 0.0013 | - | | 1.8838 | 9400 | 0.0016 | - | | 1.8938 | 9450 | 0.0013 | - | | 1.9038 | 9500 | 0.0038 | - | | 1.9138 | 9550 | 0.0013 | - | | 1.9238 | 9600 | 0.0038 | - | | 1.9339 | 9650 | 0.0013 | - | | 1.9439 | 9700 | 0.0012 | - | | 1.9539 | 9750 | 0.0016 | - | | 1.9639 | 9800 | 0.0053 | - | | 1.9739 | 9850 | 0.0018 | - | | 1.9840 | 9900 | 0.0036 | - | | 1.9940 | 9950 | 0.0014 | - | | 2.0 | 9980 | - | 0.1052 | | 2.0040 | 10000 | 0.0012 | - | | 2.0140 | 10050 | 0.0014 | - | | 2.0240 | 10100 | 0.0013 | - | | 2.0341 | 10150 | 0.0014 | - | | 2.0441 | 10200 | 0.0012 | - | | 2.0541 | 10250 | 0.0014 | - | | 2.0641 | 10300 | 0.0013 | - | | 2.0741 | 10350 | 0.0012 | - | | 2.0842 | 10400 | 0.0013 | - | | 2.0942 | 10450 | 0.0033 | - | | 2.1042 | 10500 | 0.0013 | - | | 2.1142 | 10550 | 0.003 | - | | 2.1242 | 10600 | 0.0036 | - | | 2.1343 | 10650 | 0.0013 | - | | 2.1443 | 10700 | 0.0036 | - | | 2.1543 | 10750 | 0.0037 | - | | 2.1643 | 10800 | 0.0024 | - | | 2.1743 | 10850 | 0.0038 | - | | 2.1844 | 10900 | 0.0014 | - | | 2.1944 | 10950 | 0.0012 | - | | 2.2044 | 11000 | 0.0035 | - | | 2.2144 | 11050 | 0.0015 | - | | 2.2244 | 11100 | 0.0012 | - | | 2.2345 | 11150 | 0.0012 | - | | 2.2445 | 11200 | 0.0011 | - | | 2.2545 | 11250 | 0.0035 | - | | 2.2645 | 11300 | 0.0012 | - | | 2.2745 | 11350 | 0.0011 | - | | 2.2846 | 11400 | 0.0011 | - | | 2.2946 | 11450 | 0.0011 | - | | 2.3046 | 11500 | 0.0035 | - | | 2.3146 | 11550 | 0.0012 | - | | 2.3246 | 11600 | 0.0011 | - | | 2.3347 | 11650 | 0.0011 | - | | 2.3447 | 11700 | 0.0014 | - | | 2.3547 | 11750 | 0.0011 | - | | 2.3647 | 11800 | 0.0011 | - | | 2.3747 | 11850 | 0.0012 | - | | 2.3848 | 11900 | 0.0011 | - | | 2.3948 | 11950 | 0.001 | - | | 2.4048 | 12000 | 0.001 | - | | 2.4148 | 12050 | 0.0011 | - | | 2.4248 | 12100 | 0.0011 | - | | 2.4349 | 12150 | 0.0011 | - | | 2.4449 | 12200 | 0.001 | - | | 2.4549 | 12250 | 0.0034 | - | | 2.4649 | 12300 | 0.0011 | - | | 2.4749 | 12350 | 0.0013 | - | | 2.4850 | 12400 | 0.0012 | - | | 2.4950 | 12450 | 0.0015 | - | | 2.5050 | 12500 | 0.0011 | - | | 2.5150 | 12550 | 0.0034 | - | | 2.5251 | 12600 | 0.001 | - | | 2.5351 | 12650 | 0.0011 | - | | 2.5451 | 12700 | 0.0011 | - | | 2.5551 | 12750 | 0.001 | - | | 2.5651 | 12800 | 0.001 | - | | 2.5752 | 12850 | 0.0034 | - | | 2.5852 | 12900 | 0.0033 | - | | 2.5952 | 12950 | 0.0011 | - | | 2.6052 | 13000 | 0.001 | - | | 2.6152 | 13050 | 0.001 | - | | 2.6253 | 13100 | 0.0012 | - | | 2.6353 | 13150 | 0.0011 | - | | 2.6453 | 13200 | 0.0033 | - | | 2.6553 | 13250 | 0.0034 | - | | 2.6653 | 13300 | 0.001 | - | | 2.6754 | 13350 | 0.001 | - | | 2.6854 | 13400 | 0.0034 | - | | 2.6954 | 13450 | 0.001 | - | | 2.7054 | 13500 | 0.001 | - | | 2.7154 | 13550 | 0.001 | - | | 2.7255 | 13600 | 0.0009 | - | | 2.7355 | 13650 | 0.001 | - | | 2.7455 | 13700 | 0.001 | - | | 2.7555 | 13750 | 0.0009 | - | | 2.7655 | 13800 | 0.001 | - | | 2.7756 | 13850 | 0.0009 | - | | 2.7856 | 13900 | 0.0031 | - | | 2.7956 | 13950 | 0.001 | - | | 2.8056 | 14000 | 0.0031 | - | | 2.8156 | 14050 | 0.0033 | - | | 2.8257 | 14100 | 0.001 | - | | 2.8357 | 14150 | 0.0009 | - | | 2.8457 | 14200 | 0.0009 | - | | 2.8557 | 14250 | 0.0009 | - | | 2.8657 | 14300 | 0.001 | - | | 2.8758 | 14350 | 0.001 | - | | 2.8858 | 14400 | 0.0033 | - | | 2.8958 | 14450 | 0.001 | - | | 2.9058 | 14500 | 0.001 | - | | 2.9158 | 14550 | 0.001 | - | | 2.9259 | 14600 | 0.0033 | - | | 2.9359 | 14650 | 0.001 | - | | 2.9459 | 14700 | 0.0009 | - | | 2.9559 | 14750 | 0.001 | - | | 2.9659 | 14800 | 0.001 | - | | 2.9760 | 14850 | 0.0009 | - | | 2.9860 | 14900 | 0.0009 | - | | 2.9960 | 14950 | 0.0009 | - | | 3.0 | 14970 | - | 0.1077 | | 3.0060 | 15000 | 0.0033 | - | | 3.0160 | 15050 | 0.0009 | - | | 3.0261 | 15100 | 0.0009 | - | | 3.0361 | 15150 | 0.0009 | - | | 3.0461 | 15200 | 0.0009 | - | | 3.0561 | 15250 | 0.0008 | - | | 3.0661 | 15300 | 0.001 | - | | 3.0762 | 15350 | 0.0009 | - | | 3.0862 | 15400 | 0.0009 | - | | 3.0962 | 15450 | 0.0032 | - | | 3.1062 | 15500 | 0.0009 | - | | 3.1162 | 15550 | 0.0009 | - | | 3.1263 | 15600 | 0.0009 | - | | 3.1363 | 15650 | 0.0009 | - | | 3.1463 | 15700 | 0.0008 | - | | 3.1563 | 15750 | 0.0009 | - | | 3.1663 | 15800 | 0.0009 | - | | 3.1764 | 15850 | 0.0008 | - | | 3.1864 | 15900 | 0.0008 | - | | 3.1964 | 15950 | 0.0009 | - | | 3.2064 | 16000 | 0.0009 | - | | 3.2164 | 16050 | 0.0033 | - | | 3.2265 | 16100 | 0.0031 | - | | 3.2365 | 16150 | 0.0008 | - | | 3.2465 | 16200 | 0.0008 | - | | 3.2565 | 16250 | 0.0008 | - | | 3.2665 | 16300 | 0.0008 | - | | 3.2766 | 16350 | 0.0008 | - | | 3.2866 | 16400 | 0.0008 | - | | 3.2966 | 16450 | 0.0008 | - | | 3.3066 | 16500 | 0.0009 | - | | 3.3166 | 16550 | 0.0008 | - | | 3.3267 | 16600 | 0.0032 | - | | 3.3367 | 16650 | 0.0008 | - | | 