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: 10 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 |
---|---|
Disclaimer |
|
IB Loan against Sovereign Gold Bond |
|
Ind Advantage (Reward Program) |
|
Amalgamation |
|
Loan / OD against NSC / KVP / Relief bonds of RBI / LIC policies |
|
Chief General Managers |
|
Point of Sale (PoS) |
|
Featured Products / Services / Schemes |
|
e-Allahabad Bank Journey |
|
Centralized Pension Processing Centre |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.975 |
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("kneau007/my-classifier")
# Run inference
preds = model("\"Relief Bonds redemption during OD tenure?\"")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 3 | 5.2062 | 8 |
Label | Training Sample Count |
---|---|
Amalgamation | 14 |
Chief General Managers | 16 |
Disclaimer | 11 |
Featured Products / Services / Schemes | 18 |
IB Loan against Sovereign Gold Bond | 18 |
Ind Advantage (Reward Program) | 19 |
Loan / OD against NSC / KVP / Relief bonds of RBI / LIC policies | 16 |
Point of Sale (PoS) | 16 |
e-Allahabad Bank Journey | 15 |
Centralized Pension Processing Centre | 17 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0025 | 1 | 0.172 | - |
0.125 | 50 | 0.1198 | - |
0.25 | 100 | 0.0251 | - |
0.375 | 150 | 0.0068 | - |
0.5 | 200 | 0.003 | - |
0.625 | 250 | 0.0018 | - |
0.75 | 300 | 0.0015 | - |
0.875 | 350 | 0.0013 | - |
1.0 | 400 | 0.0013 | - |
Framework Versions
- Python: 3.11.11
- SetFit: 1.1.1
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Datasets: 3.3.2
- Tokenizers: 0.21.0
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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