SetFit with sentence-transformers/all-mpnet-base-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
Model Sources
Model Labels
| Label |
Examples |
| feature |
- 'imshow速度太慢，如何能加快imshow ### Descripe the feature and motivation\n\nimshow速度太慢，如何能加快imshow\n\n### Additional context\n\n_No response_'
- 'Add H264 / H265 writter support for Android ### Pull Request Readiness Checklist\r\n\r\nSee details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request\r\n\r\n- [x] I agree to contribute to the project under Apache 2 License.\r\n- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV\r\n- [x] The PR is proposed to the proper branch\r\n- [ ] There is a reference to the original bug report and related work\r\n- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable\r\n Patch to opencv_extra has the same branch name.\r\n- [ ] The feature is well documented and sample code can be built with the project CMake\r\n'
- '4-bit_palette_color ### Pull Request Readiness Checklist\r\n\r\nSee details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request\r\n\r\n- [x] I agree to contribute to the project under Apache 2 License.\r\n- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV\r\n- [x] The PR is proposed to the proper branch\r\n- [ ] There is a reference to the original bug report and related work\r\n- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable\r\n Patch to opencv_extra has the same branch name.\r\n- [ ] The feature is well documented and sample code can be built with the project CMake\r\n'
|
| bug |
- 'DNN: support the split node of onnx opset >= 13 Merge with test case: https://github.com/opencv/opencv_extra/pull/1053.\r\n\r\nThe attribute of
split in Split layer has been moved from attribute to input.\r\nRelated link: https://github.com/onnx/onnx/blob/main/docs/Operators.md#inputs-1---2-12\r\nThe purpose of this PR is to support the split with input type.\r\n\r\n### Pull Request Readiness Checklist\r\n\r\nSee details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request\r\n\r\n- [x] I agree to contribute to the project under Apache 2 License.\r\n- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV\r\n- [x] The PR is proposed to the proper branch\r\n- [ ] There is a reference to the original bug report and related work\r\n- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable\r\n Patch to opencv_extra has the same branch name.\r\n- [ ] The feature is well documented and sample code can be built with the project CMake\r\n' - "Fixed bug when MSMF webcamera doesn't start when build with VIDEOIO_PLUGIN_ALL Fixed #23937 and #23056\r\n"
- "Fixes pixel info color font for dark Qt themes For dark Qt themes, it is hard to read the pixel color information on the bottom left, like the coordinates or RGB values. This PR proposes a way on how the dynamically sets the font colors based on the system's theme.\r\nOriginal Example:\r\n\r\n
 \r\n\r\nWith patch:\r\n\r\n \r\n\r\n\r\nFor Windows, nothing is changed (tested on a windows 11 system), because the font color is #000000 when using the default Qt theme.\r\n\r\n### Pull Request Readiness Checklist\r\n\r\nSee details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request\r\n\r\n- [x] I agree to contribute to the project under Apache 2 License.\r\n- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV\r\n- [x] The PR is proposed to the proper branch\r\n- [ ] There is a reference to the original bug report and related work\r\n- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable\r\n Patch to opencv_extra has the same branch name.\r\n- [ ] The feature is well documented and sample code can be built with the project CMake\r\n"
|
| question |
- 'In file included from /home/xxx/opencv/modules/python/src2/cv2.cpp:11:0: /home/xxx/opencv/build/modules/python_bindings_generator/pyopencv_generated_include.h:19:10: fatal error: opencv2/hdf/hdf5.hpp: no file or directory #include "opencv2/hdf/hdf5.hpp" ^~~~~~~~~~~~~~~~~~~~~~ I'm building opencv-cuda,it happens at the end.'
