Swift-Support/labse-sentiment-1.0

Swift-Support LaBSE Sentiment Classifier (v1.0)

A fine-tuned LaBSE (Language-Agnostic BERT Sentence Embedding) model that flags a banking support ticket as Negative or Neutral, across five language tracks. Built for the Swift support-ticket triage project, alongside Swift-Support/labse-intent-1.0 and Swift-Support/labse-priority-1.0.

Model details

• Base architecture: sentence-transformers/LaBSE (471M parameters, 501k vocabulary)
• Task: binary text classification (sentiment)
• Classes: Neutral, Negative
• Languages: English, Sinhala, Tamil, Singlish (romanized Sinhala), Tanglish (romanized Tamil)
• Regime: one multilingual model over all five tracks — not five per-language models

The headline metric is Negative-F1, never accuracy. About 94% of tickets are Neutral, so a model that predicts "Neutral" for everything scores ~0.94 accuracy and is useless. Negative is the minority class, the one that matters for escalation, and the only one worth scoring on.

Evaluation

Trained on train+dev (49,990 rows = 9,998 tickets × 5 languages), scored once on the held-out test set (15,395 rows = 3,079 tickets × 5 languages, 975 Negatives). Frozen split e7b5934392cd; test tickets come from the official BANKING77 test file and were never used for model selection.

Pooled test Negative-F1: 0.7138 (precision 0.7601, recall 0.6728)

Macro-F1 0.8478 · accuracy 0.9658 · predicted 863 Negatives against 975 true.

Against the alternatives (pooled test Negative-F1, same labels, same split)

model	Negative-F1
LaBSE (this model)	0.7138
gemma-3-1b (LoRA, single-task)	0.7126
gemma-3-1b joint multi-task, shared head	0.7048
gemma-3-1b joint multi-task, 3 heads	0.7042
TF-IDF + LinearSVC (classical champion)	0.6663
TF-IDF + logistic regression	0.6415

LaBSE beats the classical champion by +0.048. The Gemma decoder is a statistical tie (+0.001), not a worse model — it is simply larger to serve for no measured gain.

Per-epoch

epoch	Negative-F1	precision	recall
1	0.6749	0.5939	0.7815
2	0.7115	0.7697	0.6615
3	0.7138	0.7601	0.6728

Best epoch is 3 of 3 — still improving when training stopped, which is what a consistent labelling target looks like. Under the older v5 labels the same model peaked at epoch 1 and then degraded, the signature of memorising contradictory labels. The relabel fixed that.

⚠️ Label provenance — this model was trained on v8 labels

The sentiment labels are LLM-generated, not human. They went through one major revision:

prompt	agreement with human annotation (500-ticket gold set)	κ
v5 (superseded)	0.5769 Negative-F1	0.55
v8 (this model)	0.7931 Negative-F1, 97.6% agreement	0.780

v8 replaced 711 of 13,077 sentiment labels on 2026-08-19 and nearly doubled the labelling rule's agreement with a human annotator. This model's 0.7138 reaches 90.0% of its 0.7931 ceiling.

Corrected 2026-09-08. This card previously gave the v8 ceiling as 0.7812 (97.2% agreement, κ = 0.766). That figure scored the prompt's raw output, not the labels actually shipped in the dataset — the two differ on 8 of the 500 gold tickets. Against the shipped labels, which are what this model trained on, the ceiling is 0.7931 (95% CI [0.6667, 0.8956]; agreement 0.976, κ = 0.780).

The interval is wide because the gold set holds only 31 Negative tickets, annotated by one person. Treat 0.7931 as a point estimate with real uncertainty, not a hard bound — it is the weakest link in every claim on this card.

Do not compare this score to any Swift sentiment number recorded before 2026-08-19. Those score against v5 labels on a test set with 505 Negatives rather than 975; the metric moves on the label change alone. The previous LaBSE checkpoint scored 0.5664 on v5 and is not this model.

Per-language results

From the same fit, sliced by track (3,079 tickets and 195 Negatives each):

track	script	Negative-F1
English	Latin (native)	0.8032
Tamil	Tamil	0.7606
Sinhala	Sinhala	0.7234
Singlish	romanized Sinhala	0.6757
Tanglish	romanized Tamil	0.5948

A 20.8-point spread. Every track is significantly below English (paired bootstrap on the same tickets, all CIs clear of zero). Expect materially worse service for customers writing in transliteration.

The advantage over TF-IDF exists only on native script

Pooled, this model beats a TF-IDF+SVM baseline by +0.0485 (95% CI [+0.0257, +0.0721], p < 0.0001). That gain is not spread evenly:

track	gain over TF-IDF+SVM	p
English	+0.0834	<0.001
Sinhala	+0.0822	0.004
Tamil	+0.0357	0.144
Singlish	+0.0124	0.712
Tanglish	+0.0225	0.490

On both romanized tracks the encoder's advantage is indistinguishable from zero. Sinhala and Singlish are the same tickets in the same language differing only in script, so the contrast can be tested directly: the difference of differences is +0.0698 (95% CI [+0.0140, +0.1283], p = 0.012). Romanizing the same tickets removes about 7 points of the encoder's advantage. The same test on Tamil/Tanglish is not significant (p = 0.762), so this is established for Sinhala only.

