Modelpublic

TinyModels/Setfit-banking-intent

sourceHugging Faceapache-2.0updated 9d agoView on Hugging Face

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Model Card

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### 🏦 A tiny, fast, and accurate SetFit classifier that routes banking customer intents — trained with just 4 examples per class (308 sentences total).

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🧭 Table of Contents

<a href="#-model-description"><kbd>📖 Description</kbd></a>  •  <a href="#-architecture"><kbd>🏗️ Architecture</kbd></a>  •  <a href="#-quick-start"><kbd>🚀 Quick Start</kbd></a>  •  <a href="#-model-labels-77-classes"><kbd>🏷️ Labels</kbd></a>  •  <a href="#-training-details"><kbd>🎓 Training</kbd></a>  •  <a href="#-bias-risks--limitations"><kbd>⚠️ Risks</kbd></a>  •  <a href="#-citation"><kbd>📜 Citation</kbd></a>

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📖 Model Description

🔍	💡
Model Type	`SetFit` — Efficient Few-Shot Learning
Author	[TinyModels](https://huggingface.co/TinyModels)
Sentence Transformer	`BAAI/bge-small-en-v1.5`
Classification Head	`sklearn.linear_model.LogisticRegression`
Max Sequence Length	`512` tokens
Number of Classes	`77` banking intents
Training Samples	`4` per class — `308` total
Language	English 🇬🇧
Domain	🏦 Banking / Fintech customer support

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🌟 Why SetFit?

SetFit achieves strong accuracy without prompts and without large labeled datasets. It first fine-tunes a sentence embedding model with contrastive learning, then trains a lightweight classifier on top — perfect for low-data, high-class-count problems like this one.

🏗️ Architecture

mermaid

flowchart LR
    A["📝 Input Text<br/>(customer message)"] --> B["🔤 BAAI/bge-small-en-v1.5<br/>Sentence Transformer"]
    B --> C["🧬 Sentence Embedding<br/>(384-dim)"]
    C --> D["🎯 LogisticRegression<br/>Classification Head"]
    D --> E["🏷️ Predicted Intent<br/>(1 of 77 classes)"]

    subgraph TRAIN["🎓 Training Pipeline"]
        direction TB
        T1["Contrastive Fine-tuning<br/>(CosineSimilarityLoss)"] --> T2["Embedding Extraction"]
        T2 --> T3["LogReg Head Training"]
    end

    style A fill:#6C63FF,color:#fff,stroke:#4B4BFF,stroke-width:2px
    style B fill:#FF6F91,color:#fff,stroke:#E0567A,stroke-width:2px
    style C fill:#00C2A8,color:#fff,stroke:#009E88,stroke-width:2px
    style D fill:#F7B32B,color:#000,stroke:#D99A1F,stroke-width:2px
    style E fill:#34D399,color:#000,stroke:#25A67A,stroke-width:2px
    style TRAIN fill:#1a1a2e,color:#fff,stroke:#6C63FF,stroke-dasharray: 5 5

🚀 Quick Start

<details open> <summary><b>🐍 Python — one-liner inference</b></summary>

bash

pip install setfit

python

from setfit import SetFitModel

# 🔽 Load from the Hub
model = SetFitModel.from_pretrained("TinyModels/setfit-banking-intent")

# 🎯 Run inference
preds = model("I have an unauthorized charge.")
print(preds)
# → ['direct_debit_payment_not_recognised']

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<details> <summary><b>📦 Batch inference with confidence scores</b></summary>

python

from setfit import SetFitModel
import numpy as np

model = SetFitModel.from_pretrained("TinyModels/setfit-banking-intent")

texts = [
    "What do I do? My card is broken.",
    "Where should I withdraw money from?",
    "What are the currency exchange fees?",
    "I have an unauthorized charge.",
]

# Predicted labels
labels = model(texts)
print(labels)

# Probability distribution per class
probs = model.predict_proba(texts)
top_idx = np.argmax(probs, axis=1)
top_conf = probs[np.arange(len(texts)), top_idx]

for t, l, c in zip(texts, labels, top_conf):
    print(f"[{c:0.2%}] {l:<40} ← {t}")

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<details> <summary><b>⚡ FastAPI microservice snippet</b></summary>

python

from fastapi import FastAPI
from pydantic import BaseModel
from setfit import SetFitModel

app = FastAPI(title="Banking Intent API")
model = SetFitModel.from_pretrained("TinyModels/setfit-banking-intent")

class Query(BaseModel):
    text: str

@app.post("/classify")
def classify(q: Query):
    label = model([q.text])[0]
    return {"intent": label, "text": q.text}

