Respan Dataset Explorer

Спасибо. На основе полученной информации о сцене, сделай краткое описание кадра по пунктам. Первый пункт, опиши помещение в котором происходит действие, опиши цветовую палитру, опиши свет который присутствует в сцене. Второй пункт, опиши действующих героев в сцене, как они выглядят, в чем одеты, где они находят в и какой они позе находятся. Третий пункт, опиши действие, которое происходит в сцене. Четвертый пункт, дай три примера, которые можно взять за рефернс к этой сцене. Примеры бери из фильмов. 

1. Помещение: маленький и уютный кабинет невролога в больнице; цветовая палитра: нейтральные и теплые оттенки (бежевые, коричневые, серые); свет: естественный свет проникает через окно, также есть мягкий искусственный свет от настольной лампы и тлеющие свечи на подоконнике.

2. Герои: Доктор Джон Пембертон – седой мужчина средних лет, носит очки и одет в белый лабораторный халат поверх черного свитера и серых брюк, сидящий за рабочим столом, изучает медицинские отчеты; Линда Мартин – молодая, энергичная тележурналистка с светлыми волосами до плеч, одетая в синий блейзер и черную юбку-карандаш, активно входит в кабинет.

3. Действие: В офис входит Линда и задает вопрос доктору Пембертону о странном поведении его пожилых пациентов, связанным с тайной программой омоложения.

4. Референсы из фильмов: 
   а) Сцена в кабинете доктора из фильма "Профессор" (2018) – для атмосферы тесного рабочего пространства и профессиональной обстановки.
   б) Сцена в больнице из фильма "Человек паук: Через вселенные" (2018) – для примера взаимодействия персонажей и динамичного диалога.
   в) Сцена с доктором Стивеном Стрейнджем из фильма "Доктор Стрэйндж" (2016) – для представления вида и обстановки кабинета медицинского специалиста.

hi

Hello! How can I assist you today?

¿Qué le falta a este código para obtener una predicción y no <pad> <pad>?: import torch
import torch.nn as nn
import time
from torchtext.datasets import PennTreebank
from torchtext.data.functional import to_map_style_dataset
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
from torch.utils.data import DataLoader

# 1. Define the model
class TransformerModel(nn.Module):
    def __init__(self, vocab_size, d_model, nhead, num_layers, dropout=0.1):
        super(TransformerModel, self).__init__()
        self.embedding = nn.Embedding(vocab_size, d_model)
        self.transformer = nn.Transformer(d_model, nhead, num_layers, dropout=dropout)
        self.fc = nn.Linear(d_model, vocab_size)

    def forward(self, src, tgt):
        src = self.embedding(src)
        tgt = self.embedding(tgt)
        x = self.transformer(src, tgt)
        x = self.fc(x)
        return x


# 2. Preparar datos
train_data_raw = to_map_style_dataset(PennTreebank(split='train'))
tokenizer = get_tokenizer("spacy", "en_core_web_sm")

# Agregar tokens especiales (UNK y PAD) al vocabulario
specials = ['<unk>','<pad>']
vocab = build_vocab_from_iterator((tokenizer(y) for y in train_data_raw), specials=specials)
vocab.set_default_index(vocab['<unk>'])

# Dividir los datos en conjuntos de entrenamiento y validación
train_ratio = 0.8
train_data_size = int(len(train_data_raw) * train_ratio)
train_data = train_data_raw[:train_data_size]
valid_data = train_data_raw[train_data_size:]


# 3. Entrenar el modelo
def generate_pairs(tokens, shift=1):
    source = [tokens[i] for i in range(0, len(tokens) - shift)]
    target = [tokens[i] for i in range(shift, len(tokens))]
    return source, target

def tokenize_data(text, max_seq_len=None):
    tokens = tokenizer(text)[:max_seq_len]
    return torch.tensor([vocab[token] if token in vocab else vocab['<unk>'] for token in tokens], dtype=torch.long)

def create_batches(data, batch_size, max_seq_len=None):
    batched_data = []
    for i in range(0, len(data), batch_size):
        batched_text = []
        for text in data[i:i + batch_size]:
            tokens = tokenize_data(text, max_seq_len=max_seq_len)
            if len(tokens) <= 1:
                continue
            batched_text.append(tokens)
        batched_data.append(batched_text)
    return batched_data

