SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 2 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 |
---|---|
tax_form |
|
bank_statement |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 1.0 |
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("EphronM/setfit-txtClassifier_v1")
# Run inference
preds = model("2106 employee buslness expenses omb 1545 0139 form separate instruction 1988 department treasury attach form 1040 internal revenue service x attachment sociai security number occupation expense incur stevens w. upton a91 80 l 1362 r0ustabout 1 employee business expenses column step 1 enter expenses column 8 meals meals entertainment entertainment 1 vehicle expense il line 15 line 22 .. 3294 2 parking fee toll local transportation include train bus etc 3 trave expense away home include lodging airplane car rental etc include meal entertainment 3 4 business expense include line 1 3 include meal entertainment 5 meals entertainment expense instruction 6 add line 1 5 enter total expense r29 note reimburse expense step 1 skip line 7 13 enter line 6 line 14 step 2 figure excess reimbursements report income 7 reimbursements expense list step 1 employer report form w-2 form 1099 .. 2272 note column line 6 line 7 skip line 8 9 57 line 10 excess reimbursement 8 subtract line 6 trom line 7 zero enter zero 9 add line 8 column enter total excess relmbursement reportable income add form 1040 line 7 .00 step 3 figure fully deductible reimbursed expenses 10 subtract jine 7 line 6 zero enter zero 1022 11 reimbursements allowance expense step 1 4 0 00 employer identify include form w-2 form 1099 subject withhold tax note enter line 11 report income form 1040 12 enter small line 10 line 11 0 13 add line 12 column enter total tully deductible .00 relmburse expense aiso enter total form 1040 line 24 13 0 00 step 4 figure unreimbursed expense deduct schedule form 1040 14 subtract line 12 line 10 14 0 note column line i4 zero stop 00 15 enter 20 .20 line 14 column b 15 16 subtract line 15 trom line 14 16 1022 17 add line 16 column enter total enter total schedule 42 a(form 1040 line 20 qualified performing artists handicapped employee instruction 1022 paperwork reduction act notice instruction 42 form 2106 1988 189")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 162 | 329.4 | 512 |
Label | Training Sample Count |
---|---|
bank_statement | 250 |
tax_form | 250 |
Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 16)
- 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
- seed: 25411
- run_name: setfit_early_stopping
- eval_max_steps: -1
- load_best_model_at_end: True
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0001 | 1 | 0.1597 | - |
0.0006 | 10 | 0.1126 | - |
0.0013 | 20 | 0.1567 | - |
0.0019 | 30 | 0.1785 | - |
0.0025 | 40 | 0.1455 | - |
0.0032 | 50 | 0.1325 | - |
0.0038 | 60 | 0.0818 | - |
0.0045 | 70 | 0.1227 | - |
0.0051 | 80 | 0.0901 | - |
0.0057 | 90 | 0.0573 | - |
0.0064 | 100 | 0.028 | - |
0.0070 | 110 | 0.0237 | - |
0.0076 | 120 | 0.0194 | - |
0.0083 | 130 | 0.0136 | - |
