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 ClassifierChain 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 ClassifierChain instance
- Maximum Sequence Length: 512 tokens
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Evaluation
Metrics
Label | Metric |
---|---|
all | 0.6569 |
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("CrisisNarratives/setfit-13classes-multi_label")
# Run inference
preds = model("my dad had huge ones..so they may be real..")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 1 | 25.8891 | 1681 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 40
- body_learning_rate: (1.752e-05, 1.752e-05)
- head_learning_rate: 1.752e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 30
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0004 | 1 | 0.3059 | - |
0.0185 | 50 | 0.3597 | - |
0.0370 | 100 | 0.272 | - |
0.0555 | 150 | 0.2282 | - |
0.0739 | 200 | 0.2413 | - |
0.0924 | 250 | 0.2239 | - |
0.1109 | 300 | 0.2447 | - |
0.1294 | 350 | 0.1574 | - |
0.1479 | 400 | 0.1873 | - |
0.1664 | 450 | 0.1537 | - |
0.1848 | 500 | 0.1661 | - |
0.2033 | 550 | 0.1692 | - |
0.2218 | 600 | 0.1105 | - |
0.2403 | 650 | 0.1316 | - |
0.2588 | 700 | 0.1018 | - |
0.2773 | 750 | 0.1148 | - |
0.2957 | 800 | 0.0588 | - |
0.3142 | 850 | 0.2385 | - |
0.3327 | 900 | 0.0302 | - |
0.3512 | 950 | 0.0714 | - |
0.3697 | 1000 | 0.1587 | - |
0.3882 | 1050 | 0.1479 | - |
0.4067 | 1100 | 0.0897 | - |
0.4251 | 1150 | 0.064 | - |
0.4436 | 1200 | 0.0774 | - |
0.4621 | 1250 | 0.0318 | - |
0.4806 | 1300 | 0.1231 | - |
0.4991 | 1350 | 0.0983 | - |
0.5176 | 1400 | 0.1537 | - |
0.5360 | 1450 | 0.1382 | - |
0.5545 | 1500 | 0.1244 | - |
0.5730 | 1550 | 0.1169 | - |
0.5915 | 1600 | 0.0185 | - |
0.6100 | 1650 | 0.1368 | - |
0.6285 | 1700 | 0.0678 | - |
0.6470 | 1750 | 0.0827 | - |
0.6654 | 1800 | 0.028 | - |
0.6839 | 1850 | 0.0655 | - |
0.7024 | 1900 | 0.1099 | - |
0.7209 | 1950 | 0.0508 | - |
0.7394 | 2000 | 0.086 | - |
0.7579 | 2050 | 0.1087 | - |
0.7763 | 2100 | 0.0764 | - |
0.7948 | 2150 | 0.0646 | - |
0.8133 | 2200 | 0.0793 | - |
0.8318 | 2250 | 0.0678 | - |
0.8503 | 2300 | 0.0538 | - |
0.8688 | 2350 | 0.0495 | - |
0.8872 | 2400 | 0.0651 | - |
0.9057 | 2450 | 0.0966 | - |
0.9242 | 2500 | 0.1726 | - |
0.9427 | 2550 | 0.0491 | - |
0.9612 | 2600 | 0.043 | - |
0.9797 | 2650 | 0.0807 | - |
0.9982 | 2700 | 0.0905 | - |
1.0166 | 2750 | 0.0841 | - |
1.0351 | 2800 | 0.0735 | - |
1.0536 | 2850 | 0.0508 | - |
1.0721 | 2900 | 0.082 | - |
1.0906 | 2950 | 0.085 | - |
1.1091 | 3000 | 0.0412 | - |
1.1275 | 3050 | 0.0274 | - |
1.1460 | 3100 | 0.1012 | - |
1.1645 | 3150 | 0.0269 | - |
1.1830 | 3200 | 0.0377 | - |
1.2015 | 3250 | 0.0854 | - |
1.2200 | 3300 | 0.0854 | - |
1.2384 | 3350 | 0.0682 | - |
1.2569 | 3400 | 0.038 | - |
1.2754 | 3450 | 0.1073 | - |
1.2939 | 3500 | 0.0841 | - |
1.3124 | 3550 | 0.1024 | - |
1.3309 | 3600 | 0.0636 | - |