def collate_fn(batch):
    batch_srcs = []
    batch_tgts = []
    max_len = max([len(text) for text in batch])
    for text in batch:
        source, target = generate_pairs(text)
        source = torch.tensor(source, dtype=torch.long)
        target = torch.tensor(target, dtype=torch.long)
        source = torch.cat([source, torch.full((max_len - len(source),), vocab['<pad>'], dtype=torch.long)], dim=0)
        target = torch.cat([target, torch.full((max_len - len(target),), vocab['<pad>'], dtype=torch.long)], dim=0)
        batch_srcs.append(source)
        batch_tgts.append(target)
    return torch.stack(batch_srcs).T, torch.stack(batch_tgts).T


vocab_size = len(vocab)
d_model = 768
nhead = 8  # Ajuste de hparámetro
num_layers = 6  # Ajuste de hiperparámetro
num_epochs = 30  # Increase the number of epochs
learning_rate = 1e-3  # Experiment with different learning rates
optimizer_choice = "Adam"  # Choose the optimizer: "Adam", "RMSprop", or "Adagrad"
batch_size = 64  # Ajuste de hiperparámetro
max_seq_len = 50  # Limitar la longitud máxima de la secuencia para evitar problemas de memoria
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

train_batches = create_batches(train_data, batch_size, max_seq_len=max_seq_len)
valid_batches = create_batches(valid_data, batch_size, max_seq_len=max_seq_len)

train_loader = DataLoader(train_batches, batch_size=None, collate_fn=collate_fn)
valid_loader = DataLoader(valid_batches, batch_size=None, collate_fn=collate_fn)

model = TransformerModel(vocab_size=vocab_size, d_model=d_model, nhead=nhead, num_layers=num_layers, dropout=0.1).to(device)
loss_function = nn.CrossEntropyLoss()

# Use different optimizers
if optimizer_choice == "Adam":
    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
elif optimizer_choice == "RMSprop":
    optimizer = torch.optim.RMSprop(model.parameters(), lr=learning_rate)
elif optimizer_choice == "Adagrad":
    optimizer = torch.optim.Adagrad(model.parameters(), lr=learning_rate)
else:
    raise ValueError("Invalid optimizer choice")

def train(model, epoch):
    model.train()
    train_loss = 0
    for i, (source, target) in enumerate(train_loader):
        source = source.to(device)
        target = target.to(device)
        optimizer.zero_grad()
        output = model(source, target[:-1])
        loss = loss_function(output.reshape(-1, vocab_size), target[1:].reshape(-1))
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
        if i % 1000 == 0:
            print(f'Epoch: {epoch + 1},\tLoss: {loss.item()}')
    return train_loss / len(train_loader)


for epoch in range(num_epochs):
    start_time = time.time()
    train_loss = train(model, epoch)
    end_time = time.time()
    elapsed_time = end_time - start_time
    print(f"Epoch: {epoch + 1},\tAverage Loss: {train_loss},\tTime taken: {elapsed_time} seconds")

# 4. Evaluar el modelo en el conjunto de validación
def evaluate(model, valid_loader):
    model.eval()
    valid_loss = 0
    with torch.no_grad():
        for source, target in valid_loader:
            source = source.to(device)
            target = target.to(device)
            output = model(source, target[:-1])
            loss = loss_function(output.reshape(-1, vocab_size), target[1:].reshape(-1))
            valid_loss += loss.item()
    return valid_loss / len(valid_loader)


# 5. Entrenar y evaluar el modelo en cada época
for epoch in range(num_epochs):
    start_time = time.time()
    train_loss = train(model, epoch)
    valid_loss = evaluate(model, valid_loader)
    end_time = time.time()
    elapsed_time = end_time - start_time
    print(f"Epoch: {epoch + 1},\tAverage Train Loss: {train_loss},\tAverage Valid Loss: {valid_loss},\tTime taken: {elapsed_time} seconds")


# 6. Guardar el modelo entrenado
torch.save(model.state_dict(), "trained_model.pth")

# 7. Cargar el modelo
def load_model(path, device):
    loaded_model = TransformerModel(vocab_size=vocab_size, d_model=d_model, nhead=nhead, num_layers=num_layers).to(device)
    loaded_model.load_state_dict(torch.load(path))
    loaded_model.eval()
    return loaded_model

loaded_model = load_model("trained_model.pth", device)