- "Opencv error with GPU backend \r\n\r\n##### System information (version)\r\n\r\n- OpenCV => 4.5.5-dev\r\n- Operating System / Platform => Ubuntu 20\r\n- Compiler => GCC\r\n\r\n##### Detailed description\r\n\r\nI cannot run anymore my cv2.dnn on GPU. \r\nI tried to run the code in CPU and its workings. I also tested with the older version of OpenCV and is working. I think the new commit merged in February changed the dnn output is an np.array[] and is not compatible with 4.5.5 release version. Could you please give an example of how to use the new output?\r\n\r\n##### Steps to reproduce\r\n
.python\r\n //with CUDA enabled\r\n net = cv2.dnn.readNetFromONNX(model_yolo_v5))\r\n net.setInput(_image)\r\n results = net.forward()\r\n \r\n##### Issue submission checklist\r\n\r\n - [ ] I report the issue, it's not a question\r\n \r\n - [x] I checked the problem with documentation, FAQ, open issues,\r\n forum.opencv.org, Stack Overflow, etc and have not found any solution\r\n \r\n - [x] I updated to the latest OpenCV version and the issue is still there\r\n \r\n - [ ] There is reproducer code and related data files: videos, images, onnx, etc\r\n \r\n" - 'cv2.imread() fails when filenames have multiple dots (.) \r\n\r\n##### System information (version)\r\n\r\n\r\n- OpenCV => 4.5.5.64\r\n- Operating System / Platform => Windows\r\n- Compiler => PyCharm Community Edition 2020.1.1\r\n\r\n##### Detailed description\r\n\r\nWhen the
cv2.imread() method encounters a filename with multiple dots (.), a NoneType is returned. I think this might be because the file extension is assumed to be everything after the first dot, and not the last, but it could be something completely different.\r\n\r\nExample: \r\n\r\n##### Steps to reproduce\r\n\r\n\r\nRunning the following code, with two images in the same directory,\r\n.py\r\nimport cv2\r\n\r\nim = cv2.imread("C:/Users/.../Videos/Captures/test project – main.py 18_05_2022 15_44_50.png")\r\nim2 = cv2.imread("C:/Users/.../Videos/Captures/Screenshot 16_05_2022 23_40_07.png")\r\n\r\nprint(im)\r\nprint(im2)\r\n\r\ngives the result:\r\n.py\r\n[ WARN:0@0.221] global D:\\\\a\\\\opencv-python\\\\opencv-python\\\\opencv\\\\modules\\\\imgcodecs\\\\src\\\\loadsave.cpp (239) cv::findDecoder imread_(\'C:/Users/.../Videos/Captures/test project – main.py 18_05_2022 15_44_50.png\'): can\'t open/read file: check file path/integrity\r\nNone\r\n[[[255 255 255]\r\n [255 255 255]\r\n [255 255 255]\r\n ...\r\n [255 255 255]\r\n [255 255 255]\r\n [255 255 255]]\r\n\r\n [[255 255 255]\r\n [255 255 255]\r\n [255 255 255]\r\n ...\r\n [255 255 255]\r\n [255 255 255]\r\n [255 255 255]]\r\n\r\n [[153 139 140]\r\n [152 139 139]\r\n [151 139 138]\r\n ...\r\n [148 136 135]\r\n [150 138 138]\r\n [151 140 139]]]\r\n\r\nwhich shows that the first image (one dot) loaded successfully, but the second one (with the multiple dots) failed.\r\n##### Issue submission checklist\r\n\r\n - [x] I report the issue, it's not a question\r\n \r\n - [x] I checked the problem with documentation, FAQ, open issues,\r\n forum.opencv.org, Stack Overflow, etc and have not found any solution\r\n \r\n - [x] I updated to the latest OpenCV version and the issue is still there\r\n \r\n - [x] There is reproducer code and related data files: videos, images, onnx, etc\r\n \r\n'
|
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
model = SetFitModel.from_pretrained("setfit_model_id")
preds = model("OpenCV build - cmake don't recognize GTK2 or GTK3 ##### System information (version)
- OpenCV => 4.7.0
- Operating System / Platform => Ubuntu 20.04
- Compiler => g++ (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
- C++ Standard: 11
##### Detailed description
I am trying to create OpenCV with the GTK GUI. I have `GTK2` and `GTK3` installed on the system, but neither version is recognised by cmake.