0.0089 | 140 | 0.0035 | - |
0.0096 | 150 | 0.0027 | - |
0.0102 | 160 | 0.0036 | - |
0.0108 | 170 | 0.0024 | - |
0.0115 | 180 | 0.0026 | - |
0.0121 | 190 | 0.002 | - |
0.0127 | 200 | 0.0009 | - |
0.0134 | 210 | 0.0017 | - |
0.0140 | 220 | 0.0015 | - |
0.0147 | 230 | 0.0008 | - |
0.0153 | 240 | 0.0005 | - |
0.0159 | 250 | 0.0005 | - |
0.0166 | 260 | 0.001 | - |
0.0172 | 270 | 0.0006 | - |
0.0178 | 280 | 0.0003 | - |
0.0185 | 290 | 0.0007 | - |
0.0191 | 300 | 0.0008 | - |
0.0198 | 310 | 0.0003 | - |
0.0204 | 320 | 0.0006 | - |
0.0210 | 330 | 0.0002 | - |
0.0217 | 340 | 0.0003 | - |
0.0223 | 350 | 0.0004 | - |
0.0229 | 360 | 0.0002 | - |
0.0236 | 370 | 0.0002 | - |
0.0242 | 380 | 0.0001 | - |
0.0249 | 390 | 0.0001 | - |
0.0255 | 400 | 0.0002 | - |
0.0261 | 410 | 0.0001 | - |
0.0268 | 420 | 0.0002 | - |
0.0274 | 430 | 0.0001 | - |
0.0280 | 440 | 0.0001 | - |
0.0287 | 450 | 0.0001 | - |
0.0293 | 460 | 0.0003 | - |
0.0300 | 470 | 0.0001 | - |
0.0306 | 480 | 0.0001 | - |
0.0312 | 490 | 0.0001 | - |
0.0319 | 500 | 0.0001 | - |
0.0325 | 510 | 0.0002 | - |
0.0331 | 520 | 0.0004 | - |
0.0338 | 530 | 0.0002 | - |
0.0344 | 540 | 0.0001 | - |
0.0351 | 550 | 0.0 | - |
0.0357 | 560 | 0.0001 | - |
0.0363 | 570 | 0.0001 | - |
0.0370 | 580 | 0.0 | - |
0.0376 | 590 | 0.0001 | - |
0.0382 | 600 | 0.0 | - |
0.0389 | 610 | 0.0 | - |
0.0395 | 620 | 0.0 | - |
0.0402 | 630 | 0.0002 | - |
0.0408 | 640 | 0.0001 | - |
0.0414 | 650 | 0.0 | - |
0.0421 | 660 | 0.0001 | - |
0.0427 | 670 | 0.0001 | - |
0.0433 | 680 | 0.0 | - |
0.0440 | 690 | 0.0001 | - |
0.0446 | 700 | 0.0 | - |
0.0453 | 710 | 0.0001 | - |
0.0459 | 720 | 0.0 | - |
0.0465 | 730 | 0.0 | - |
0.0472 | 740 | 0.0 | - |
0.0478 | 750 | 0.0001 | - |
0.0484 | 760 | 0.0001 | - |
0.0491 | 770 | 0.0 | - |
0.0497 | 780 | 0.0 | - |
0.0504 | 790 | 0.0 | - |
0.0510 | 800 | 0.0 | - |
0.0516 | 810 | 0.0 | - |
0.0523 | 820 | 0.0 | - |
0.0529 | 830 | 0.0 | - |
0.0535 | 840 | 0.0 | - |
0.0542 | 850 | 0.0 | - |
0.0548 | 860 | 0.0 | - |
0.0555 | 870 | 0.0 | - |
0.0561 | 880 | 0.0 | - |
0.0567 | 890 | 0.0 | - |
0.0574 | 900 | 0.0 | - |
0.0580 | 910 | 0.0 | - |
0.0586 | 920 | 0.0 | - |
0.0593 | 930 | 0.0 | - |
0.0599 | 940 | 0.0 | - |
0.0606 | 950 | 0.0 | - |
0.0612 | 960 | 0.0 | - |
0.0618 | 970 | 0.0 | - |
0.0625 | 980 | 0.0 | - |
0.0631 | 990 | 0.0 | - |
0.0637 | 1000 | 0.0 | 0.0 |
0.0644 | 1010 | 0.0 | - |
0.0650 | 1020 | 0.0 | - |
0.0657 | 1030 | 0.0 | - |
0.0663 | 1040 | 0.0 | - |
0.0669 | 1050 | 0.0001 | - |
0.0676 | 1060 | 0.0 | - |
0.0682 | 1070 | 0.0 | - |
0.0688 | 1080 | 0.0 | - |
0.0695 | 1090 | 0.0 | - |
0.0701 | 1100 | 0.0 | - |
0.0708 | 1110 | 0.0 | - |
0.0714 | 1120 | 0.0 | - |
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0.0733 | 1150 | 0.0 | - |
0.0739 | 1160 | 0.0 | - |
0.0746 | 1170 | 0.0 | - |
0.0752 | 1180 | 0.0 | - |
0.0759 | 1190 | 0.0 | - |
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0.0771 | 1210 | 0.0 | - |
0.0778 | 1220 | 0.0 | - |
0.0784 | 1230 | 0.0 | - |
0.0790 | 1240 | 0.0 | - |
0.0797 | 1250 | 0.0 | - |
0.0803 | 1260 | 0.0 | - |