# 8. Predecir texto
def decode_output(output):
    probabilities = torch.softmax(output, dim=2)
    predicted_indices = torch.argmax(probabilities, dim=2)
    return " ".join(vocab.lookup_tokens(predicted_indices.cpu().numpy().squeeze().tolist()))

def predict_next_token(input_text, model):
    tokens = tokenize_data(input_text)
    source, target = generate_pairs(tokens, shift=1)
    source = torch.tensor(source, dtype=torch.long).unsqueeze(1).to(device)
    target = torch.tensor(target, dtype=torch.long).unsqueeze(1).to(device)
    with torch.no_grad():
        output = model(source, target[:-1])
        return decode_output(output)

input_text = "My favorite color is"
predicted_output = predict_next_token(input_text, loaded_model)
print(f"Predicted output: {predicted_output}")

Algunas posibles razones por las que se obtiene `<pad> <pad>` como predicción son: una longitud de secuencia de entrada muy corta en el texto de entrada, un vocabulario limitado o un modelo que no ha aprendido de manera efectiva la distribución del lenguaje. La longitud máxima del token y los tamaños de lote pueden ser ajustados para mejorar el rendimiento del modelo.

También puedes intentar reducir el error de aprendizaje, cambiar el optimizador, mejorar la composición de los datos de entrenamiento y ajustar la longitud de la secuencia máxima.

Aquí hay algunas modificaciones posibles en el modelo y el proceso de predicción:

1. Durante la predicción, permitir una secuencia de entrada más larga para que el modelo tenga más contexto:
```python
def predict_next_token(input_text, model, max_seq_len=None):
    tokens = tokenize_data(input_text, max_seq_len=max_seq_len)
    ...
```

2. Ajustar la función `decode_output()` para evitar la traducción de `<pad>`. Esto evitará la aparición de `<pad>` en la salida. Aquí hay una posible solución:

```python
def decode_output(output):
    probabilities = torch.softmax(output, dim=2)
    predicted_indices = torch.argmax(probabilities, dim=2)
    predicted_tokens = vocab.lookup_tokens(predicted_indices.cpu().numpy().squeeze().tolist())
    return " ".join(token for token in predicted_tokens if token != '<pad>')
```

3. Asegúrate de que el modelo esté ajustado adecuadamente en los datos de entrenamiento.

En última instancia, obtener una buena distribución del lenguaje depende en gran medida de la calidad y cantidad de los datos de entrenamiento, así como de la arquitectura del modelo y los hiperparámetros. Asegúrate de entrenar el modelo con suficientes datos de entrenamiento y ajustar los hiperparámetros.

¿Cuál es un conjunto de datos de texto mejor que PeenTreebank para entren el modelo que te mostré?

Un conjunto de datos de entrenamiento más grande y diverso podría mejorar el rendimiento del modelo. Algunos conjuntos de datos ampliamente utilizados incluyen:

1. **Wikitext**: El conjunto de datos Wikitext contiene textos de los artículos de Wikipedia y es más grande y diverso que Penn Treebank. Tiene dos variantes populares: Wikitext-2 y Wikitext-103. La primera tiene aproximadamente 2 millones de tokens, mientras que la segunda tiene más de 100 millones de tokens.
   - En Torchtext, puedes usar `torchtext.datasets.WikiText2` o `torchtext.datasets.WikiText103` para cargar fácilmente estos conjuntos de datos. Por ejemplo:
```python
from torchtext.datasets import WikiText2

train_iter, valid_iter, test_iter = WikiText2()
```

2. **Common Crawl**: Common Crawl es un proyecto que recopila y archiva miles de millones de páginas web en una amplia gama de idiomas con miles de millones de tokens. Está organizado en formatos de texto puro y se puede utilizar para entrenar modelos de lenguaje de una escala muy grande.
   - Puedes acceder al conjunto de datos de Common Crawl a través de [su página web](https://commoncrawl.org/).

3. **Gutenberg Books**: El Proyecto Gutenberg es una colección digital de miles de libros de dominio público en varios idiomas. Estos libros pueden proporcionar un corpus bien estructurado que incluye textos literarios diversos y ricos.
   - Puedes obtener más detalles y acceder a este conjunto de datos en el [Proyecto Gutenberg](https://www.gutenberg.org/).

En general, tu elección del conjunto de datos de entrenamiento debe basarse en el tipo de texto que esperas generar y tus requisitos específicos de predicción. Asegúrate de preprocesar y limpiar adecuadamente los datos de entrada antes de alimentarlos al modelo.

