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--- |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: '"e-Allahabad Journey loan application workflow?"' |
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- text: '"Relief Bonds redemption during OD tenure?"' |
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- text: '"Chief General Managers'' discretionary powers?"' |
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- text: '"Digital Journey e-Allahabad nominee update steps?"' |
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- text: '"SGB partial withdrawal during loan period?"' |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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library_name: setfit |
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inference: true |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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model-index: |
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- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.975 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) |
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Classes:** 10 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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### Model Labels |
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| Label | Examples | |
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|:-----------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| Disclaimer | <ul><li>'"Terms of Use API restrictions?"'</li><li>'"Terms of Use age restrictions?"'</li><li>'"Disclaimer update alerts?"'</li></ul> | |
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| IB Loan against Sovereign Gold Bond | <ul><li>'"Sovereign Jewel Bond loan margin requirements?"'</li><li>'"Sovereign Gold Bond joint holder rules?"'</li><li>'"Sovereign Jewel Bond nomination process?"'</li></ul> | |
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| Ind Advantage (Reward Program) | <ul><li>'"Advantage Rewards international redemption fees?"'</li><li>'"Blackout dates for reward travel bookings?"'</li><li>'"Advantage Program customer support channels?"'</li></ul> | |
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| Amalgamation | <ul><li>'"Merger documentation checklist for branches?"'</li><li>'"Banking Amalgamation customer notification process?"'</li><li>'"Amalgamation loan portfolio transfer details?"'</li></ul> | |
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| Loan / OD against NSC / KVP / Relief bonds of RBI / LIC policies | <ul><li>'"Relief Bonds OD interest payment frequency?"'</li><li>'"KVP valuation for overdraft approval criteria?"'</li><li>'"NSC loan documentation checklist?"'</li></ul> | |
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| Chief General Managers | <ul><li>'"Chief General Managers\' office working hours?"'</li><li>'"How to contact Chief General Managers for escalations?"'</li><li>'"Senior General Managers\' regional jurisdiction list?"'</li></ul> | |
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| Point of Sale (PoS) | <ul><li>'"Offline PoS transaction capabilities?"'</li><li>'"PoS transaction audit trails?"'</li><li>'"PoS batch settlement timing?"'</li></ul> | |
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| Featured Products / Services / Schemes | <ul><li>'"Highlighted Products insurance coverage details?"'</li><li>'"Highlighted Products loan-to-value ratio?"'</li><li>'"Featured schemes disbursement timeline?"'</li></ul> | |
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| e-Allahabad Bank Journey | <ul><li>'"e-Allahabad Experience customer support channels?"'</li><li>'"Allahabad Online Journey QR code payments?"'</li><li>'"Allahabad Online Journey statement download process?"'</li></ul> | |
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| Centralized Pension Processing Centre | <ul><li>'"Processing time for pension applications?"'</li><li>'"QR code payments at Payment Office?"'</li><li>'"Central Pension Management Centre contact details?"'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.975 | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("kneau007/my-classifier") |
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# Run inference |
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preds = model("\"Relief Bonds redemption during OD tenure?\"") |
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``` |
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<!-- |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:-------|:----| |
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| Word count | 3 | 5.2062 | 8 | |
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| Label | Training Sample Count | |
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|:-----------------------------------------------------------------|:----------------------| |
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| Amalgamation | 14 | |
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| Chief General Managers | 16 | |
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| Disclaimer | 11 | |
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| Featured Products / Services / Schemes | 18 | |
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| IB Loan against Sovereign Gold Bond | 18 | |
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| Ind Advantage (Reward Program) | 19 | |
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| Loan / OD against NSC / KVP / Relief bonds of RBI / LIC policies | 16 | |
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| Point of Sale (PoS) | 16 | |
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| e-Allahabad Bank Journey | 15 | |
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| Centralized Pension Processing Centre | 17 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (1, 1) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 20 |
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- body_learning_rate: (2e-05, 2e-05) |
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- head_learning_rate: 2e-05 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- l2_weight: 0.01 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0025 | 1 | 0.172 | - | |
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| 0.125 | 50 | 0.1198 | - | |
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| 0.25 | 100 | 0.0251 | - | |
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| 0.375 | 150 | 0.0068 | - | |
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| 0.5 | 200 | 0.003 | - | |
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| 0.625 | 250 | 0.0018 | - | |
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| 0.75 | 300 | 0.0015 | - | |
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| 0.875 | 350 | 0.0013 | - | |
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| 1.0 | 400 | 0.0013 | - | |
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### Framework Versions |
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- Python: 3.11.11 |
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- SetFit: 1.1.1 |
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- Sentence Transformers: 3.4.1 |
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- Transformers: 4.48.3 |
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- PyTorch: 2.5.1+cu124 |
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- Datasets: 3.3.2 |
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- Tokenizers: 0.21.0 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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*Clearly define terms in order to be accessible across audiences.* |
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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