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: 6 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 |
---|---|
product policy |
|
general faq |
|
product discoverability |
|
Out of Scope |
|
order tracking |
|
product faq |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.8711 |
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("I like to listen to classical music")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 4 | 10.66 | 28 |
Label | Training Sample Count |
---|---|
Out of Scope | 50 |
general faq | 50 |
order tracking | 50 |
product discoverability | 50 |
product faq | 50 |
product policy | 50 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- 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: 42
- eval_max_steps: -1
- load_best_model_at_end: True
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0002 | 1 | 0.2592 | - |
0.0107 | 50 | 0.2424 | - |
0.0213 | 100 | 0.1506 | - |
0.0320 | 150 | 0.222 | - |
0.0427 | 200 | 0.1227 | - |
0.0533 | 250 | 0.1801 | - |
0.0640 | 300 | 0.1111 | - |
0.0747 | 350 | 0.0346 | - |
0.0853 | 400 | 0.0313 | - |
0.0960 | 450 | 0.0048 | - |
0.1067 | 500 | 0.0023 | - |
0.1173 | 550 | 0.0018 | - |
0.1280 | 600 | 0.0133 | - |
0.1387 | 650 | 0.0008 | - |
0.1493 | 700 | 0.0006 | - |
0.1600 | 750 | 0.0005 | - |
0.1706 | 800 | 0.0008 | - |
0.1813 | 850 | 0.0007 | - |
0.1920 | 900 | 0.0006 | - |
0.2026 | 950 | 0.0006 | - |
0.2133 | 1000 | 0.0003 | - |
0.2240 | 1050 | 0.0026 | - |
0.2346 | 1100 | 0.0004 | - |
0.2453 | 1150 | 0.0004 | - |
0.2560 | 1200 | 0.0004 | - |
0.2666 | 1250 | 0.0005 | - |
0.2773 | 1300 | 0.0005 | - |
0.2880 | 1350 | 0.0003 | - |
0.2986 | 1400 | 0.0001 | - |
0.3093 | 1450 | 0.0001 | - |
0.3200 | 1500 | 0.0002 | - |
0.3306 | 1550 | 0.0002 | - |
0.3413 | 1600 | 0.0002 | - |
0.3520 | 1650 | 0.0001 | - |
0.3626 | 1700 | 0.0004 | - |
0.3733 | 1750 | 0.0002 | - |
0.3840 | 1800 | 0.0005 | - |
0.3946 | 1850 | 0.0002 | - |
0.4053 | 1900 | 0.0002 | - |
0.4160 | 1950 | 0.0001 | - |
0.4266 | 2000 | 0.0001 | - |
0.4373 | 2050 | 0.0001 | - |
0.4480 | 2100 | 0.0001 | - |
0.4586 | 2150 | 0.0001 | - |
0.4693 | 2200 | 0.0002 | - |
0.4799 | 2250 | 0.0048 | - |
0.4906 | 2300 | 0.0001 | - |
0.5013 | 2350 | 0.001 | - |
0.5119 | 2400 | 0.0002 | - |
0.5226 | 2450 | 0.0002 | - |
0.5333 | 2500 | 0.0001 | - |
0.5439 | 2550 | 0.0001 | - |
0.5546 | 2600 | 0.0001 | - |
0.5653 | 2650 | 0.0001 | - |
0.5759 | 2700 | 0.0001 | - |
0.5866 | 2750 | 0.0001 | - |
0.5973 | 2800 | 0.0001 | - |
0.6079 | 2850 | 0.0001 | - |
0.6186 | 2900 | 0.0001 | - |
0.6293 | 2950 | 0.0001 | - |
0.6399 | 3000 | 0.0001 | - |
0.6506 | 3050 | 0.0001 | - |
0.6613 | 3100 | 0.0001 | - |
0.6719 | 3150 | 0.0001 | - |
0.6826 | 3200 | 0.0001 | - |
0.6933 | 3250 | 0.0001 | - |
0.7039 | 3300 | 0.0001 | - |
0.7146 | 3350 | 0.0001 | - |
0.7253 | 3400 | 0.0001 | - |
0.7359 | 3450 | 0.0001 | - |
0.7466 | 3500 | 0.0001 | - |
0.7573 | 3550 | 0.0001 | - |
0.7679 | 3600 | 0.0001 | - |
0.7786 | 3650 | 0.0001 | - |
0.7892 | 3700 | 0.0001 | - |
0.7999 | 3750 | 0.0001 | - |
0.8106 | 3800 | 0.0001 | - |
0.8212 | 3850 | 0.0 | - |
0.8319 | 3900 | 0.0001 | - |
0.8426 | 3950 | 0.0001 | - |