The output after executing cmake remains
**GUI: NONE**
**GTK+:NO**
I execute the cmake command as follows:
`cmake -DPYTHON_DEFAULT_EXECUTABLE=$(which python3) -DWITH_GTK=ON ../opencv`
or
`cmake -DPYTHON_DEFAULT_EXECUTABLE=$(which python3) -DWITH_GTK=ON -DWITH_GTK_2_X=ON ../opencv`
##### Steps to reproduce
git clone https://github.com/opencv/opencv.git
mkdir -p build && cd build
cmake ../opencv (default, custom look above)
Thank you for your help
")
Training Details
Training Set Metrics
| Training set |
Min |
Median |
Max |
| Word count |
2 |
393.0933 |
13648 |
| 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 |
0.2963 |
- |
| 0.0067 |
10 |
0.2694 |
- |
| 0.0133 |
20 |
0.2443 |
- |
| 0.02 |
30 |
0.2872 |
- |
| 0.0267 |
40 |
0.2828 |
- |
| 0.0333 |
50 |
0.2279 |
- |
| 0.04 |
60 |
0.1852 |
- |
| 0.0467 |
70 |
0.1983 |
- |
| 0.0533 |
80 |
0.224 |
- |
| 0.06 |
90 |
0.2315 |
- |
| 0.0667 |
100 |
0.2698 |
- |
| 0.0733 |
110 |
0.1178 |
- |
| 0.08 |
120 |
0.1216 |
- |
| 0.0867 |
130 |
0.1065 |
- |
| 0.0933 |
140 |
0.1519 |
- |
| 0.1 |
150 |
0.073 |
- |
| 0.1067 |
160 |
0.0858 |
- |
| 0.1133 |
170 |
0.1697 |
- |
| 0.12 |
180 |
0.1062 |
- |
| 0.1267 |
190 |
0.0546 |
- |
| 0.1333 |
200 |
0.139 |
- |
| 0.14 |
210 |
0.0513 |
- |
| 0.1467 |
220 |
0.1168 |
- |
| 0.1533 |
230 |
0.0455 |
- |
| 0.16 |
240 |
0.0521 |
- |
| 0.1667 |
250 |
0.0307 |
- |
| 0.1733 |
260 |
0.0618 |
- |
| 0.18 |
270 |
0.0964 |
- |
| 0.1867 |
280 |
0.0707 |
- |
| 0.1933 |
290 |
0.0524 |
- |
| 0.2 |
300 |
0.0358 |
- |
| 0.2067 |
310 |
0.0238 |
- |
| 0.2133 |
320 |
0.0759 |
- |
| 0.22 |
330 |
0.0197 |
- |
| 0.2267 |
340 |
0.0053 |
- |
| 0.2333 |
350 |
0.0035 |
- |
| 0.24 |
360 |
0.0036 |
- |
| 0.2467 |
370 |
0.0079 |
- |
| 0.2533 |
380 |
0.0033 |
- |
| 0.26 |
390 |
0.0021 |
- |
| 0.2667 |
400 |
0.0026 |
- |
| 0.2733 |
410 |
0.0018 |
- |
| 0.28 |
420 |
0.0014 |
- |
| 0.2867 |
430 |
0.0019 |
- |
| 0.2933 |
440 |
0.0027 |
- |
| 0.3 |
450 |
0.0016 |
- |
| 0.3067 |
460 |
0.0027 |
- |
| 0.3133 |
470 |
0.0095 |
- |
| 0.32 |
480 |
0.0005 |
- |
| 0.3267 |
490 |
0.0006 |
- |
| 0.3333 |
500 |
0.0006 |
- |
| 0.34 |
510 |
0.0634 |
- |
| 0.3467 |
520 |
0.0025 |
- |
| 0.3533 |
530 |
0.0013 |
- |
| 0.36 |
540 |
0.0007 |
- |
| 0.3667 |
550 |
0.0007 |
- |
| 0.3733 |
560 |
0.0003 |
- |
| 0.38 |
570 |
0.0006 |
- |
| 0.3867 |
580 |
0.0007 |
- |
| 0.3933 |
590 |
0.0004 |
- |
| 0.4 |
600 |
0.0006 |
- |
| 0.4067 |
610 |
0.0007 |
- |
| 0.4133 |
620 |
0.0005 |
- |
| 0.42 |
630 |
0.0004 |
- |
| 0.4267 |
640 |
0.0005 |
- |
| 0.4333 |
650 |
0.0013 |
- |
| 0.44 |
660 |
0.0005 |
- |
| 0.4467 |
670 |
0.0007 |
- |
