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 Type: SetFit
- Sentence Transformer body: sentence-transformers/all-mpnet-base-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 384 tokens
- Number of Classes: 3 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
Label | Examples |
---|---|
feature |
|
question |
|
bug |
|
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
# 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
@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}
}
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sentence-transformers/all-mpnet-base-v2