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"""
=== MIA · Clasificador de Emociones (Pretrained Encoder + MLP) ===
- Mantiene compatibilidad con tu API pública.
- Permite usar tu TextEmbedder aleatorio (emb_dim) o un encoder preentrenado (BETO) con 768D.
- Expone freeze/unfreeze para controlar el fine-tuning desde el trainer.
"""
import torch
import torch.nn as nn
from typing import List, Optional
from transformers import AutoTokenizer, AutoModel
# ==================== MÓDULO 1A: TextEmbedder (embedding aleatorio) ====================
class TextEmbedder(nn.Module):
"""
Módulo de Embedding simple:
- Usa el tokenizador de BETO para sub-palabras (por conveniencia, vocab, pad_id, etc.)
- La representación es un embedding aleatorio + mean pooling (no contextual).
"""
def __init__(
self,
model_name: str = "dccuchile/bert-base-spanish-wwm-cased",
emb_dim: int = 300,
max_length: int = 128,
device: Optional[torch.device] = None
):
super().__init__()
self.tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
self.vocab_size = self.tokenizer.vocab_size
self.pad_id = self.tokenizer.pad_token_id
self.cls_id = self.tokenizer.cls_token_id
self.sep_id = self.tokenizer.sep_token_id
self.max_length = max_length
# Capa de embedding
self.embedding = nn.Embedding(self.vocab_size, emb_dim, padding_idx=self.pad_id)
nn.init.xavier_uniform_(self.embedding.weight)
with torch.no_grad():
if self.pad_id is not None:
self.embedding.weight[self.pad_id].zero_()
# Regularización opcional (ayuda contra sobreajuste)
self.emb_dropout = nn.Dropout(p=0.1)
self.device = device or torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.to(self.device)
def embed_batch(self, texts: List[str]) -> torch.Tensor:
batch = self.tokenizer(
texts, padding=True, truncation=True, max_length=self.max_length, return_tensors="pt"
)
input_ids = batch["input_ids"].to(self.device) # [B, T]
attention_mask = batch["attention_mask"].to(self.device) # [B, T]
embeds = self.embedding(input_ids) # [B, T, E]
if self.training:
embeds = self.emb_dropout(embeds)
mask = attention_mask.bool() # [B, T]
if self.cls_id is not None:
mask = mask & (input_ids != self.cls_id)
if self.sep_id is not None:
mask = mask & (input_ids != self.sep_id)
mask_f = mask.unsqueeze(-1).float() # [B, T, 1]
summed = (embeds * mask_f).sum(dim=1) # [B, E]
counts = mask_f.sum(dim=1).clamp(min=1.0) # [B, 1]
sentence_vecs = summed / counts # [B, E]
return sentence_vecs
def embed_sentence(self, text: str) -> torch.Tensor:
return self.embed_batch([text])[0]
# ==================== MÓDULO 1B: BETOEmbedder (encoder preentrenado) ====================
class BETOEmbedder(nn.Module):
"""
Usa el encoder de BETO (BERT en español) para obtener embeddings contextuales.
Mean pooling sobre last_hidden_state.
Salida: [B, 768]
"""
def __init__(
self,
model_name: str = "dccuchile/bert-base-spanish-wwm-cased",
max_length: int = 128,
device: Optional[torch.device] = None
):
super().__init__()
self.device = device or torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
self.encoder = AutoModel.from_pretrained(model_name)
self.max_length = max_length
self.encoder.to(self.device)
def embed_batch(self, texts: List[str]) -> torch.Tensor:
inputs = self.tokenizer(
texts, padding=True, truncation=True, max_length=self.max_length, return_tensors="pt"
).to(self.device)
outputs = self.encoder(**inputs) # last_hidden_state [B, T, 768]
last_hidden = outputs.last_hidden_state
mask = inputs["attention_mask"].unsqueeze(-1).float() # [B, T, 1]
pooled = (last_hidden * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1e-9) # [B, 768]
return pooled
# ==================== MÓDULO 2: MLP Classifier ====================
class MLPClassifier(nn.Module):
"""
Feedforward para clasificación de emociones:
Input → 128 → 64 → 6 (logits)
"""
def __init__(
self,
input_dim: int = 300,
hidden1: int = 128,
hidden2: int = 64,
num_classes: int = 6,
dropout: float = 0.3
):
super().__init__()
self.fc1 = nn.Linear(input_dim, hidden1)
self.relu1 = nn.ReLU()
self.dropout1 = nn.Dropout(dropout)
self.fc2 = nn.Linear(hidden1, hidden2)
self.relu2 = nn.ReLU()
self.dropout2 = nn.Dropout(dropout)
self.fc3 = nn.Linear(hidden2, num_classes)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.fc1(x); x = self.relu1(x); x = self.dropout1(x)
x = self.fc2(x); x = self.relu2(x); x = self.dropout2(x)
x = self.fc3(x)
return x
# ==================== MÓDULO 3: Modelo Completo ====================
class EmotionClassifier(nn.Module):
"""
Integra embedder (aleatorio o BETO) + MLP.
- `pretrained_encoder=None` → usa TextEmbedder (emb_dim configurable)
- `pretrained_encoder="beto"` → usa BETOEmbedder (salida 768D)
"""
def __init__(
self,
model_name: str = "dccuchile/bert-base-spanish-wwm-cased",
emb_dim: int = 300,
max_length: int = 128,
hidden1: int = 128,
hidden2: int = 64,
num_classes: int = 6,
dropout: float = 0.3,
device: Optional[torch.device] = None,
pretrained_encoder: Optional[str] = None
):
super().__init__()
self.device = device or torch.device("cuda" if torch.cuda.is_available() else "cpu")
if pretrained_encoder == "beto":
self.embedder = BETOEmbedder(model_name=model_name, max_length=max_length, device=self.device)
embed_dim = 768
else:
self.embedder = TextEmbedder(model_name=model_name, emb_dim=emb_dim, max_length=max_length, device=self.device)
embed_dim = emb_dim
self.classifier = MLPClassifier(
input_dim=embed_dim, hidden1=hidden1, hidden2=hidden2, num_classes=num_classes, dropout=dropout
)
self.label_map = {0: "tristeza", 1: "alegría", 2: "amor", 3: "ira", 4: "miedo", 5: "sorpresa"}
self.to(self.device)
# ---------- Forward & Utils ----------
def forward(self, texts: List[str]) -> torch.Tensor:
embeddings = self.embedder.embed_batch(texts) # [B, D]
logits = self.classifier(embeddings) # [B, C]
return logits
def predict(self, texts: List[str], return_probs: bool = False):
self.eval()
with torch.no_grad():
logits = self.forward(texts)
probs = torch.softmax(logits, dim=-1)
predictions = torch.argmax(probs, dim=-1)
emotions = [self.label_map[p.item()] for p in predictions]
if return_probs:
return emotions, probs.cpu().numpy()
return emotions
def predict_single(self, text: str, return_probs: bool = False):
out = self.predict([text], return_probs=return_probs)
if return_probs:
emotions, probs = out
return emotions[0], probs[0]
return out[0]
# ---------- Fine-tuning helpers ----------
def freeze_encoder(self):
for p in self.embedder.parameters():
p.requires_grad = False
def unfreeze_encoder(self):
for p in self.embedder.parameters():
p.requires_grad = True |