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WSHAPER/distilbert-multilingual-dialogue-act-classifier

sourceHugging Faceapache-2.0updated 4mo agoView on Hugging Face

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Model Card

distilbert-multilingual-dialogue-act-classifier

Fine-tuned DistilBERT (distilbert-base-multilingual-cased) for 4-class dialogue act classification in English, German, and Russian. Trained on conversational dialogue data, optimized for ASR transcripts.

Labels

Index	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?")

Evaluation

Per-language performance on held-out test sets:

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

Edge-case test suite (ASR disfluent input, conversational): 77.8% (35/45)

Usage

python

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

model = AutoModelForSequenceClassification.from_pretrained("WSHAPER/distilbert-multilingual-dialogue-act-classifier")
tokenizer = AutoTokenizer.from_pretrained("WSHAPER/distilbert-multilingual-dialogue-act-classifier")

texts = ["What is the timeline?", "Send the report.", "The meeting went well."]
inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits
    probs = torch.softmax(logits, dim=-1)
    preds = torch.argmax(probs, dim=-1)

labels = ["commissive", "directive", "inform", "question"]
for text, pred, prob in zip(texts, preds, probs):
    print(f"{text} → {labels[pred]} ({prob[pred]:.2f})")

Training Details

—Base model: distilbert-base-multilingual-cased (277M params)
—Training data:
—XDailyDialog — EN, DE, IT (~249K utterances)
—WSHAPER/xdailydialog-ru — RU (~82K utterances)
—Total: ~331K utterances across 4 languages
—Hyperparameters: 5 epochs, batch 32, lr 2e-5, warmup 10%
—Hardware: NVIDIA RTX A3000 12GB, ~1.5 hours

Rust Inference (candle-transformers)

This model is compatible with candle-transformers for pure Rust inference:

rust

// Loads model.safetensors + tokenizer.json directly
let config = DistilBertConfig::from_file("config.json");
let bert = BertModel::load(vb.pp("distilbert"), &config)?;
let classifier = candle_nn::linear(config.hidden_size, 4, vb.pp("classifier"))?;

License

Apache-2.0