0.0810 | 1270 | 0.0 | - |
0.0816 | 1280 | 0.0 | - |
0.0822 | 1290 | 0.0 | - |
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0.0956 | 1500 | 0.0 | - |
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0.0994 | 1560 | 0.0 | - |
0.1001 | 1570 | 0.0 | - |
0.1007 | 1580 | 0.0 | - |
0.1014 | 1590 | 0.0 | - |
0.1020 | 1600 | 0.0 | - |
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0.1103 | 1730 | 0.0 | - |
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0.1122 | 1760 | 0.0 | - |
0.1128 | 1770 | 0.0 | - |
0.1135 | 1780 | 0.0 | - |
0.1141 | 1790 | 0.0 | - |
0.1147 | 1800 | 0.0 | - |
0.1154 | 1810 | 0.0 | - |
0.1160 | 1820 | 0.0 | - |
0.1166 | 1830 | 0.0 | - |
0.1173 | 1840 | 0.0 | - |
0.1179 | 1850 | 0.0 | - |
0.1186 | 1860 | 0.0 | - |
0.1192 | 1870 | 0.0 | - |
0.1198 | 1880 | 0.0 | - |
0.1205 | 1890 | 0.0 | - |
0.1211 | 1900 | 0.0 | - |
0.1217 | 1910 | 0.0 | - |
0.1224 | 1920 | 0.0 | - |
0.1230 | 1930 | 0.0 | - |
0.1237 | 1940 | 0.0 | - |
0.1243 | 1950 | 0.0 | - |
0.1249 | 1960 | 0.0 | - |
0.1256 | 1970 | 0.0 | - |
0.1262 | 1980 | 0.0 | - |
0.1268 | 1990 | 0.0 | - |
0.1275 | 2000 | 0.0 | 0.0 |
0.1281 | 2010 | 0.0 | - |
0.1288 | 2020 | 0.0 | - |
0.1294 | 2030 | 0.0 | - |
0.1300 | 2040 | 0.0 | - |
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0.1594 | 2500 | 0.0 | - |
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0.1702 | 2670 | 0.0 | - |
0.1708 | 2680 | 0.0 | - |
0.1715 | 2690 | 0.0 | - |
0.1721 | 2700 | 0.0 | - |
0.1727 | 2710 | 0.0 | - |
0.1734 | 2720 | 0.0 | - |
0.1740 | 2730 | 0.0 | - |
0.1747 | 2740 | 0.0 | - |
0.1753 | 2750 | 0.0 | - |
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0.1766 | 2770 | 0.0 | - |
0.1772 | 2780 | 0.0 | - |
0.1778 | 2790 | 0.0 | - |
0.1785 | 2800 | 0.0 | - |
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0.1829 | 2870 | 0.0 | - |
0.1836 | 2880 | 0.0 | - |
0.1842 | 2890 | 0.0 | - |
0.1849 | 2900 | 0.0 | - |
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0.1861 | 2920 | 0.0 | - |
0.1868 | 2930 | 0.0 | - |
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0.1880 | 2950 | 0.0 | - |
0.1887 | 2960 | 0.0 | - |
0.1893 | 2970 | 0.0 | - |
0.1900 | 2980 | 0.0 | - |
0.1906 | 2990 | 0.0 | - |
0.1912 | 3000 | 0.0 | 0.0 |
0.1919 | 3010 | 0.0 | - |
0.1925 | 3020 | 0.0 | - |
0.1931 | 3030 | 0.0 | - |
0.1938 | 3040 | 0.0 | - |
0.1944 | 3050 | 0.0 | - |
0.1951 | 3060 | 0.0 | - |
0.1957 | 3070 | 0.0 | - |
0.1963 | 3080 | 0.0 | - |
0.1970 | 3090 | 0.0 | - |
0.1976 | 3100 | 0.0 | - |
0.1982 | 3110 | 0.0 | - |
0.1989 | 3120 | 0.0 | - |
0.1995 | 3130 | 0.0 | - |
0.2002 | 3140 | 0.0 | - |
0.2008 | 3150 | 0.0 | - |
0.2014 | 3160 | 0.0 | - |
0.2021 | 3170 | 0.0 | - |
0.2027 | 3180 | 0.0 | - |
0.2033 | 3190 | 0.0 | - |
0.2040 | 3200 | 0.0 | - |
0.2046 | 3210 | 0.0 | - |
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0.2065 | 3240 | 0.0 | - |
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0.2862 | 4490 | 0.0 | - |
0.2868 | 4500 | 0.0 | - |
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0.9351 | 14670 | 0.0 | - |
