--- base_model: sentence-transformers/all-mpnet-base-v2 library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: "[Feature Request] Add workbench action to split editor terminal below \r\n\r\n\ \r\n\r\n\r\n\r\nI currently have the following actions\ \ available:\r\n\r\n```\r\nworkbench.action.createTerminalEditor\r\nworkbench.action.createTerminalEditorSameGroup\r\ \nworkbench.action.createTerminalEditorSide\r\n```\r\n\r\nI'd like to be able\ \ to split an editor terminal below. It seems like the UI can do this, because\ \ I can drag a terminal editor below another and have a horizontal split:\r\n\r\ \n\"image\"\r\n\r\nSo I would love to have a `workbench.action.createTerminalEditorBelow`\ \ action to split it below \U0001F60A" - text: "Notebook toolbar foreground color cannot be modified by custom styles: The\ \ unavailable foreground color has been marked with a red arrow, please see the\ \ image\r\n\r\n\r\nExpected behavior:\r\nNotebook toolbar foreground color can\ \ be modified through custom styles.\r\n\r\nUnexpected behavior:\r\nNotebook toolbar\ \ foreground color cannot be modified through custom styles.\r\n\r\nVS Code Version:\ \ 1.81 | 1.82\r\nOS Version: Windows10\r\n" - text: "Where are the Extensions Stored? \r\nType: Performance Issue\r\n\r\ \nI need to know where are the VS Code Extensions stored. It is not in the VS\ \ Code subdirectory.\r\n\r\nVS Code version: Code 1.82.2 (abd2f3db4bdb28f9e95536dfa84d8479f1eb312d,\ \ 2023-09-14T05:55:25.390Z)\r\nOS version: Windows_NT x64 10.0.22621\r\nModes:\r\ \n\r\n
\r\nSystem Info\r\n\r\n|Item|Value|\r\n|---|---|\r\ \n|CPUs|11th Gen Intel(R) Core(TM) i5-1135G7 @ 2.40GHz (8 x 2419)|\r\n|GPU Status|2d_canvas:\ \ enabled
canvas_oop_rasterization: enabled_on
direct_rendering_display_compositor:\ \ disabled_off_ok
gpu_compositing: enabled
multiple_raster_threads: enabled_on
opengl:\ \ enabled_on
rasterization: enabled
raw_draw: disabled_off_ok
video_decode:\ \ enabled
video_encode: enabled
vulkan: disabled_off
webgl: enabled
webgl2:\ \ enabled
webgpu: enabled|\r\n|Load (avg)|undefined|\r\n|Memory (System)|15.77GB\ \ (8.81GB free)|\r\n|Process Argv|--crash-reporter-id d28106b3-cd04-490a-b194-f819821f7d80|\r\ \n|Screen Reader|no|\r\n|VM|0%|\r\n
\r\nProcess Info\r\ \n\r\n" - text: 'Terminal multiple action icons overlap Does this issue occur when all extensions are disabled?: Yes/No - VS Code Version: Insiders - OS Version: macOS ![Image](https://github.com/microsoft/vscode/assets/876920/421798b0-dd84-4399-b7eb-ceab65ffdb0a) ' - text: "Custom view container menu action contribute \r\n\r\n\r\n\r\n\r\n\r\nI want to be able to add actions to custom view container.\r\ \nI've tried : \r\n```json\r\n\"commands\" : [\r\n {\r\n \"command\": \"test_command_id\"\ ,\r\n \"title\": \"Test command\",\r\n \"icon\": \"$(zap)\"\r\n }\r\n],\r\ \n\"viewsContainers\" : {\r\n \"activitybar\": [\r\n {\r\n \"id\": \"\ custiom_view_container\",\r\n \"title\": \"Test title\",\r\n \"icon\"\ : \"$(zap)\"\r\n }\r\n ]\r\n},\r\n\"menus\": {\r\n \"view/title\": [\r\n\ \ {\r\n \"command\": \"test_command_id\",\r\n \"when\": \"view ==\ \ custiom_view_container\",\r\n \"group\": \"navigation\"\r\n }\r\n ],\r\ \n \"custiom_view_container/title\": [\r\n {\r\n \"command\": \"test_command_id\"\ ,\r\n \"when\": \"true\",\r\n \"group\": \"navigation\"\r\n }\r\n\ \ ]\r\n}\r\n\r\n```\r\nNon of this works, however i am able to add command for\ \ built in containers : \r\n```json\r\n\"menus\": {\r\n \"scm/title\": [\r\n\ \ {\r\n \"command\": \"test_command_id\",\r\n \"when\": \"true\"\ ,\r\n \"group\": \"navigation\"\r\n }\r\n ]\r\n}\r\n```\r\n\r\n" inference: true --- # 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:** 3 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 | |:---------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | feature | | | question | | | bug | | ## 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("setfit_model_id") # Run inference preds = model("Notebook toolbar foreground color cannot be modified by custom styles: The unavailable foreground color has been marked with a red arrow, please see the image Expected behavior: Notebook toolbar foreground color can be modified through custom styles. Unexpected behavior: Notebook toolbar foreground color cannot be modified through custom styles. VS Code Version: 1.81 | 1.82 OS Version: Windows10 ") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:---------|:-----| | Word count | 5 | 118.4567 | 1482 | | Label | Training Sample Count | |:---------|:----------------------| | bug | 200 | | feature | 200 | | question | 200 | ### Training Hyperparameters - batch_size: (16, 2) - 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 | |:------:|:----:|:-------------:|:---------------:| | 0.0007 | 1 | 0.2896 | - | | 0.0067 | 10 | 0.262 | - | | 0.0133 | 20 | 0.2299 | - | | 0.02 | 30 | 0.2345 | - | | 0.0267 | 40 | 0.235 | - | | 0.0333 | 50 | 0.2213 | - | | 0.04 | 60 | 0.3084 | - | | 0.0467 | 70 | 0.2107 | - | | 0.0533 | 80 | 0.1596 | - | | 0.06 | 90 | 0.1916 | - | | 0.0667 | 100 | 0.2366 | - | | 0.0733 | 110 | 0.1513 | - | | 0.08 | 120 | 0.1281 | - | | 0.0867 | 130 | 0.2217 | - | | 0.0933 | 140 | 0.1859 | - | | 0.1 | 150 | 0.1835 | - | | 0.1067 | 160 | 0.1312 | - | | 0.1133 | 170 | 0.1415 | - | | 0.12 | 180 | 0.1287 | - | | 0.1267 | 190 | 0.1377 | - | | 0.1333 | 200 | 0.1116 | - | | 0.14 | 210 | 0.0769 | - | | 0.1467 | 220 | 0.0548 | - | | 0.1533 | 230 | 0.0647 | - | | 0.16 | 240 | 0.0348 | - | | 0.1667 | 250 | 0.0165 | - | | 0.1733 | 260 | 0.0043 | - | | 0.18 | 270 | 0.0038 | - | | 0.1867 | 280 | 0.0673 | - | | 0.1933 | 290 | 0.0458 | - | | 0.2 | 300 | 0.0048 | - | | 0.2067 | 310 | 0.0054 | - | | 0.2133 | 320 | 0.0019 | - | | 0.22 | 330 | 0.0052 | - | | 0.2267 | 340 | 0.0103 | - | | 0.2333 | 350 | 0.0163 | - | | 0.24 | 360 | 0.0022 | - | | 0.2467 | 370 | 0.0009 | - | | 0.2533 | 380 | 0.0006 | - | | 0.26 | 390 | 0.001 | - | | 0.2667 | 400 | 0.0011 | - | | 0.2733 | 410 | 0.0005 | - | | 0.28 | 420 | 0.0007 | - | | 0.2867 | 430 | 0.0006 | - | | 0.2933 | 440 | 0.0005 | - | | 0.3 | 450 | 0.0012 | - | | 0.3067 | 460 | 0.0006 | - | | 0.3133 | 470 | 0.0004 | - | | 0.32 | 480 | 0.0006 | - | | 0.3267 | 490 | 0.0009 | - | | 0.3333 | 500 | 0.001 | - | | 0.34 | 510 | 0.0003 | - | | 