0.8532 | 4000 | 0.0001 | - |
0.8639 | 4050 | 0.0001 | - |
0.8746 | 4100 | 0.0001 | - |
0.8852 | 4150 | 0.0 | - |
0.8959 | 4200 | 0.0001 | - |
0.9066 | 4250 | 0.0001 | - |
0.9172 | 4300 | 0.0001 | - |
0.9279 | 4350 | 0.0001 | - |
0.9386 | 4400 | 0.0001 | - |
0.9492 | 4450 | 0.0001 | - |
0.9599 | 4500 | 0.0001 | - |
0.9706 | 4550 | 0.0001 | - |
0.9812 | 4600 | 0.0 | - |
0.9919 | 4650 | 0.0001 | - |
1.0026 | 4700 | 0.0 | - |
1.0132 | 4750 | 0.0001 | - |
1.0239 | 4800 | 0.0001 | - |
1.0346 | 4850 | 0.0001 | - |
1.0452 | 4900 | 0.0001 | - |
1.0559 | 4950 | 0.0001 | - |
1.0666 | 5000 | 0.0 | - |
1.0772 | 5050 | 0.0 | - |
1.0879 | 5100 | 0.0001 | - |
1.0985 | 5150 | 0.0 | - |
1.1092 | 5200 | 0.0 | - |
1.1199 | 5250 | 0.0 | - |
1.1305 | 5300 | 0.0001 | - |
1.1412 | 5350 | 0.0001 | - |
1.1519 | 5400 | 0.0 | - |
1.1625 | 5450 | 0.0001 | - |
1.1732 | 5500 | 0.0001 | - |
1.1839 | 5550 | 0.0002 | - |
1.1945 | 5600 | 0.0 | - |
1.2052 | 5650 | 0.0 | - |
1.2159 | 5700 | 0.0 | - |
1.2265 | 5750 | 0.0 | - |
1.2372 | 5800 | 0.0001 | - |
1.2479 | 5850 | 0.0001 | - |
1.2585 | 5900 | 0.0001 | - |
1.2692 | 5950 | 0.0 | - |
1.2799 | 6000 | 0.0 | - |
1.2905 | 6050 | 0.0 | - |
1.3012 | 6100 | 0.0001 | - |
1.3119 | 6150 | 0.0 | - |
1.3225 | 6200 | 0.0 | - |
1.3332 | 6250 | 0.0 | - |
1.3439 | 6300 | 0.0 | - |
1.3545 | 6350 | 0.0 | - |
1.3652 | 6400 | 0.0 | - |
1.3759 | 6450 | 0.0 | - |
1.3865 | 6500 | 0.0 | - |
1.3972 | 6550 | 0.0 | - |
1.4078 | 6600 | 0.0 | - |
1.4185 | 6650 | 0.0 | - |
1.4292 | 6700 | 0.0 | - |
1.4398 | 6750 | 0.0 | - |
1.4505 | 6800 | 0.0 | - |
1.4612 | 6850 | 0.0 | - |
1.4718 | 6900 | 0.0001 | - |
1.4825 | 6950 | 0.0001 | - |
1.4932 | 7000 | 0.0 | - |
1.5038 | 7050 | 0.0 | - |
1.5145 | 7100 | 0.0001 | - |
1.5252 | 7150 | 0.0001 | - |
1.5358 | 7200 | 0.0001 | - |
1.5465 | 7250 | 0.0001 | - |
1.5572 | 7300 | 0.0 | - |
1.5678 | 7350 | 0.0 | - |
1.5785 | 7400 | 0.0 | - |
1.5892 | 7450 | 0.0001 | - |
1.5998 | 7500 | 0.0 | - |
1.6105 | 7550 | 0.0 | - |
1.6212 | 7600 | 0.0 | - |
1.6318 | 7650 | 0.0 | - |
1.6425 | 7700 | 0.0 | - |
1.6532 | 7750 | 0.0 | - |
1.6638 | 7800 | 0.0 | - |
1.6745 | 7850 | 0.0 | - |
1.6852 | 7900 | 0.0 | - |
1.6958 | 7950 | 0.0 | - |
1.7065 | 8000 | 0.0 | - |
1.7172 | 8050 | 0.0 | - |
1.7278 | 8100 | 0.0 | - |
1.7385 | 8150 | 0.0001 | - |
1.7491 | 8200 | 0.0 | - |
1.7598 | 8250 | 0.0 | - |
1.7705 | 8300 | 0.0 | - |
1.7811 | 8350 | 0.0001 | - |
1.7918 | 8400 | 0.0 | - |
1.8025 | 8450 | 0.0 | - |
1.8131 | 8500 | 0.0 | - |
1.8238 | 8550 | 0.0 | - |
1.8345 | 8600 | 0.0001 | - |
1.8451 | 8650 | 0.0 | - |
1.8558 | 8700 | 0.0 | - |
1.8665 | 8750 | 0.0001 | - |
1.8771 | 8800 | 0.0 | - |
1.8878 | 8850 | 0.0 | - |
1.8985 | 8900 | 0.0 | - |
1.9091 | 8950 | 0.0001 | - |
1.9198 | 9000 | 0.0 | - |
1.9305 | 9050 | 0.0 | - |
1.9411 | 9100 | 0.0 | - |
1.9518 | 9150 | 0.0 | - |
1.9625 | 9200 | 0.0 | - |
1.9731 | 9250 | 0.0 | - |
1.9838 | 9300 | 0.0 | - |
1.9945 | 9350 | 0.0 | - |
Framework Versions
- Python: 3.10.16
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.2
- PyTorch: 2.2.2
- Datasets: 2.19.1
- 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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