0.9357 | 14680 | 0.0 | - |
0.9364 | 14690 | 0.0 | - |
0.9370 | 14700 | 0.0 | - |
0.9377 | 14710 | 0.0 | - |
0.9383 | 14720 | 0.0 | - |
0.9389 | 14730 | 0.0 | - |
0.9396 | 14740 | 0.0 | - |
0.9402 | 14750 | 0.0 | - |
0.9408 | 14760 | 0.0 | - |
0.9415 | 14770 | 0.0 | - |
0.9421 | 14780 | 0.0 | - |
0.9428 | 14790 | 0.0 | - |
0.9434 | 14800 | 0.0 | - |
0.9440 | 14810 | 0.0 | - |
0.9447 | 14820 | 0.0 | - |
0.9453 | 14830 | 0.0 | - |
0.9459 | 14840 | 0.0 | - |
0.9466 | 14850 | 0.0 | - |
0.9472 | 14860 | 0.0 | - |
0.9479 | 14870 | 0.0 | - |
0.9485 | 14880 | 0.0 | - |
0.9491 | 14890 | 0.0 | - |
0.9498 | 14900 | 0.0 | - |
0.9504 | 14910 | 0.0 | - |
0.9510 | 14920 | 0.0 | - |
0.9517 | 14930 | 0.0 | - |
0.9523 | 14940 | 0.0 | - |
0.9530 | 14950 | 0.0 | - |
0.9536 | 14960 | 0.0 | - |
0.9542 | 14970 | 0.0 | - |
0.9549 | 14980 | 0.0 | - |
0.9555 | 14990 | 0.0 | - |
0.9561 | 15000 | 0.0 | 0.0 |
0.9568 | 15010 | 0.0 | - |
0.9574 | 15020 | 0.0 | - |
0.9581 | 15030 | 0.0 | - |
0.9587 | 15040 | 0.0 | - |
0.9593 | 15050 | 0.0 | - |
0.9600 | 15060 | 0.0 | - |
0.9606 | 15070 | 0.0 | - |
0.9612 | 15080 | 0.0 | - |
0.9619 | 15090 | 0.0 | - |
0.9625 | 15100 | 0.0 | - |
0.9632 | 15110 | 0.0 | - |
0.9638 | 15120 | 0.0 | - |
0.9644 | 15130 | 0.0 | - |
0.9651 | 15140 | 0.0 | - |
0.9657 | 15150 | 0.0 | - |
0.9663 | 15160 | 0.0 | - |
0.9670 | 15170 | 0.0 | - |
0.9676 | 15180 | 0.0 | - |
0.9683 | 15190 | 0.0 | - |
0.9689 | 15200 | 0.0 | - |
0.9695 | 15210 | 0.0 | - |
0.9702 | 15220 | 0.0 | - |
0.9708 | 15230 | 0.0 | - |
0.9714 | 15240 | 0.0 | - |
0.9721 | 15250 | 0.0 | - |
0.9727 | 15260 | 0.0 | - |
0.9734 | 15270 | 0.0 | - |
0.9740 | 15280 | 0.0 | - |
0.9746 | 15290 | 0.0 | - |
0.9753 | 15300 | 0.0 | - |
0.9759 | 15310 | 0.0 | - |
0.9765 | 15320 | 0.0 | - |
0.9772 | 15330 | 0.0 | - |
0.9778 | 15340 | 0.0 | - |
0.9785 | 15350 | 0.0 | - |
0.9791 | 15360 | 0.0 | - |
0.9797 | 15370 | 0.0 | - |
0.9804 | 15380 | 0.0 | - |
0.9810 | 15390 | 0.0 | - |
0.9816 | 15400 | 0.0 | - |
0.9823 | 15410 | 0.0 | - |
0.9829 | 15420 | 0.0 | - |
0.9836 | 15430 | 0.0 | - |
0.9842 | 15440 | 0.0 | - |
0.9848 | 15450 | 0.0 | - |
0.9855 | 15460 | 0.0 | - |
0.9861 | 15470 | 0.0 | - |
0.9867 | 15480 | 0.0 | - |
0.9874 | 15490 | 0.0 | - |
0.9880 | 15500 | 0.0 | - |
0.9887 | 15510 | 0.0 | - |
0.9893 | 15520 | 0.0 | - |
0.9899 | 15530 | 0.0 | - |
0.9906 | 15540 | 0.0 | - |
0.9912 | 15550 | 0.0 | - |
0.9918 | 15560 | 0.0 | - |
0.9925 | 15570 | 0.0 | - |
0.9931 | 15580 | 0.0 | - |
0.9938 | 15590 | 0.0 | - |
0.9944 | 15600 | 0.0 | - |
0.9950 | 15610 | 0.0 | - |
0.9957 | 15620 | 0.0 | - |
0.9963 | 15630 | 0.0 | - |
0.9969 | 15640 | 0.0 | - |
0.9976 | 15650 | 0.0 | - |
0.9982 | 15660 | 0.0 | - |
0.9989 | 15670 | 0.0 | - |
0.9995 | 15680 | 0.0 | - |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.14
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.0.1
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu121
- Datasets: 2.21.0
- Tokenizers: 0.19.1
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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