---
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

```python
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