If your traffic is predominantly romanized, this model is not measurably better than TF-IDF and costs 1.8 GB and a GPU to serve.

Usage

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

repo = "Swift-Support/labse-sentiment-1.0"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForSequenceClassification.from_pretrained(repo).eval()

texts = ["Someone has taken money from my account and nobody is helping me!",
         "How do I activate my new card?"]

with torch.no_grad():
    batch = tok(texts, return_tensors="pt", padding=True, truncation=True, max_length=128)
    probs = model(**batch).logits.softmax(-1)

for text, p in zip(texts, probs):
    print(model.config.id2label[int(p.argmax())], f"{p.max():.3f}", "|", text)

Two things that will silently corrupt results if you get them wrong:

• max_length=128 must match training. It is not stored in the checkpoint.
• Read the label from model.config.id2label, never a hardcoded index. This checkpoint carries an explicit mapping (0: Neutral, 1: Negative). A wrong guess does not raise — it silently inverts every prediction.

Cost: ~1.9 GB resident, roughly 100–300 ms per ticket on CPU. Load once at process start.

Training

base	sentence-transformers/LaBSE
fit portion	train+dev, 49,990 rows
epochs	3 (best epoch: 3 of 3)
learning rate	2e-5
batch size	32
max sequence length	128
class imbalance	class_weight (balanced)
precision	fp16
hardware	Kaggle T4, ~112 rows/s, 22 min wall

Limitations

• Treat as advisory, not routing. At 0.7138 Negative-F1 with 0.67 recall, roughly a third of genuinely negative tickets are missed. This is a prioritisation signal, not an escalation gate.
• Labels are LLM-generated — see the ceiling section. The model is measured against a rule that itself agrees with a human 97.2% of the time (κ 0.77), not against human judgement directly.
• Romanized text is synthetic. Singlish is rule-generated from Sinhala and Tanglish is machine-translated, so both are cleaner and more regular than text a human would type. No romanized-specific conclusion from this model should be trusted until re-measured on human-typed data.
• No per-language test breakdown exists for this model — it was scored pooled only.
• Not calibrated. Softmax scores are not probabilities to threshold on without re-calibrating; a CV-tuned threshold failed to transfer to test in this project (−0.005 on the champion).
• Domain-bound. Derived from BANKING77; behaviour outside retail-banking support is untested.
• Single seed, no repeats — no variance estimate.

Citation & provenance

Derived from BANKING77 (PolyAI, CC-BY-4.0), translated into Sinhala and Tamil and romanized into Singlish and Tanglish. Sentiment labels were generated by an LLM prompt (v8) and benchmarked against human annotation as described above.

Training data: Swift-Support/swift-support-tickets-1.0

How to cite

Citing BANKING77 alone is not sufficient attribution for this corpus. BANKING77 supplies the English text and the 77-way intent labels — 20% of the rows and one of the three label columns. The rest is this project's work:

Rows whose text is new (Sinhala, Tamil, Singlish, Tanglish)	52,308 of 65,385 — 80%
Rows inherited from BANKING77 (English)	13,077 — 20%
Label columns inherited	1 (category / intent)
Label columns added	2 (sentiment, priority) — 26,154 new per-ticket assignments
Split	the frozen id-level split is this project's, not BANKING77's

If you use the translated tracks, the sentiment or priority labels, the frozen split, or any of the models, cite this work. If you additionally use the English text or the intent labels, cite BANKING77 as well. Both licences are CC-BY-4.0, so attribution is a licence condition, not a courtesy.

This work — the corpus

@misc{swift_tickets_2026,
  author       = {Sithija Seneviratne and Ruththiragayan Sutharsan and Shazan Shaheed},
  title        = {Swift Support Tickets: A Multilingual Ticket-Triage Corpus for Sinhala,
                  Tamil and English, Including Romanized Code-Mixed Text},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/Swift-Support/swift-support-tickets-1.0}},
  note         = {Derived from BANKING77. Adds four language tracks and the sentiment and
                  priority label columns. Paper in preparation.}
}

This work — the models

@misc{swift_models_2026,
  author       = {Sithija Seneviratne and Ruththiragayan Sutharsan and Shazan Shaheed},
  title        = {Swift: Multilingual Baselines for Support-Ticket Triage in Sinhala,
                  Tamil and English},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Swift-Support}},
  note         = {Intent, sentiment and priority classifiers. Paper in preparation.}
}

BANKING77 — the source of the English text and the intent labels

@inproceedings{casanueva2020banking77,
  author    = {Casanueva, I{\~n}igo and Tem{\v c}inas, Tadas and Gerz, Daniela and
               Henderson, Matthew and Vuli{\'c}, Ivan},
  title     = {Efficient Intent Detection with Dual Sentence Encoders},
  booktitle = {Proceedings of the 2nd Workshop on Natural Language Processing for
               Conversational AI},
  pages     = {38--45},
  year      = {2020},
  publisher = {Association for Computational Linguistics},
  doi       = {10.18653/v1/2020.nlp4convai-1.5},
  url       = {https://aclanthology.org/2020.nlp4convai-1.5/}
}