Por favor, configúrame el código para usar Wikitext y ajústame los siguientes parámetros para que genere una buena predicción, así se demore el proceso de entrenamiento: vocab_size = len(vocab)
d_model = 768
nhead = 8 # Ajuste de hparámetro
num_layers = 6 # Ajuste de hiperparámetro
num_epochs = 30 # Increase the number of epochs
learning_rate = 1e-3 # Experiment with different learning rates
optimizer_choice = “Adam” # Choose the optimizer: “Adam”, “RMSprop”, or “Adagrad”
batch_size = 64 # Ajuste de hiperparámetro
max_seq_len = 50 # Limitar la longitud máxima de la secuencia para evitar problemas de memoria

He actualizado el código para que utilice el conjunto de datos Wikitext-2 y ajusté algunos hiperparámetros según tus requerimientos. Ten en cuenta que entrenar un modelo durante muchas épocas o en largas secuencias de texto puede aumentar el tiempo de entrenamiento y el uso de la memoria. 

```python
import torch
import torch.nn as nn
from torchtext.datasets import WikiText2
from torchtext.data.functional import to_map_style_dataset
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
from torch.utils.data import DataLoader

# 1. Define the model
class TransformerModel(nn.Module):
    def __init__(self, vocab_size, d_model, nhead, num_layers, dropout=0.1):
        super(TransformerModel, self).__init__()
        self.embedding = nn.Embedding(vocab_size, d_model)
        self.transformer = nn.Transformer(d_model, nhead, num_layers, dropout=dropout)
        self.fc = nn.Linear(d_model, vocab_size)

    def forward(self, src, tgt):
        src = self.embedding(src)
        tgt = self.embedding(tgt)
        x = self.transformer(src, tgt)
        x = self.fc(x)
        return x

# 2. Preparar datos
train_data_raw, valid_data_raw, _ = WikiText2(split=('train', 'valid', 'test'))
train_data_raw = to_map_style_dataset(train_data_raw)
valid_data_raw = to_map_style_dataset(valid_data_raw)

tokenizer = get_tokenizer("spacy", "en_core_web_sm")

# Agregar tokens especiales (UNK y PAD) al vocabulario
specials = ['<unk>','<pad>']
vocab = build_vocab_from_iterator((tokenizer(y) for y in train_data_raw), specials=specials)
vocab.set_default_index(vocab['<unk>'])

vocab_size = len(vocab)
d_model = 768
nhead = 8
num_layers = 6
num_epochs = 30
learning_rate = 1e-3
optimizer_choice = 'Adam'
batch_size = 64
max_seq_len = 50
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Rest of the code remains unchanged ...
```

Este código utiliza el conjunto de datos Wikitext-2 al cargarlo mediante `torchtext.datasets.WikiText2`. Los hiperparámetros se han mantenido igual que los de tus requerimientos. Sin embargo, podrías ajustar `num_epochs`, `max_seq_len`, `learning_rate`, `optimizer_choice` y otros hiperparámetros según la calidad de las predicciones y la capacidad de tu equipo.

Ten en cuenta que aunque he cambiado el código para usar Wikitext-2 en lugar de Penn Treebank, los detalles de cómo se tokeniza, se crea el vocabulario y los lotes siguen siendo los mismos que en tu código original. Dependiendo de la adecuación de los datos y la arquitectura del modelo, la calidad de las predicciones puede variar.

Recuerda que es posible que debas experimentar con distintos hiperparámetros y ajustar el preprocesamiento de los datos para obtener una predicción deseable. Estos cambios pueden requerir tiempos más largos de entrenamiento y ajuste, así que ten paciencia al experimentar con distintas configuraciones y conjuntos de datos.

