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MiaMotion/.gitattributes

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MiaMotion/README.md

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----
-license: gpl-2.0
----

MiaMotion/best_model.pt

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-version https://git-lfs.github.com/spec/v1
-oid sha256:80051b5bd4ac73173990e59b712e8a7300b3a23ae5d23f13ffbec2715651d2a0
-size 1315023871

MiaMotion/config.json

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-{
-    "base_model_id": "dccuchile/bert-base-spanish-wwm-cased",
-    "pretrained_encoder": "beto",
-    "max_length": 128,
-    "hidden1": 128,
-    "hidden2": 64,
-    "num_classes": 6,
-    "dropout": 0.3
-}

MiaMotion/emotion_classifier_model.py

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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

MiaMotion/label_map.json

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-{
-    "0": "tristeza",
-    "1": "alegría",
-    "2": "amor",
-    "3": "ira",
-    "4": "miedo",
-    "5": "sorpresa"
-}

MiaMotion/requirements.txt

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-aiohappyeyeballs==2.6.1
-aiohttp==3.12.15
-aiosignal==1.4.0
-anyio==4.10.0
-attrs==25.3.0
-beautifulsoup4==4.13.5
-certifi==2025.8.3
-charset-normalizer==3.4.3
-contourpy==1.3.3
-cycler==0.12.1
-datasets==4.1.1
-deep-translator==1.11.4
-dill==0.4.0
-filelock==3.19.1
-fonttools==4.60.1
-frozenlist==1.7.0
-fsspec==2025.9.0
-googletrans==4.0.2
-h11==0.16.0
-h2==4.3.0
-hf-xet==1.1.10
-hpack==4.1.0
-httpcore==1.0.9
-httpx==0.28.1
-huggingface-hub==0.35.0
-hyperframe==6.1.0
-idna==3.10
-ijson==3.4.0
-Jinja2==3.1.6
-joblib==1.5.2
-kiwisolver==1.4.9
-MarkupSafe==3.0.3
-matplotlib==3.10.7
-mpmath==1.3.0
-multidict==6.6.4
-multiprocess==0.70.16
-networkx==3.5
-numpy==2.3.3
-nvidia-cublas-cu12==12.8.4.1
-nvidia-cuda-cupti-cu12==12.8.90
-nvidia-cuda-nvrtc-cu12==12.8.93
-nvidia-cuda-runtime-cu12==12.8.90
-nvidia-cudnn-cu12==9.10.2.21
-nvidia-cufft-cu12==11.3.3.83
-nvidia-cufile-cu12==1.13.1.3
-nvidia-curand-cu12==10.3.9.90
-nvidia-cusolver-cu12==11.7.3.90
-nvidia-cusparse-cu12==12.5.8.93
-nvidia-cusparselt-cu12==0.7.1
-nvidia-nccl-cu12==2.27.5
-nvidia-nvjitlink-cu12==12.8.93
-nvidia-nvshmem-cu12==3.3.20
-nvidia-nvtx-cu12==12.8.90
-packaging==25.0
-pandas==2.3.2
-pillow==12.0.0
-propcache==0.3.2
-protobuf==6.33.0
-pyarrow==21.0.0
-pyparsing==3.2.5
-python-dateutil==2.9.0.post0
-pytz==2025.2
-PyYAML==6.0.2
-regex==2025.10.23
-requests==2.32.5
-safetensors==0.6.2
-scikit-learn==1.7.2
-scipy==1.16.2
-seaborn==0.13.2
-setuptools==80.9.0
-six==1.17.0
-sniffio==1.3.1
-soupsieve==2.8
-sympy==1.14.0
-threadpoolctl==3.6.0
-tiktoken==0.12.0
-tokenizers==0.22.1
-torch==2.9.0
-tqdm==4.67.1
-transformers==4.57.1
-triton==3.5.0
-typing_extensions==4.15.0
-tzdata==2025.2
-urllib3==2.5.0
-xxhash==3.5.0
-yarl==1.20.1