1.3494 | 3650 | 0.0821 | - |
1.3678 | 3700 | 0.0742 | - |
1.3863 | 3750 | 0.0504 | - |
1.4048 | 3800 | 0.1198 | - |
1.4233 | 3850 | 0.0233 | - |
1.4418 | 3900 | 0.0659 | - |
1.4603 | 3950 | 0.0252 | - |
1.4787 | 4000 | 0.0772 | - |
1.4972 | 4050 | 0.0466 | - |
1.5157 | 4100 | 0.0771 | - |
1.5342 | 4150 | 0.0489 | - |
1.5527 | 4200 | 0.0273 | - |
1.5712 | 4250 | 0.0335 | - |
1.5896 | 4300 | 0.0733 | - |
1.6081 | 4350 | 0.0323 | - |
1.6266 | 4400 | 0.0358 | - |
1.6451 | 4450 | 0.0252 | - |
1.6636 | 4500 | 0.078 | - |
1.6821 | 4550 | 0.0137 | - |
1.7006 | 4600 | 0.0858 | - |
1.7190 | 4650 | 0.0377 | - |
1.7375 | 4700 | 0.0607 | - |
1.7560 | 4750 | 0.0438 | - |
1.7745 | 4800 | 0.0501 | - |
1.7930 | 4850 | 0.0682 | - |
1.8115 | 4900 | 0.0571 | - |
1.8299 | 4950 | 0.0144 | - |
1.8484 | 5000 | 0.0518 | - |
1.8669 | 5050 | 0.0388 | - |
1.8854 | 5100 | 0.0685 | - |
1.9039 | 5150 | 0.0522 | - |
1.9224 | 5200 | 0.0518 | - |
1.9409 | 5250 | 0.0649 | - |
1.9593 | 5300 | 0.083 | - |
1.9778 | 5350 | 0.0652 | - |
1.9963 | 5400 | 0.0907 | - |
2.0148 | 5450 | 0.0767 | - |
2.0333 | 5500 | 0.0825 | - |
2.0518 | 5550 | 0.0818 | - |
2.0702 | 5600 | 0.0364 | - |
2.0887 | 5650 | 0.134 | - |
2.1072 | 5700 | 0.0379 | - |
2.1257 | 5750 | 0.1066 | - |
2.1442 | 5800 | 0.1288 | - |
2.1627 | 5850 | 0.0527 | - |
2.1811 | 5900 | 0.0343 | - |
2.1996 | 5950 | 0.0766 | - |
2.2181 | 6000 | 0.0862 | - |
2.2366 | 6050 | 0.0661 | - |
2.2551 | 6100 | 0.069 | - |
2.2736 | 6150 | 0.0429 | - |
2.2921 | 6200 | 0.0546 | - |
2.3105 | 6250 | 0.1237 | - |
2.3290 | 6300 | 0.0337 | - |
2.3475 | 6350 | 0.0616 | - |
2.3660 | 6400 | 0.0833 | - |
2.3845 | 6450 | 0.1074 | - |
2.4030 | 6500 | 0.0424 | - |
2.4214 | 6550 | 0.033 | - |
2.4399 | 6600 | 0.0933 | - |
2.4584 | 6650 | 0.0434 | - |
2.4769 | 6700 | 0.0328 | - |
2.4954 | 6750 | 0.0553 | - |
2.5139 | 6800 | 0.0557 | - |
2.5323 | 6850 | 0.0861 | - |
2.5508 | 6900 | 0.0294 | - |
2.5693 | 6950 | 0.0521 | - |
2.5878 | 7000 | 0.1529 | - |
2.6063 | 7050 | 0.055 | - |
2.6248 | 7100 | 0.0522 | - |
2.6433 | 7150 | 0.0715 | - |
2.6617 | 7200 | 0.0524 | - |
2.6802 | 7250 | 0.0469 | - |
2.6987 | 7300 | 0.1064 | - |
2.7172 | 7350 | 0.0485 | - |
2.7357 | 7400 | 0.0526 | - |
2.7542 | 7450 | 0.1063 | - |
2.7726 | 7500 | 0.0549 | - |
2.7911 | 7550 | 0.041 | - |
2.8096 | 7600 | 0.0312 | - |
2.8281 | 7650 | 0.0249 | - |
2.8466 | 7700 | 0.0807 | - |
2.8651 | 7750 | 0.0268 | - |
2.8835 | 7800 | 0.0306 | - |
2.9020 | 7850 | 0.0655 | - |
2.9205 | 7900 | 0.1469 | - |
2.9390 | 7950 | 0.0454 | - |
2.9575 | 8000 | 0.0754 | - |
2.9760 | 8050 | 0.0587 | - |
2.9945 | 8100 | 0.0452 | - |
Framework Versions
- Python: 3.9.16
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.0
- PyTorch: 2.1.0+cu121
- Datasets: 2.14.6
- Tokenizers: 0.14.1
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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