Por favor, dame una buena configuración en los hiperparámetros para evitar esto: Epoch: 1,	Loss: 9.567360877990723
Epoch: 1,	Average Loss: 3.7003726646474107,	Time taken: 292.0087857246399 seconds
Epoch: 2,	Loss: 3.559448480606079
Epoch: 2,	Average Loss: 3.618738709293844,	Time taken: 292.34765434265137 seconds
Epoch: 3,	Loss: 3.5392792224884033
Epoch: 3,	Average Loss: 3.605547330225375,	Time taken: 292.08597803115845 seconds
Epoch: 4,	Loss: 3.522873640060425
Epoch: 4,	Average Loss: 3.5965943490597687,	Time taken: 292.3419635295868 seconds
Epoch: 5,	Loss: 3.5057291984558105
Epoch: 5,	Average Loss: 3.5902317744244185,	Time taken: 291.74957275390625 seconds
Epoch: 6,	Loss: 3.5161614418029785
Epoch: 6,	Average Loss: 3.587415994346822,	Time taken: 292.1076292991638 seconds
Epoch: 7,	Loss: 3.511284589767456
Epoch: 7,	Average Loss: 3.5886631560416276,	Time taken: 291.77211713790894 seconds
Epoch: 8,	Loss: 3.5088226795196533
Epoch: 8,	Average Loss: 3.5876666606605734,	Time taken: 291.85539841651917 seconds
Epoch: 9,	Loss: 3.505660057067871
Epoch: 9,	Average Loss: 3.586097168831771,	Time taken: 291.7574985027313 seconds
Epoch: 10,	Loss: 3.5023460388183594
Epoch: 10,	Average Loss: 3.584595971687665,	Time taken: 291.9380223751068 seconds
Epoch: 11,	Loss: 3.5048999786376953
Epoch: 11,	Average Loss: 3.583113308641847,	Time taken: 292.0109279155731 seconds
Epoch: 12,	Loss: 3.5031940937042236
Epoch: 12,	Average Loss: 3.580842248840477,	Time taken: 291.76497888565063 seconds
Epoch: 13,	Loss: 3.50352144241333
Epoch: 13,	Average Loss: 3.5786439665370113,	Time taken: 291.74229407310486 seconds
Epoch: 14,	Loss: 3.497807025909424
Epoch: 14,	Average Loss: 3.5781566240035083,	Time taken: 291.3640627861023 seconds
Epoch: 15,	Loss: 3.4952096939086914
Epoch: 15,	Average Loss: 3.5777785469823917,	Time taken: 291.2954170703888 seconds
Epoch: 16,	Loss: 3.497789144515991
Epoch: 16,	Average Loss: 3.577572054282794,	Time taken: 291.54339694976807 seconds
Epoch: 17,	Loss: 3.4942948818206787
Epoch: 17,	Average Loss: 3.5770502394143167,	Time taken: 291.37605381011963 seconds
Epoch: 18,	Loss: 3.4956154823303223
Epoch: 18,	Average Loss: 3.576229152117392,	Time taken: 290.98668670654297 seconds
Epoch: 19,	Loss: 3.492783546447754
Epoch: 19,	Average Loss: 3.5748239583388934,	Time taken: 290.95834708213806 seconds
Epoch: 20,	Loss: 3.4896273612976074
Epoch: 20,	Average Loss: 3.572025065639626,	Time taken: 290.86392188072205 seconds
Epoch: 21,	Loss: 3.4918222427368164
Epoch: 21,	Average Loss: 3.5701150871501675,	Time taken: 290.33885979652405 seconds
Epoch: 22,	Loss: 3.485771894454956
Epoch: 22,	Average Loss: 3.568570154701349,	Time taken: 289.31501388549805 seconds
Epoch: 23,	Loss: 3.4859931468963623
Epoch: 23,	Average Loss: 3.567728873894695,	Time taken: 289.6293840408325 seconds
Epoch: 24,	Loss: 3.4873950481414795
Epoch: 24,	Average Loss: 3.567380349898973,	Time taken: 289.5343222618103 seconds
Epoch: 25,	Loss: 3.4872074127197266
Epoch: 25,	Average Loss: 3.5652468594308133,	Time taken: 289.4293782711029 seconds
Epoch: 26,	Loss: 3.4843451976776123
Epoch: 26,	Average Loss: 3.5643289098268225,	Time taken: 289.92592668533325 seconds
Epoch: 27,	Loss: 3.4866209030151367
Epoch: 27,	Average Loss: 3.563500219878135,	Time taken: 289.3788197040558 seconds
Epoch: 28,	Loss: 3.481735944747925
Epoch: 28,	Average Loss: 3.563423510740012,	Time taken: 289.35951137542725 seconds
Epoch: 29,	Loss: 3.4829232692718506
Epoch: 29,	Average Loss: 3.5658165968869575,	Time taken: 289.45616269111633 seconds
Epoch: 30,	Loss: 3.487635374069214
