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0I rented I AM CURIOUS-YELLOW from my video sto...0
1\"I Am Curious: Yellow\" is a risible and preten...0
2If only to avoid making this type of film in t...0
3This film was probably inspired by Godard's Ma...0
4Oh, brother...after hearing about this ridicul...0
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", 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" ], "text/plain": [ " palabra conteo\n", "0 the 336749\n", "1 and 164140\n", "2 a 163123\n", "3 of 145864\n", "4 to 135724\n", "5 is 107332\n", "6 br 101871\n", "7 it 96467\n", "8 in 93976\n", "9 i 87690\n", "10 this 76007\n", "11 that 73286\n", "12 s 63602\n", "13 was 48209\n", "14 as 46935\n", "15 for 44345\n", "16 with 44130\n", "17 movie 44047\n", "18 but 42623\n", "19 film 40159" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ---------------------------------------------\n", "# CELDA 1: IMPORTACIONES Y CARGA DE DATOS (EDA)\n", "# ---------------------------------------------\n", "\n", "# 1) Librerías de datos y visualización\n", "from datasets import load_dataset\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from collections import Counter\n", "import re\n", "import warnings\n", "warnings.filterwarnings(\"ignore\") # Ocultar warnings no críticos\n", "\n", "# 2) Carga del dataset IMDb\n", "ds = load_dataset(\"imdb\") # Descarga y cachea automáticamente\n", "train = ds[\"train\"].to_pandas() # Partición de entrenamiento\n", "test = ds[\"test\"].to_pandas() # Partición de prueba\n", "\n", "# 3) Vistazo rápido a los datos\n", "print(f\"Train set: {train.shape[0]} ejemplos\")\n", "print(f\"Test set: {test.shape[0]} ejemplos\\n\")\n", "display(train.head())\n", "\n", "# 4) Distribución de clases\n", "train[\"label\"].value_counts().plot.bar()\n", "plt.title(\"Distribución de etiquetas (0=negativo, 1=positivo)\")\n", "plt.xlabel(\"Etiqueta\")\n", "plt.ylabel(\"Conteo\")\n", "plt.show()\n", "\n", "# 5) Análisis de longitud de reseñas\n", "train[\"length\"] = train[\"text\"].str.len()\n", "display(train[\"length\"].describe()) # Media, percentiles, etc.\n", "train[\"length\"].hist(bins=50)\n", "plt.title(\"Longitud de reseñas (nº caracteres)\")\n", "plt.xlabel(\"Longitud\")\n", "plt.ylabel(\"Frecuencia\")\n", "plt.show()\n", "\n", "# 6) Top 20 palabras más frecuentes\n", "all_words = Counter()\n", "for t in train[\"text\"]:\n", " tokens = re.findall(r\"\\w+\", t.lower()) # Solo palabras\n", " all_words.update(tokens)\n", "most_common = all_words.most_common(20)\n", "display(pd.DataFrame(most_common, columns=[\"palabra\",\"conteo\"]))\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "a6b2c3ea-1bce-4c70-a41e-5ea2ab43e641", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tokenizer cargado: BertTokenizerFast\n", "Batches de entrenamiento: 1563\n", "Batches de validación: 1563\n", "\n", "Tamaños del batch de entrenamiento:\n", " input_ids torch.Size([16, 128])\n", " attention_mask torch.Size([16, 128])\n", " labels torch.Size([16])\n" ] } ], "source": [ "# -------------------------------------------------------------\n", "# CELDA 2: TOKENIZACIÓN Y CREACIÓN DE DATASETS & DATALOADERS\n", "# -------------------------------------------------------------\n", "\n", "# 1) Importaciones necesarias\n", "from transformers import AutoTokenizer\n", "import torch\n", "from torch.utils.data import Dataset, DataLoader\n", "\n", "# 2) Cargar tokenizer BERT\n", "# - \"bert-base-uncased\" es un modelo preentrenado en inglés\n", "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n", "print(\"Tokenizer cargado:\", tokenizer.__class__.