3.3467 | 16700 | 0.0008 | - | | 3.3567 | 16750 | 0.0009 | - | | 3.3667 | 16800 | 0.0031 | - | | 3.3768 | 16850 | 0.0009 | - | | 3.3868 | 16900 | 0.0008 | - | | 3.3968 | 16950 | 0.0009 | - | | 3.4068 | 17000 | 0.0009 | - | | 3.4168 | 17050 | 0.0008 | - | | 3.4269 | 17100 | 0.0009 | - | | 3.4369 | 17150 | 0.0031 | - | | 3.4469 | 17200 | 0.0032 | - | | 3.4569 | 17250 | 0.0008 | - | | 3.4669 | 17300 | 0.0008 | - | | 3.4770 | 17350 | 0.0008 | - | | 3.4870 | 17400 | 0.0008 | - | | 3.4970 | 17450 | 0.0057 | - | | 3.5070 | 17500 | 0.0032 | - | | 3.5170 | 17550 | 0.0009 | - | | 3.5271 | 17600 | 0.0052 | - | | 3.5371 | 17650 | 0.0008 | - | | 3.5471 | 17700 | 0.0009 | - | | 3.5571 | 17750 | 0.0008 | - | | 3.5671 | 17800 | 0.0008 | - | | 3.5772 | 17850 | 0.0008 | - | | 3.5872 | 17900 | 0.0008 | - | | 3.5972 | 17950 | 0.0009 | - | | 3.6072 | 18000 | 0.0032 | - | | 3.6172 | 18050 | 0.0008 | - | | 3.6273 | 18100 | 0.0008 | - | | 3.6373 | 18150 | 0.0008 | - | | 3.6473 | 18200 | 0.0008 | - | | 3.6573 | 18250 | 0.0008 | - | | 3.6673 | 18300 | 0.0008 | - | | 3.6774 | 18350 | 0.0008 | - | | 3.6874 | 18400 | 0.0008 | - | | 3.6974 | 18450 | 0.0008 | - | | 3.7074 | 18500 | 0.0008 | - | | 3.7174 | 18550 | 0.0007 | - | | 3.7275 | 18600 | 0.0008 | - | | 3.7375 | 18650 | 0.0008 | - | | 3.7475 | 18700 | 0.003 | - | | 3.7575 | 18750 | 0.0008 | - | | 3.7675 | 18800 | 0.0008 | - | | 3.7776 | 18850 | 0.0008 | - | | 3.7876 | 18900 | 0.0007 | - | | 3.7976 | 18950 | 0.0008 | - | | 3.8076 | 19000 | 0.0007 | - | | 3.8176 | 19050 | 0.0007 | - | | 3.8277 | 19100 | 0.0029 | - | | 3.8377 | 19150 | 0.0007 | - | | 3.8477 | 19200 | 0.0008 | - | | 3.8577 | 19250 | 0.0031 | - | | 3.8677 | 19300 | 0.0007 | - | | 3.8778 | 19350 | 0.0007 | - | | 3.8878 | 19400 | 0.0008 | - | | 3.8978 | 19450 | 0.0008 | - | | 3.9078 | 19500 | 0.0031 | - | | 3.9178 | 19550 | 0.0008 | - | | 3.9279 | 19600 | 0.0008 | - | | 3.9379 | 19650 | 0.0007 | - | | 3.9479 | 19700 | 0.0008 | - | | 3.9579 | 19750 | 0.0008 | - | | 3.9679 | 19800 | 0.0008 | - | | 3.9780 | 19850 | 0.0008 | - | | 3.9880 | 19900 | 0.0008 | - | | 3.9980 | 19950 | 0.0007 | - | | 4.0 | 19960 | - | 0.1050 | ### Framework Versions - Python: 3.11.10 - SetFit: 1.1.0 - Sentence Transformers: 3.1.1 - Transformers: 4.45.2 - PyTorch: 2.4.1+cu124 - Datasets: 3.0.1 - 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} } ```