0.3467 | 520 | 0.0003 | - | | 0.3533 | 530 | 0.0005 | - | | 0.36 | 540 | 0.0002 | - | | 0.3667 | 550 | 0.0004 | - | | 0.3733 | 560 | 0.0603 | - | | 0.38 | 570 | 0.0014 | - | | 0.3867 | 580 | 0.0007 | - | | 0.3933 | 590 | 0.0005 | - | | 0.4 | 600 | 0.0004 | - | | 0.4067 | 610 | 0.0053 | - | | 0.4133 | 620 | 0.0002 | - | | 0.42 | 630 | 0.0002 | - | | 0.4267 | 640 | 0.0008 | - | | 0.4333 | 650 | 0.0001 | - | | 0.44 | 660 | 0.0002 | - | | 0.4467 | 670 | 0.0001 | - | | 0.4533 | 680 | 0.0002 | - | | 0.46 | 690 | 0.0002 | - | | 0.4667 | 700 | 0.0001 | - | | 0.4733 | 710 | 0.0003 | - | | 0.48 | 720 | 0.0001 | - | | 0.4867 | 730 | 0.0001 | - | | 0.4933 | 740 | 0.0002 | - | | 0.5 | 750 | 0.0001 | - | | 0.5067 | 760 | 0.0002 | - | | 0.5133 | 770 | 0.0002 | - | | 0.52 | 780 | 0.0001 | - | | 0.5267 | 790 | 0.0001 | - | | 0.5333 | 800 | 0.0001 | - | | 0.54 | 810 | 0.0001 | - | | 0.5467 | 820 | 0.0002 | - | | 0.5533 | 830 | 0.0001 | - | | 0.56 | 840 | 0.0001 | - | | 0.5667 | 850 | 0.0001 | - | | 0.5733 | 860 | 0.0002 | - | | 0.58 | 870 | 0.0001 | - | | 0.5867 | 880 | 0.0002 | - | | 0.5933 | 890 | 0.0002 | - | | 0.6 | 900 | 0.0002 | - | | 0.6067 | 910 | 0.0001 | - | | 0.6133 | 920 | 0.0001 | - | | 0.62 | 930 | 0.0001 | - | | 0.6267 | 940 | 0.0001 | - | | 0.6333 | 950 | 0.0001 | - | | 0.64 | 960 | 0.0001 | - | | 0.6467 | 970 | 0.0001 | - | | 0.6533 | 980 | 0.0001 | - | | 0.66 | 990 | 0.0001 | - | | 0.6667 | 1000 | 0.0001 | - | | 0.6733 | 1010 | 0.0001 | - | | 0.68 | 1020 | 0.0001 | - | | 0.6867 | 1030 | 0.0001 | - | | 0.6933 | 1040 | 0.0001 | - | | 0.7 | 1050 | 0.0001 | - | | 0.7067 | 1060 | 0.0002 | - | | 0.7133 | 1070 | 0.0001 | - | | 0.72 | 1080 | 0.0001 | - | | 0.7267 | 1090 | 0.0001 | - | | 0.7333 | 1100 | 0.0001 | - | | 0.74 | 1110 | 0.0002 | - | | 0.7467 | 1120 | 0.0001 | - | | 0.7533 | 1130 | 0.0001 | - | | 0.76 | 1140 | 0.0001 | - | | 0.7667 | 1150 | 0.0001 | - | | 0.7733 | 1160 | 0.0001 | - | | 0.78 | 1170 | 0.0001 | - | | 0.7867 | 1180 | 0.0001 | - | | 0.7933 | 1190 | 0.0002 | - | | 0.8 | 1200 | 0.0001 | - | | 0.8067 | 1210 | 0.0001 | - | | 0.8133 | 1220 | 0.0001 | - | | 0.82 | 1230 | 0.0001 | - | | 0.8267 | 1240 | 0.0 | - | | 0.8333 | 1250 | 0.0 | - | | 0.84 | 1260 | 0.0002 | - | | 0.8467 | 1270 | 0.0001 | - | | 0.8533 | 1280 | 0.0001 | - | | 0.86 | 1290 | 0.0001 | - | | 0.8667 | 1300 | 0.0001 | - | | 0.8733 | 1310 | 0.0001 | - | | 0.88 | 1320 | 0.0001 | - | | 0.8867 | 1330 | 0.0 | - | | 0.8933 | 1340 | 0.0001 | - | | 0.9 | 1350 | 0.0001 | - | | 0.9067 | 1360 | 0.0001 | - | | 0.9133 | 1370 | 0.0001 | - | | 0.92 | 1380 | 0.0001 | - | | 0.9267 | 1390 | 0.0001 | - | | 0.9333 | 1400 | 0.0001 | - | | 0.94 | 1410 | 0.0 | - | | 0.9467 | 1420 | 0.0001 | - | | 0.9533 | 1430 | 0.0001 | - | | 0.96 | 1440 | 0.0001 | - | | 0.9667 | 1450 | 0.0001 | - | | 0.9733 | 1460 | 0.0001 | - | | 0.98 | 1470 | 0.0001 | - | | 0.9867 | 1480 | 0.0001 | - | | 0.9933 | 1490 | 0.0001 | - | | 1.0 | 1500 | 0.0001 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 3.0.1 - Transformers: 4.39.0 - PyTorch: 2.3.0+cu121 - Datasets: 2.20.0 - Tokenizers: 0.15.2 ## 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} } ```