__name__)\n", "\n", "# 3) Definir clase Dataset para IMDb\n", "class IMDbDataset(Dataset):\n", " def __init__(self, texts, labels, tokenizer, max_len=128):\n", " self.texts = texts\n", " self.labels = labels\n", " self.tokenizer = tokenizer\n", " self.max_len = max_len\n", "\n", " def __len__(self):\n", " return len(self.texts)\n", "\n", " def __getitem__(self, idx):\n", " text = str(self.texts[idx])\n", " label = int(self.labels[idx])\n", " # Tokenización y padding/truncation\n", " enc = self.tokenizer(\n", " text,\n", " add_special_tokens=True, # Añade [CLS] y [SEP]\n", " max_length=self.max_len, # Longitud fija\n", " truncation=True, # Trunca si es muy largo\n", " padding=\"max_length\", # Rellena si es muy corto\n", " return_attention_mask=True, # Máscara de atención\n", " return_tensors=\"pt\" # Tensores PyTorch\n", " )\n", " return {\n", " \"input_ids\": enc[\"input_ids\"].squeeze(), # Tensor [max_len]\n", " \"attention_mask\": enc[\"attention_mask\"].squeeze(), # Tensor [max_len]\n", " \"labels\": torch.tensor(label, dtype=torch.long)\n", " }\n", "\n", "# 4) Instanciar los datasets de entrenamiento y prueba\n", "train_dataset = IMDbDataset(\n", " texts=train[\"text\"].tolist(),\n", " labels=train[\"label\"].tolist(),\n", " tokenizer=tokenizer,\n", " max_len=128\n", ")\n", "test_dataset = IMDbDataset(\n", " texts=test[\"text\"].tolist(),\n", " labels=test[\"label\"].tolist(),\n", " tokenizer=tokenizer,\n", " max_len=128\n", ")\n", "\n", "# 5) Crear DataLoaders para batching\n", "train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)\n", "test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False)\n", "\n", "# 6) Verificar número de batches y un batch de ejemplo\n", "print(f\"Batches de entrenamiento: {len(train_loader)}\")\n", "print(f\"Batches de validación: {len(test_loader)}\\n\")\n", "\n", "# Obtener y mostrar dimensiones de un batch\n", "batch = next(iter(train_loader))\n", "print(\"Tamaños del batch de entrenamiento:\")\n", "print(\" input_ids \", batch[\"input_ids\"].shape)\n", "print(\" attention_mask\", batch[\"attention_mask\"].shape)\n", "print(\" labels \", batch[\"labels\"].shape)\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "377d78c7-1ee2-4059-bf7f-f441c837426e", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "✔ TrainingArguments configurados\n", "✔ Trainer instanciado\n", "\n", "→ Entrenamiento en curso… puede tardar varios minutos:\n" ] }, { "data": { "text/html": [ "\n", "
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StepTraining Loss
1000.516200
2000.412300
3000.410600
4000.361900
5000.372400
6000.358200
7000.351000
8000.351700
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10000.345700
11000.315200
12000.331200
13000.309800
14000.305700
15000.299400
16000.294500
17000.207200
18000.191500
19000.226800
20000.163900
21000.186600
22000.217100
23000.179600
24000.187800
25000.167500
26000.204400
27000.182700
28000.195400
29000.183800
30000.184700
31000.181700
32000.110600
33000.071500
34000.098600
35000.084200
36000.085900
37000.083100
38000.085900
39000.097200
40000.077700
41000.089600
42000.096600
43000.073400
44000.086300
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\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "✔ Evaluación final en test:\n", " eval_loss: 0.5139\n", " eval_accuracy: 0.8884\n", " eval_f1: 0.8883\n", " eval_runtime: 7263.0685\n", " eval_samples_per_second: 3.4420\n", " eval_steps_per_second: 0.2150\n", " epoch: 3.0000\n", "\n", "✔ Modelo y tokenizer guardados en 'sentiment-bert-model/'\n" ] } ], "source": [ "# -------------------------------------------------------------\n", "# CELDA 3: DEFINICIÓN, ENTRENAMIENTO, EVALUACIÓN Y GUARDADO\n", "# -------------------------------------------------------------\n", "\n", "# 1) Importaciones para el entrenamiento\n", "from transformers import (\n", " AutoModelForSequenceClassification,\n", " TrainingArguments,\n", " Trainer\n", ")\n", "import numpy as np\n", "from sklearn.metrics import accuracy_score, f1_score\n", "\n", "# 2) Carga del modelo BERT para clasificación binaria\n", "# num_labels=2 porque tenemos dos clases: positiva y negativa\n", "model = AutoModelForSequenceClassification.from_pretrained(\n", " \"bert-base-uncased\",\n", " num_labels=2\n", ")\n", "\n", "# 3) Definición de la función de métricas\n", "def compute_metrics(eval_pred):\n", " logits, labels = eval_pred\n", " preds = np.argmax(logits, axis=1)\n", " return {\n", " \"accuracy\": accuracy_score(labels, preds),\n", " \"f1\": f1_score(labels, preds)\n", " }\n", "\n", "# 