ID	Label	Description
0	commissive	Promises, commitments ("I'll handle it.")
1	directive	Commands, requests ("Send the report.")
2	inform	Statements, facts ("The deadline is Friday.")
3	question	Questions, inquiries ("What is the timeline?")

Performance (NVIDIA RTX A3000, CUDA 12.8)

Metric	FP32 ONNX	FP16 ONNX
F1-macro	0.7239	0.7247
Accuracy	80.66%	80.70%
Model size	~540 MB	270.8 MB

Latency by Execution Provider

Provider	p50	p95	p99
CUDAExecutionProvider (ORT_ENABLE_ALL)	0.907ms	0.956ms	1.000ms
CUDA EP + IOBinding	0.899ms	0.928ms	0.942ms
TensorRT EP (FP16)	0.556ms	0.588ms	0.640ms
TensorRT EP + IOBinding	0.528ms	0.554ms	0.562ms

Batch Throughput (TensorRT EP, dynamic padding)

Batch Size	Throughput	Per-sample
1	178 samples/s	5.63ms
4	495 samples/s	2.02ms
8	513 samples/s	1.95ms
16	524 samples/s	1.91ms
32	411 samples/s	2.43ms

Per-Language Accuracy (PyTorch FP32)

Language	Test Set	Accuracy	F1 Macro
English	SILICONE dyda_da	80.8%	0.725
English	XDailyDialog	82.5%	0.750
German	XDailyDialog	81.8%	0.738
Russian	xdailydialog-ru	81.7%	0.734

Usage

python

import numpy as np
import onnxruntime as ort
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("WSHAPER/dialogue-act-classifier-fp16-multilingual")
session = ort.InferenceSession("model.onnx", providers=["CUDAExecutionProvider"])

text = "Können Sie mir den Bericht schicken?"  # or any EN/DE/RU text
inputs = tokenizer(text, padding="max_length", truncation=True, max_length=48, return_tensors="np")
outputs = session.run(None, {
    "input_ids": inputs["input_ids"].astype(np.int64),
    "attention_mask": inputs["attention_mask"].astype(np.int64),
})

label_names = ["commissive", "directive", "inform", "question"]
probs = np.exp(outputs[0][0]) / np.exp(outputs[0][0]).sum()
pred = label_names[np.argmax(probs)]
print(f"Prediction: {pred} (confidence: {probs.max():.2f})")

Training Details

Base model: distilbert-base-multilingual-cased
Datasets: XDailyDialog (EN+DE+IT), WSHAPER/xdailydialog-ru (Russian, machine-translated)
Epochs: 5, Batch size: 32, LR: 2e-5
Languages: en, de, ru
Quantization: FP16 via onnxruntime.transformers.float16
Inference optimizations: ORT_ENABLE_ALL, cudnn exhaustive search, TensorRT EP, IOBinding