--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: - text: 'Building TopazMarket Prev AptosLabs Founder AptosNames All views posts and opinions shared are my own Not financial advice ' - text: 'Founder FrequenC__ an awardwinning marketing agency for the next internet Mentor speaker cat mom Tweets are my own opinion libertylabsxyz ' - text: No1 ExchangeIndonesia Pertama Terdaftar dan Teregulasi di Bappebti CS Live Chat 247 Jakarta Capital Region - text: producer business and elsewhere on leave views my own la gran manzana - text: Founder GainForestNow CoLead ETHBiodivX CL ClimateChangeAI PhD ETH prevGermanyHong_Kong_SAR_ChinaVietnam Son of Hoa refugees hehim Zurich Switzerland pipeline_tag: text-classification inference: true base_model: BAAI/bge-small-en-v1.5 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: 0.5565092989985694 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:** 28 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 | |:---------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | UNDETERMINED | | | NFT_ARTIST | | | ONCHAIN_ANALYST | | | BUSINESS_DEVELOPER | | | NFT_COLLECTOR | | | DEVELOPER | | | TRADER | | | COMMUNITY_MANAGER | | | SECURITY_AUDITOR | | | VENTURE_CAPITALIST | | | INVESTOR | | | ANGEL_INVESTOR | | | EXECUTIVE | | | MARKETER | | | DATA_SCIENTIST | | | EDUCATOR | | | INFLUENCER | | | ADVISOR | | | BLOGGER | | | RESEARCHER | | | METAVERSE_ENTHUSIAST | | | NODE_OPERATOR | | | LAWYER | | | DATA_ANALYST | | | MINER | | | SHITCOINER | | | FINANCIAL_ANALYST | | | BUSINESS_ANALYST | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.5565 | ## 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("kasparas12/crypto_individual_infer_model_setfit") # Run inference preds = model("producer business and elsewhere on leave views my own la gran manzana") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 1 | 13.3415 | 65 | | Label | Training Sample Count | |:---------------------|:----------------------| | DEVELOPER | 2111 | | DATA_SCIENTIST | 93 | | DATA_ANALYST | 25 | | NODE_OPERATOR | 71 | | MINER | 47 | | SECURITY_AUDITOR | 352 | | INVESTOR | 484 | | ANGEL_INVESTOR | 160 | | VENTURE_CAPITALIST | 941 | | TRADER | 270 | | SHITCOINER | 88 | | BUSINESS_DEVELOPER | 917 | | BUSINESS_ANALYST | 1 | | COMMUNITY_MANAGER | 401 | | MARKETER | 190 | | FINANCIAL_ANALYST | 72 | | ADVISOR | 150 | | RESEARCHER | 691 | | ONCHAIN_ANALYST | 45 | | EXECUTIVE | 741 | | INFLUENCER | 834 | | LAWYER | 137 | | BLOGGER | 198 | | NFT_COLLECTOR | 335 | | NFT_ARTIST | 598 | | EDUCATOR | 281 | | METAVERSE_ENTHUSIAST | 132 | | UNDETERMINED | 2216 | ### Training Hyperparameters - batch_size: (64, 64) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 20 - 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 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0001 | 1 | 0.2625 | - | | 0.0064 | 50 | 0.2677 | - | | 0.0127 | 100 | 0.2515 | - | | 0.0191 | 150 | 0.2413 | - | | 0.0254 | 200 | 0.2374 | - | | 0.0318 | 250 | 0.2383 | - | | 0.0381 | 300 | 0.222 | - | | 0.0445 | 350 | 0.1972 | - | | 0.0509 | 400 | 0.2268 | - | | 0.0572 | 450 | 0.2333 | - | | 0.0636 | 500 | 0.199 | - | | 0.0699 | 550 | 0.2035 | - | | 0.0763 | 600 | 0.1676 | - | | 0.0827 | 650 | 0.1566 | - | | 0.0890 | 700 | 0.1909 | - | | 0.0954 | 750 | 0.189 | - | | 0.1017 | 800 | 0.1872 | - | | 0.1081 | 850 | 0.1576 | - | | 0.1144 | 900 | 0.1382 | - | | 0.1208 | 950 | 0.1603 | - | | 0.1272 | 1000 | 0.155 | - | | 0.1335 | 1050 | 0.1764 | - | | 0.1399 | 1100 | 0.1506 | - | | 0.1462 | 1150 | 0.1439 | - | | 0.1526 | 1200 | 0.1581 | - | | 0.1590 | 1250 | 0.1494 | - | | 0.1653 | 1300 | 0.1622 | - | | 0.1717 | 1350 | 0.1503 | - | | 0.1780 | 1400 | 0.1094 | - | | 0.1844 | 1450 | 0.1576 | - | | 0.1907 | 1500 | 0.1194 | - | | 0.1971 | 1550 | 0.1515 | - | | 0.2035 | 1600 | 0.1662 | - | | 0.2098 | 1650 | 0.1642 | - | | 0.2162 | 1700 | 0.0943 | - | | 0.2225 | 1750 | 0.1472 | - | | 0.2289 | 1800 | 0.1622 | - | | 0.2352 | 1850 | 0.0809 | - | | 0.2416 | 1900 | 0.1623 | - | | 0.2480 | 1950 | 0.1444 | - | | 0.2543 | 2000 | 0.1304 | - | | 0.2607 | 2050 | 0.1175 | - | | 0.2670 | 2100 | 0.078 | - | | 0.2734 | 2150 | 0.1189 | - | | 0.2798 | 2200 | 0.141 | - | | 0.2861 | 2250 | 0.1233 | - | | 0.2925 | 2300 | 0.1446 | - | | 0.2988 | 2350 | 0.1076 | - | | 0.3052 | 2400 | 0.1016 | - | | 0.3115 | 2450 | 0.0818 | - | | 0.3179 | 2500 | 0.1384 | - | | 0.3243 | 2550 | 0.1065 | - | | 0.3306 | 2600 | 0.1029 | - | | 0.3370 | 2650 | 0.1227 | - | | 0.3433 | 2700 | 0.0982 | - | | 0.3497 | 2750 | 0.0959 | - | | 0.3561 | 2800 | 0.0851 | - | | 0.3624 | 2850 | 0.1028 | - | | 0.3688 | 2900 | 0.1136 | - | | 0.3751 | 2950 | 0.1111 | - | | 0.3815 | 3000 | 0.115 | - | | 0.3878 | 3050 | 0.1183 | - | | 0.3942 | 3100 | 0.0689 | - | | 0.4006 | 3150 | 0.1004 | - | | 0.4069 | 3200 | 0.1079 | - | | 0.4133 | 3250 | 0.112 | - | | 0.4196 | 3300 | 0.0758 | - | | 0.4260 | 3350 | 0.09 | - | | 0.4323 | 3400 | 0.1267 | - | | 0.4387 | 3450 | 0.1024 | - | | 0.4451 | 3500 | 0.1352 | - | | 0.4514 | 3550 | 0.0681 | - | | 0.4578 | 3600 | 0.0483 | - | | 0.4641 | 3650 | 0.0937 | - | | 0.4705 | 3700 | 0.0744 | - | | 0.4769 | 3750 | 0.0926 | - | | 0.4832 | 3800 | 0.0764 | - | | 0.4896 | 3850 | 0.0814 | - | | 0.4959 | 3900 | 0.108 | - | | 0.5023 | 3950 | 0.0936 | - | | 