4) Configuración de los argumentos de entrenamiento\n", "# Adaptado a transformers 4.51.3 con do_train y do_eval\n", "training_args = TrainingArguments(\n", " output_dir=\"./results\", # Carpeta donde guardar checkpoints\n", " num_train_epochs=3, # Número de pasadas sobre el dataset\n", " per_device_train_batch_size=16, # Tamaño de batch en entrenamiento\n", " per_device_eval_batch_size=16, # Tamaño de batch en evaluación\n", " do_train=True, # Ejecutar fase de entrenamiento\n", " do_eval=True, # Ejecutar evaluación al final\n", " logging_dir=\"./logs\", # Carpeta de logs para TensorBoard\n", " logging_steps=100 # Cada cuántos pasos registrar métricas\n", ")\n", "\n", "print(\"✔ TrainingArguments configurados\")\n", "\n", "# 5) Creación del Trainer\n", "trainer = Trainer(\n", " model=model,\n", " args=training_args,\n", " train_dataset=train_dataset, # Viene de la celda 2\n", " eval_dataset=test_dataset, # Viene de la celda 2\n", " compute_metrics=compute_metrics\n", ")\n", "\n", "print(\"✔ Trainer instanciado\")\n", "\n", "# 6) Lanzar el entrenamiento\n", "print(\"\\n→ Entrenamiento en curso… puede tardar varios minutos:\")\n", "trainer.train()\n", "\n", "# 7) Evaluación final en test set\n", "metrics = trainer.evaluate()\n", "print(\"\\n✔ Evaluación final en test:\")\n", "for name, value in metrics.items():\n", " print(f\" {name}: {value:.4f}\")\n", "\n", "# 8) Guardar modelo y tokenizer para despliegue\n", "trainer.save_model(\"sentiment-bert-model\") # Pesos y config\n", "tokenizer.save_pretrained(\"sentiment-bert-model\") # Archivos del tokenizer\n", "print(\"\\n✔ Modelo y tokenizer guardados en 'sentiment-bert-model/'\")\n", "\n" ] }, { "cell_type": "code", "execution_count": 14, "id": "0db4d022-aa06-40e2-b4b8-ab6b1e226a16", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: huggingface_hub in c:\\users\\light\\documents\\sentiment-analysis-bert\\venv\\lib\\site-packages (0.31.1)\n", "Collecting huggingface_hub\n", " Using cached huggingface_hub-0.32.0-py3-none-any.whl.metadata (14 kB)\n", "Requirement already satisfied: filelock in c:\\users\\light\\documents\\sentiment-analysis-bert\\venv\\lib\\site-packages (from huggingface_hub) (3.18.0)\n", "Requirement already satisfied: fsspec>=2023.5.0 in c:\\users\\light\\documents\\sentiment-analysis-bert\\venv\\lib\\site-packages (from huggingface_hub) (2025.3.0)\n", "Requirement already satisfied: packaging>=20.9 in c:\\users\\light\\documents\\sentiment-analysis-bert\\venv\\lib\\site-packages (from huggingface_hub) (25.0)\n", "Requirement already satisfied: pyyaml>=5.1 in c:\\users\\light\\documents\\sentiment-analysis-bert\\venv\\lib\\site-packages (from huggingface_hub) (6.0.2)\n", "Requirement already satisfied: requests in c:\\users\\light\\documents\\sentiment-analysis-bert\\venv\\lib\\site-packages (from huggingface_hub) (2.32.3)\n", "Requirement already satisfied: tqdm>=4.42.1 in c:\\users\\light\\documents\\sentiment-analysis-bert\\venv\\lib\\site-packages (from huggingface_hub) (4.67.1)\n", "Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\users\\light\\documents\\sentiment-analysis-bert\\venv\\lib\\site-packages (from huggingface_hub) (4.13.2)\n", "Requirement already satisfied: colorama in c:\\users\\light\\documents\\sentiment-analysis-bert\\venv\\lib\\site-packages (from tqdm>=4.42.1->huggingface_hub) (0.4.6)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\light\\documents\\sentiment-analysis-bert\\venv\\lib\\site-packages (from requests->huggingface_hub) (3.4.2)\n", "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\light\\documents\\sentiment-analysis-bert\\venv\\lib\\site-packages (from requests->huggingface_hub) (3.10)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\light\\documents\\sentiment-analysis-bert\\venv\\lib\\site-packages (from requests->huggingface_hub) (2.4.0)\n", "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\light\\documents\\sentiment-analysis-bert\\venv\\lib\\site-packages (from requests->huggingface_hub) (2025.4.26)\n", "Using