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🏷️ Model Labels (77 Classes)

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<details> <summary>💳 <b>Card Management</b> — <i>20 classes</i></summary>

Label	Example Utterance
`activate_my_card`	I have a new card and need to activate it
`card_about_to_expire`	Do I need to do something to get a new card once it expires?
`card_acceptance`	Where can I use my Mastercard?
`card_arrival`	Where is the card I ordered 2 weeks ago?
`card_delivery_estimate`	What is the delivery time for US?
`card_linking`	My new card isn't in my app, how do I get it in there?
`card_not_working`	What do I do? My card is broken.
`card_swallowed`	Please send a new card; the ATM ate mine.
`contactless_not_working`	Why is my contactless not working?
`country_support`	Will my new card work outside of the EU?
`disposable_card_limits`	Is there a limit to how many times I can use my disposable virtual card?
`get_disposable_virtual_card`	Where can I order a disposable virtual card?
`get_physical_card`	In the app, where do I find my card PIN?
`getting_spare_card`	Is it possible to get another card?
`getting_virtual_card`	How do I receive a virtual card?
`lost_or_stolen_card`	I've lost my card. What can I do about that?
`order_physical_card`	How do I ask for a physical card?
`supported_cards_and_currencies`	What currencies are approved to add money?
`virtual_card_not_working`	Why isn't my disposable virtual card working?
`visa_or_mastercard`	Which one are you? Visa or Mastercard?

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<details> <summary>💸 <b>Payments</b> — <i>9 classes</i></summary>

Label	Example Utterance
`apple_pay_or_google_pay`	How can I get my Google pay top up to work?
`card_payment_fee_charged`	Why am I getting charged more for using my card?
`card_payment_not_recognised`	I don't understand where this charge came from.
`card_payment_wrong_exchange_rate`	My exchange rate isn't correct.
`declined_card_payment`	You have declined my payment.
`extra_charge_on_statement`	Why is there a $1 charge on my statement?
`pending_card_payment`	A card payment on my account is shown as pending.
`reverted_card_payment?`	I did a payment but it was reverted by the app.
`transaction_charged_twice`	I have a duplicate charge.

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<details> <summary>🔁 <b>Transfers</b> — <i>12 classes</i></summary>

Label	Example Utterance
`balance_not_updated_after_bank_transfer`	I just transferred some money and do not see it updated yet.
`beneficiary_not_allowed`	Why can't I use my beneficiary?
`cancel_transfer`	Please cancel the transfer I just made.
`declined_transfer`	I got a message that my transfer was declined.
`failed_transfer`	I can't seem to make a standard bank transfer.
`pending_transfer`	I am still waiting for a transfer to show up.
`receiving_money`	How can my boss pay me directly to the card?
`transfer_fee_charged`	Why was I charged a fee for transferring money?
`transfer_into_account`	How can I transfer money to this account from another bank?
`transfer_not_received_by_recipient`	I transferred money and it didn't get there.
`transfer_timing`	How long until transfers from Europe go through?
`top_up_by_bank_transfer_charge`	If I top up by transfer, am I going to be charged?

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<details> <summary>⬆️ <b>Top-ups</b> — <i>9 classes</i></summary>

Label	Example Utterance
`automatic_top_up`	How can I setup automatic top-up?
`pending_top_up`	Why is the top-up I made still pending?
`top_up_by_card_charge`	Do you have any fees if I want to add money using an international card?
`top_up_by_cash_or_cheque`	Can I top up with check?
`top_up_failed`	I don't think that my top-up worked.
`top_up_limits`	What's the top-up limit?
`top_up_reverted`	Why did my top-up get reverted?
`topping_up_by_card`	I can't see my top up in my wallet!
`verify_top_up`	How are top-ups verified?

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<details> <summary>🏧 <b>Cash & ATM</b> — <i>8 classes</i></summary>

Label	Example Utterance
`atm_support`	Where should I withdraw money from?
`balance_not_updated_after_cheque_or_cash_deposit`	Why isn't my cash deposit showing up in my account?
`cash_withdrawal_charge`	I got charged fees for withdrawing cash!
`cash_withdrawal_not_recognised`	I see cash withdrawals that I did not authorize.
`declined_cash_withdrawal`	Is my card broken? I can't get cash out of the ATM.
`pending_cash_withdrawal`	My ATM withdrawal is taking forever.
`wrong_amount_of_cash_received`	I only got $20 of the $100 that I attempted to withdraw.
`wrong_exchange_rate_for_cash_withdrawal`	I received the incorrect exchange rate.

