README.md

metadata

library_name: transformers
tags:
  - lora
  - sequence-classification
  - end-of-utterance
  - multilingual
  - english
  - spanish
license: apache-2.0
datasets:
  - marc-es/orga-dynamic-dataset
model_type: llama
language:
  - es
  - en
base_model:
  - HuggingFaceTB/SmolLM2-135M-Instruct
metrics:
  - accuracy

Orga Dynamic (1) — Bilingual End-of-Utterance Classifier

Orga Dynamic (1) es un adaptador LoRA (Low-Rank Adaptation) entrenado para detectar automáticamente el fin de turno (End of Utterance, EOU) en conversaciones.

• Base model: HuggingFaceTB/SmolLM2-135M-Instruct
• Method: LoRA-r16 / α32 sobre q_proj, k_proj, v_proj, o_proj
• Training data: 4 000 intervenciones
• Metrics (test 20 %)

Metric	EN + ES
Accuracy	0.951
F1	0.948

---

Model Details

Languages	English (en), Spanish (es)
Labels	0 = NO_EOU, 1 = EOU
Precision	fp16 (LoRA weights ≈ 5 MB)
License	Apache 2.0
Author	@marc-es

---

Quick Start

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel

base = AutoModelForSequenceClassification.from_pretrained(
          "HuggingFaceTB/SmolLM2-135M-Instruct", num_labels=2)
model = PeftModel.from_pretrained(base, "marc-es/orga-dynamic-1")
tok   = AutoTokenizer.from_pretrained("marc-es/orga-dynamic-1")

def is_end(text):
    out = model(**tok(text, return_tensors="pt"))[0]
    return out.argmax(-1).item() == 1   # True = EOU