cached huggingface_hub-0.32.0-py3-none-any.whl (509 kB)\n", "Installing collected packages: huggingface_hub\n", " Attempting uninstall: huggingface_hub\n", " Found existing installation: huggingface-hub 0.31.1\n", " Uninstalling huggingface-hub-0.31.1:\n", " Successfully uninstalled huggingface-hub-0.31.1\n", "Successfully installed huggingface_hub-0.32.0\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install --upgrade huggingface_hub" ] }, { "cell_type": "code", "execution_count": null, "id": "031f4c70-5a75-4d89-9905-bbf8016b7b68", "metadata": {}, "outputs": [], "source": [ "import os\n", "from huggingface_hub import HfApi, login, create_repo\n", "\n", "# --- CONFIGURACIÓN CRUCIAL ---\n", "HF_USERNAME = \"Light-Dav\" # ¡Tu nombre de usuario correcto!\n", "HF_TOKEN = \"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\" # ¡Tu token de Hugging Face!\n", "\n", "REPO_NAME = \"sentiment-analysis-full-project\" # <--- ¡Confirma que este es el nombre EXACTO del repo en HF Hub!\n", "REPO_ID = f\"{HF_USERNAME}/{REPO_NAME}\"\n", "\n", "# --- AJUSTE DE LA RUTA DEL PROYECTO ---\n", "# Esto asegurará que siempre suba la raíz de tu proyecto, sin importar dónde esté el notebook.\n", "# Asumimos que tu carpeta principal es 'sentiment-analysis-bert'\n", "# y que tu notebook está dentro de ella o en una subcarpeta (como 'notebooks').\n", "# Puedes ajustar esta ruta si tu proyecto no está en C:\\Users\\Light\\Documents\\sentiment-analysis-bert\n", "PROJECT_ROOT_DIR = \"C:\\\\Users\\\\Light\\\\Documents\\\\sentiment-analysis-bert\"\n", "\n", "\n", "print(f\"La ruta del proyecto a subir es: {PROJECT_ROOT_DIR}\")\n", "print(f\"El repositorio de destino será: {REPO_ID}\")\n", "\n", "# --- EJECUCIÓN ---\n", "try:\n", " print(\"\\n--- Paso 1: Iniciando sesión en Hugging Face Hub ---\")\n", " login(token=HF_TOKEN)\n", " print(\"Inicio de sesión exitoso en Hugging Face Hub.\")\n", "\n", " api = HfApi()\n", "\n", " print(f\"\\n--- Paso 2: Creando/Verificando el repositorio '{REPO_ID}' en Hugging Face Hub ---\")\n", " create_repo(repo_id=REPO_ID, private=False, exist_ok=True, token=HF_TOKEN)\n", " print(f\"Repositorio '{REPO_ID}' creado o ya existe en Hugging Face Hub.\")\n", "\n", " print(f\"\\n--- Paso 3: Subiendo el contenido de '{PROJECT_ROOT_DIR}' a '{REPO_ID}' ---\")\n", " api.upload_folder(\n", " folder_path=PROJECT_ROOT_DIR,\n", " repo_id=REPO_ID,\n", " repo_type=\"model\",\n", " commit_message=\"Initial upload of full sentiment analysis project (code, notebooks, data)\",\n", " # Ahora 'git_exclude' debería funcionar después de la actualización.\n", " git_exclude=[\n", " \"venv/\", # Excluye el entorno virtual\n", " \"**/__pycache__/\", # Excluye archivos de caché de Python\n", " \"*.ipynb_checkpoints/\", # Excluye checkpoints de Jupyter si están en la raíz\n", " \"notebooks/.ipynb_checkpoints/\", # Excluye checkpoints específicos de notebooks\n", " # Puedes añadir más exclusiones aquí, ej: \"data/raw_large_files/\"\n", " # También podrías querer excluir tu modelo ya subido si ya está en otro repo:\n", " # \"sentiment-bert-model/\"\n", " ],\n", " multi_commits=True # Útil para uploads grandes\n", " )\n", "\n", " print(\"\\n¡Carga de todo el proyecto completada con éxito!\")\n", " print(f\"Tu proyecto está ahora disponible en: https://huggingface.co/{REPO_ID}\")\n", "\n", "except Exception as e:\n", " print(f\"\\nERROR al subir el proyecto a Hugging Face Hub: {e}\")\n", " print(\"Verifica los siguientes puntos:\")\n", " print(f\"- Tu nombre de usuario: '{HF_USERNAME}' y tu token son correctos y tienen permisos de escritura.\")\n", " print(f\"- El nombre del repositorio: '{REPO_NAME}' es el que deseas.\")\n", " print(f\"- La ruta local del proyecto: '{PROJECT_ROOT_DIR}' es la correcta y contiene todos tus archivos.\")\n", " print(\"- Tienes conexión a internet.\")\n", " print(\"- ¡MUY IMPORTANTE: Asegúrate de haber reiniciado el kernel de Jupyter después de actualizar la librería!\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.10" } }, "nbformat": 4, "nbformat_minor": 5 }