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<details> <summary>🔐 <b>Security & PIN</b> — <i>5 classes</i></summary>

Label	Example Utterance
`change_pin`	Can you tell me how to change my PIN?
`compromised_card`	The card has suffered a security breach.
`lost_or_stolen_phone`	My app was on the phone and I was mugged.
`passcode_forgotten`	I thought I knew my password but I guess I was wrong.
`pin_blocked`	Help me unblock my account. I entered the PIN wrong too many times.

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<details> <summary>🪪 <b>Identity Verification</b> — <i>4 classes</i></summary>

Label	Example Utterance
`unable_to_verify_identity`	The app is not able to realize that it is me.
`verify_my_identity`	Can I get information on the identity checks?
`verify_source_of_funds`	I'd like to know where my funds come from.
`why_verify_identity`	What is the function of the identity check?

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<details> <summary>👤 <b>Account Management</b> — <i>3 classes</i></summary>

Label	Example Utterance
`age_limit`	At what age can a person open an account?
`edit_personal_details`	I just got married and need to change my name on the account.
`terminate_account`	How can I delete my account?

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<details> <summary>💱 <b>Fees & Currency</b> — <i>4 classes</i></summary>

Label	Example Utterance
`exchange_charge`	What are the currency exchange fees?
`exchange_rate`	How are the exchange rates determined?
`exchange_via_app`	Can I exchange USD and GBP from the app?
`fiat_currency_support`	How many currencies can I have?

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<details> <summary>🧾 <b>Refunds & Disputes</b> — <i>3 classes</i></summary>

Label	Example Utterance
`direct_debit_payment_not_recognised`	I have an unauthorized charge.
`Refund_not_showing_up`	Why can't I see my refund in my statement?
`request_refund`	I need a refund for something I bought.

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🎓 Training Details

⚙️ Hyperparameters

Parameter	Value	Parameter	Value
`batch_size`	`(32, 32)`	`num_epochs`	`(1, 1)`
`max_steps`	`-1`	`sampling_strategy`	`oversampling`
`num_iterations`	`2`	`body_learning_rate`	`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`	`load_best_model_at_end`	`False`

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📚 Training Set Statistics

Metric	Min	Median	Max
📝 Word count	4	11.66	78
🏷️ Samples per class	4	4	4
🗂️ Total samples	—	308	—

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📉 Training Curve

Epoch	Step	Training Loss	Validation Loss
`0.0256`	`1`	0.2141	—

🧪 Framework Versions

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⚠️ Bias, Risks & Limitations

<details> <summary><b>🔍 Click to expand</b></summary>

⚠️ Area	📝 Notes
Data size	Only 4 examples per class — rare phrasings may be misclassified.
Language	Trained only on English — will not generalize to other languages without re-training.
Domain shift	Vocabulary is banking-specific; out-of-domain inputs may produce confident but wrong labels.
Class ambiguity	Some classes are semantically close (e.g. `pending_transfer` vs. `transfer_not_received_by_recipient`).
No calibration	LogReg probabilities are not temperature-calibrated — treat confidence as approximate.
PII	Do not feed real customer PII into shared demos; use anonymized text.

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🚫 Out-of-Scope Use

—❌ Legal, medical, or financial advice generation
—❌ Production decision-making without human-in-the-loop
—❌ Non-English customer messages
—❌ Emotion / sentiment / toxicity detection

🧩 Model Card Recipe

yaml

🎯 base_model:    BAAI/bge-small-en-v1.5
🧠 head:          LogisticRegression
📚 technique:     SetFit (few-shot contrastive)
🏷️ classes:       77
📈 metric:        accuracy
🔤 max_tokens:    512
🌱 seed:          42

🗺️ Roadmap

—[x] Train on 77 banking intents
—[x] Publish to Hugging Face Hub
—[ ] Add calibrated confidence scores
—[ ] Multilingual variant (XLM-R backbone)
—[ ] ONNX / quantized export for edge
—[ ] Evaluation on public banking benchmark

📜 Citation

If you use this model, please cite the SetFit paper:

bibtex

@article{tunstall2022setfit,
    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},
    title     = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year      = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}

📚 Glossary

Term	Meaning
SetFit	Sentence Transformer Fine-tuning — few-shot text classification technique.
Contrastive Learning	Training method that pulls similar pairs together, pushes dissimilar apart.
Sentence Transformer	Encoder that maps text → dense vector embedding.
LogisticRegression Head	Simple linear classifier on top of embeddings.
Intent	The user's goal behind a message (e.g. `cancel_transfer`).

🌟 Star History

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💜 Made with love by TinyModels

<sub>⭐ If this model helped you, consider leaving a like on the Hub/Repo!</sub>

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