0.5086 | 4000 | 0.0687 | - | | 0.5150 | 4050 | 0.0607 | - | | 0.5214 | 4100 | 0.0829 | - | | 0.5277 | 4150 | 0.0772 | - | | 0.5341 | 4200 | 0.0309 | - | | 0.5404 | 4250 | 0.0797 | - | | 0.5468 | 4300 | 0.063 | - | | 0.5532 | 4350 | 0.071 | - | | 0.5595 | 4400 | 0.0667 | - | | 0.5659 | 4450 | 0.121 | - | | 0.5722 | 4500 | 0.0565 | - | | 0.5786 | 4550 | 0.0915 | - | | 0.5849 | 4600 | 0.0613 | - | | 0.5913 | 4650 | 0.0479 | - | | 0.5977 | 4700 | 0.0622 | - | | 0.6040 | 4750 | 0.0687 | - | | 0.6104 | 4800 | 0.0635 | - | | 0.6167 | 4850 | 0.1233 | - | | 0.6231 | 4900 | 0.0351 | - | | 0.6295 | 4950 | 0.0717 | - | | 0.6358 | 5000 | 0.0906 | - | | 0.6422 | 5050 | 0.0712 | - | | 0.6485 | 5100 | 0.1133 | - | | 0.6549 | 5150 | 0.0757 | - | | 0.6612 | 5200 | 0.0809 | - | | 0.6676 | 5250 | 0.112 | - | | 0.6740 | 5300 | 0.0893 | - | | 0.6803 | 5350 | 0.0591 | - | | 0.6867 | 5400 | 0.0872 | - | | 0.6930 | 5450 | 0.0937 | - | | 0.6994 | 5500 | 0.038 | - | | 0.7057 | 5550 | 0.0793 | - | | 0.7121 | 5600 | 0.0569 | - | | 0.7185 | 5650 | 0.0861 | - | | 0.7248 | 5700 | 0.1022 | - | | 0.7312 | 5750 | 0.0759 | - | | 0.7375 | 5800 | 0.0451 | - | | 0.7439 | 5850 | 0.08 | - | | 0.7503 | 5900 | 0.058 | - | | 0.7566 | 5950 | 0.0423 | - | | 0.7630 | 6000 | 0.043 | - | | 0.7693 | 6050 | 0.109 | - | | 0.7757 | 6100 | 0.072 | - | | 0.7820 | 6150 | 0.0342 | - | | 0.7884 | 6200 | 0.0833 | - | | 0.7948 | 6250 | 0.0643 | - | | 0.8011 | 6300 | 0.1069 | - | | 0.8075 | 6350 | 0.0713 | - | | 0.8138 | 6400 | 0.0807 | - | | 0.8202 | 6450 | 0.0518 | - | | 0.8266 | 6500 | 0.0796 | - | | 0.8329 | 6550 | 0.0954 | - | | 0.8393 | 6600 | 0.0709 | - | | 0.8456 | 6650 | 0.0541 | - | | 0.8520 | 6700 | 0.0503 | - | | 0.8583 | 6750 | 0.0737 | - | | 0.8647 | 6800 | 0.0931 | - | | 0.8711 | 6850 | 0.0636 | - | | 0.8774 | 6900 | 0.0579 | - | | 0.8838 | 6950 | 0.1168 | - | | 0.8901 | 7000 | 0.0751 | - | | 0.8965 | 7050 | 0.0945 | - | | 0.9028 | 7100 | 0.0396 | - | | 0.9092 | 7150 | 0.0623 | - | | 0.9156 | 7200 | 0.0641 | - | | 0.9219 | 7250 | 0.0697 | - | | 0.9283 | 7300 | 0.0675 | - | | 0.9346 | 7350 | 0.0544 | - | | 0.9410 | 7400 | 0.0803 | - | | 0.9474 | 7450 | 0.0549 | - | | 0.9537 | 7500 | 0.0612 | - | | 0.9601 | 7550 | 0.0721 | - | | 0.9664 | 7600 | 0.0692 | - | | 0.9728 | 7650 | 0.07 | - | | 0.9791 | 7700 | 0.0476 | - | | 0.9855 | 7750 | 0.0673 | - | | 0.9919 | 7800 | 0.0606 | - | | 0.9982 | 7850 | 0.1001 | - | ### Framework Versions - Python: 3.9.16 - SetFit: 1.0.3 - Sentence Transformers: 2.2.2 - Transformers: 4.21.3 - PyTorch: 1.12.1+cu116 - Datasets: 2.4.0 - Tokenizers: 0.12.1 ## 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} } ```