| 0.4533 |
680 |
0.0008 |
- |
| 0.46 |
690 |
0.0018 |
- |
| 0.4667 |
700 |
0.0007 |
- |
| 0.4733 |
710 |
0.0008 |
- |
| 0.48 |
720 |
0.0007 |
- |
| 0.4867 |
730 |
0.0007 |
- |
| 0.4933 |
740 |
0.0002 |
- |
| 0.5 |
750 |
0.0002 |
- |
| 0.5067 |
760 |
0.0002 |
- |
| 0.5133 |
770 |
0.0007 |
- |
| 0.52 |
780 |
0.0004 |
- |
| 0.5267 |
790 |
0.0003 |
- |
| 0.5333 |
800 |
0.0007 |
- |
| 0.54 |
810 |
0.0004 |
- |
| 0.5467 |
820 |
0.0003 |
- |
| 0.5533 |
830 |
0.0002 |
- |
| 0.56 |
840 |
0.001 |
- |
| 0.5667 |
850 |
0.008 |
- |
| 0.5733 |
860 |
0.0003 |
- |
| 0.58 |
870 |
0.0002 |
- |
| 0.5867 |
880 |
0.0011 |
- |
| 0.5933 |
890 |
0.0005 |
- |
| 0.6 |
900 |
0.0004 |
- |
| 0.6067 |
910 |
0.0003 |
- |
| 0.6133 |
920 |
0.0002 |
- |
| 0.62 |
930 |
0.0002 |
- |
| 0.6267 |
940 |
0.0002 |
- |
| 0.6333 |
950 |
0.0001 |
- |
| 0.64 |
960 |
0.0002 |
- |
| 0.6467 |
970 |
0.0003 |
- |
| 0.6533 |
980 |
0.0002 |
- |
| 0.66 |
990 |
0.0005 |
- |
| 0.6667 |
1000 |
0.0003 |
- |
| 0.6733 |
1010 |
0.0002 |
- |
| 0.68 |
1020 |
0.0003 |
- |
| 0.6867 |
1030 |
0.0008 |
- |
| 0.6933 |
1040 |
0.0003 |
- |
| 0.7 |
1050 |
0.0005 |
- |
| 0.7067 |
1060 |
0.0012 |
- |
| 0.7133 |
1070 |
0.0001 |
- |
| 0.72 |
1080 |
0.0003 |
- |
| 0.7267 |
1090 |
0.0002 |
- |
| 0.7333 |
1100 |
0.0001 |
- |
| 0.74 |
1110 |
0.0003 |
- |
| 0.7467 |
1120 |
0.0002 |
- |
| 0.7533 |
1130 |
0.0003 |
- |
| 0.76 |
1140 |
0.0596 |
- |
| 0.7667 |
1150 |
0.0012 |
- |
| 0.7733 |
1160 |
0.0004 |
- |
| 0.78 |
1170 |
0.0003 |
- |
| 0.7867 |
1180 |
0.0003 |
- |
| 0.7933 |
1190 |
0.0002 |
- |
| 0.8 |
1200 |
0.0015 |
- |
| 0.8067 |
1210 |
0.0002 |
- |
| 0.8133 |
1220 |
0.0001 |
- |
| 0.82 |
1230 |
0.0002 |
- |
| 0.8267 |
1240 |
0.0002 |
- |
| 0.8333 |
1250 |
0.0002 |
- |
| 0.84 |
1260 |
0.0002 |
- |
| 0.8467 |
1270 |
0.0003 |
- |
| 0.8533 |
1280 |
0.0001 |
- |
| 0.86 |
1290 |
0.0001 |
- |
| 0.8667 |
1300 |
0.0002 |
- |
| 0.8733 |
1310 |
0.0004 |
- |
| 0.88 |
1320 |
0.0004 |
- |
| 0.8867 |
1330 |
0.0004 |
- |
| 0.8933 |
1340 |
0.0001 |
- |
| 0.9 |
1350 |
0.0002 |
- |
| 0.9067 |
1360 |
0.055 |
- |
| 0.9133 |
1370 |
0.0002 |
- |
| 0.92 |
1380 |
0.0004 |
- |
| 0.9267 |
1390 |
0.0001 |
- |
| 0.9333 |
1400 |
0.0002 |
- |
| 0.94 |
1410 |
0.0002 |
- |
| 0.9467 |
1420 |
0.0004 |
- |
| 0.9533 |
1430 |
0.0009 |
- |
| 0.96 |
1440 |
0.0003 |
- |
| 0.9667 |
1450 |
0.0427 |
- |
| 0.9733 |
1460 |
0.0004 |
- |
| 0.98 |
1470 |
0.0001 |
- |
| 0.9867 |
1480 |
0.0002 |
- |
| 0.9933 |
1490 |
0.0002 |
- |
| 1.0 |
1500 |
0.0002 |
- |
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
@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}
}
