Swift-Support LaBSE Intent Classifier (v1.0)
This is a fine-tuned Language-Agnostic BERT Sentence Embedding (LaBSE) model designed for trilingual intent classification in the banking and financial support domain. It was developed as part of the Swift Support Ticket Classification project.
Model Details
- Base Architecture:
sentence-transformers/LaBSE(501k Vocabulary) - Task: Text Classification (Intent Recognition)
- Number of Classes: 77 (Derived from the BANKING77 taxonomy)
- Supported Languages: English, Sinhala, Tamil, Singlish (Code-mixed), and Tanglish (Code-mixed).
Evaluation & Benchmark Results
During the architectural ablation phase, this model was strictly evaluated on a held-out test set against classical ML algorithms, Indic Specialists (MuRIL & IndicBERT), and XLM-RoBERTa.
The metric used is Macro-F1 across all 77 intent classes.
| Language Track | Best Classical ML | MuRIL | IndicBERT | XLM-RoBERTa | LaBSE (This Model) |
|---|---|---|---|---|---|
| English | 90.98% | — | — | 93.88% | 94.13% |
| Sinhala | 83.08% | — | — | 92.42% | 92.95% |
| Singlish (Romanized) | 86.49% | — | — | 90.03% | 90.65% |
| Tamil | 86.35% | 66.01% | 89.81% | 91.74% | 93.27% |
| Tanglish (Romanized) | 61.05% | 57.62% | 61.25% | 72.04% | 70.57% |
| ALL (Pooled) | 83.18% | 62.10% | 76.24% | 88.29% | 88.54% |
Key Findings:
- LaBSE is the Intent Champion: Achieving 88.54% Macro-F1 on the pooled track, it outperformed the classical baseline by +5.36pp.
- Specialists failed on Code-Mixed Data: Indic specialists like MuRIL and IndicBERT failed outright on the pooled and code-mixed tracks because their smaller vocabularies couldn't handle heavy romanization or English slang, proving that massive multilingual coverage (LaBSE's 501k vocab) is required for real-world South Asian support tickets.
How to use in Python
You can easily use this model via the transformers pipeline:
from transformers import pipeline
classifier = pipeline("text-classification", model="Swift-Support/labse-intent-1.0")
result = classifier("I lost my credit card yesterday, please help me cancel it")
print(result)
# Output: [{'label': 'Card payment declined', 'score': 0.98}]
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