SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-MiniLM-L3-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-MiniLM-L3-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 128 tokens
- Number of Classes: 44 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 |
|---|---|
| 28 |
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| 33 |
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| 2 |
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| 42 |
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| 31 |
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| 9 |
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| 1 |
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| 25 |
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| 12 |
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| 8 |
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| 24 |
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| 13 |
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| 30 |
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| 5 |
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| 43 |
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| 14 |
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| 36 |
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| 34 |
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| 18 |
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| 6 |
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| 17 |
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| 0 |
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| 29 |
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| 15 |
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| 11 |
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| 32 |
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| 27 |
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| 41 |
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| 40 |
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| 3 |
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| 22 |
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| 21 |
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| 26 |
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| 23 |
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| 16 |
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| 39 |
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| 7 |
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| 37 |
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| 20 |
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| 35 |
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| 4 |
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| 19 |
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| 38 |
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| 10 |
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Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.9864 |
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("Varun1010/all-MiniLM-L6-v2-polaris-more")
# Run inference
preds = model("Tiong Bahru Plaza, DDC-L20, AHU 20-1 VSD CONTROL")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 3 | 8.7134 | 13 |
| Label | Training Sample Count |
|---|---|
| 0 | 4 |
| 1 | 4 |
| 2 | 4 |
| 3 | 4 |
| 4 | 4 |
| 5 | 4 |
| 6 | 4 |
| 7 | 4 |
| 8 | 4 |
| 9 | 4 |
| 10 | 4 |
| 11 | 4 |
| 12 | 4 |
| 13 | 4 |
| 14 | 4 |
| 15 | 4 |
| 16 | 4 |
| 17 | 4 |
| 18 | 4 |
| 19 | 3 |
| 20 | 3 |
| 21 | 4 |
| 22 | 4 |
| 23 | 1 |
| 24 | 1 |
| 25 | 4 |
| 26 | 4 |
| 27 | 4 |
| 28 | 4 |
| 29 | 4 |
| 30 | 3 |
| 31 | 3 |
| 32 | 4 |
| 33 | 4 |
| 34 | 4 |
| 35 | 4 |
| 36 | 4 |
| 37 | 4 |
| 38 | 3 |
| 39 | 3 |
| 40 | 1 |
| 41 | 1 |
| 42 | 3 |
| 43 | 4 |
Training Hyperparameters
- batch_size: (64, 64)
- num_epochs: (5, 5)
- 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: False
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0027 | 1 | 0.1538 | - |
| 0.1330 | 50 | 0.0495 | - |
| 0.2660 | 100 | 0.0655 | - |
| 0.3989 | 150 | 0.0336 | - |
| 0.5319 | 200 | 0.0282 | - |
| 0.6649 | 250 | 0.0207 | - |
| 0.7979 | 300 | 0.0184 | - |
| 0.9309 | 350 | 0.0163 | - |
| 1.0638 | 400 | 0.0088 | - |
| 1.1968 | 450 | 0.0307 | - |
| 1.3298 | 500 | 0.0153 | - |
| 1.4628 | 550 | 0.0079 | - |
| 1.5957 | 600 | 0.02 | - |
| 1.7287 | 650 | 0.0165 | - |
| 1.8617 | 700 | 0.0087 | - |
| 1.9947 | 750 | 0.0236 | - |
| 2.1277 | 800 | 0.0108 | - |
| 2.2606 | 850 | 0.0071 | - |
| 2.3936 | 900 | 0.0137 | - |
| 2.5266 | 950 | 0.0104 | - |
| 2.6596 | 1000 | 0.0054 | - |
| 2.7926 | 1050 | 0.0058 | - |
| 2.9255 | 1100 | 0.0052 | - |
| 3.0585 | 1150 | 0.0053 | - |
| 3.1915 | 1200 | 0.004 | - |
| 3.3245 | 1250 | 0.0047 | - |
| 3.4574 | 1300 | 0.0176 | - |
| 3.5904 | 1350 | 0.0046 | - |
| 3.7234 | 1400 | 0.0139 | - |
| 3.8564 | 1450 | 0.0043 | - |
| 3.9894 | 1500 | 0.0042 | - |
| 4.1223 | 1550 | 0.0112 | - |
| 4.2553 | 1600 | 0.0091 | - |
| 4.3883 | 1650 | 0.0045 | - |
| 4.5213 | 1700 | 0.009 | - |
| 4.6543 | 1750 | 0.0097 | - |
| 4.7872 | 1800 | 0.0049 | - |
| 4.9202 | 1850 | 0.0036 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.6.1
- Transformers: 4.38.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.18.0
- 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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