Epoch: 30,	Average Loss: 3.5698270244743435,	Time taken: 288.84308218955994 seconds
Epoch: 1,	Loss: 3.4927570819854736
Epoch: 1,	Average Train Loss: 3.5711315339962337,	Average Valid Loss: 3.6290017239975207,	Time taken: 312.00482964515686 seconds
Epoch: 2,	Loss: 3.489135503768921
Epoch: 2,	Average Train Loss: 3.571447698788951,	Average Valid Loss: 3.6297632943500173,	Time taken: 312.0168857574463 seconds
Epoch: 3,	Loss: 3.4861600399017334
Epoch: 3,	Average Train Loss: 3.5712698911079435,	Average Valid Loss: 3.6287157372994856,	Time taken: 311.7727360725403 seconds
Epoch: 4,	Loss: 3.481497049331665
Epoch: 4,	Average Train Loss: 3.5710239242691504,	Average Valid Loss: 3.625587866161809,	Time taken: 311.834326505661 seconds
Epoch: 5,	Loss: 3.4813199043273926
Epoch: 5,	Average Train Loss: 3.5704481733162594,	Average Valid Loss: 3.6241414601152595,	Time taken: 311.65282440185547 seconds
Epoch: 6,	Loss: 3.4821066856384277
Epoch: 6,	Average Train Loss: 3.5700396979263074,	Average Valid Loss: 3.624173494902524,	Time taken: 311.28872323036194 seconds
Epoch: 7,	Loss: 3.4856691360473633
Epoch: 7,	Average Train Loss: 3.569556606586442,	Average Valid Loss: 3.6256951039487664,	Time taken: 311.3145525455475 seconds
Epoch: 8,	Loss: 3.485272169113159
Epoch: 8,	Average Train Loss: 3.5691312433648927,	Average Valid Loss: 3.6258612672487893,	Time taken: 311.2050988674164 seconds
Epoch: 9,	Loss: 3.483003616333008
Epoch: 9,	Average Train Loss: 3.5687793647381745,	Average Valid Loss: 3.62409432367845,	Time taken: 311.68029260635376 seconds
Epoch: 10,	Loss: 3.4820141792297363
Epoch: 10,	Average Train Loss: 3.5684012791049797,	Average Valid Loss: 3.6230082620273936,	Time taken: 311.22282123565674 seconds
Epoch: 11,	Loss: 3.4847073554992676
Epoch: 11,	Average Train Loss: 3.567918264367281,	Average Valid Loss: 3.624123398101691,	Time taken: 310.99913930892944 seconds
Epoch: 12,	Loss: 3.4879515171051025
Epoch: 12,	Average Train Loss: 3.567450798056425,	Average Valid Loss: 3.625333491599921,	Time taken: 310.98185873031616 seconds
Epoch: 13,	Loss: 3.485262393951416
Epoch: 13,	Average Train Loss: 3.5671426356971945,	Average Valid Loss: 3.6250820737896543,	Time taken: 311.36055850982666 seconds
Epoch: 14,	Loss: 3.483027935028076
Epoch: 14,	Average Train Loss: 3.565806134572047,	Average Valid Loss: 3.622268743587263,	Time taken: 311.6248605251312 seconds
Epoch: 15,	Loss: 3.4813599586486816
Epoch: 15,	Average Train Loss: 3.5641346063903985,	Average Valid Loss: 3.6236986445658133,	Time taken: 311.5210154056549 seconds
Epoch: 16,	Loss: 3.4823060035705566
Epoch: 16,	Average Train Loss: 3.5628339691307156,	Average Valid Loss: 3.6223301923636235,	Time taken: 311.5788004398346 seconds
Epoch: 17,	Loss: 3.482267141342163
Epoch: 17,	Average Train Loss: 3.5627272088264785,	Average Valid Loss: 3.6255278822147483,	Time taken: 311.63774728775024 seconds
Epoch: 18,	Loss: 3.484595775604248
Epoch: 18,	Average Train Loss: 3.5622248064882402,	Average Valid Loss: 3.624577581882477,	Time taken: 311.4593484401703 seconds
Epoch: 19,	Loss: 3.4822275638580322
Epoch: 19,	Average Train Loss: 3.5611390410267356,	Average Valid Loss: 3.624938767967802,	Time taken: 311.02139925956726 seconds
Epoch: 20,	Loss: 3.4826202392578125
Epoch: 20,	Average Train Loss: 3.5610133885430746,	Average Valid Loss: 3.6282201156471716,	Time taken: 311.3768696784973 seconds
Epoch: 21,	Loss: 3.479175090789795
Epoch: 21,	Average Train Loss: 3.561582879875096,	Average Valid Loss: 3.6201832077719946,	Time taken: 310.644433259964 seconds
Epoch: 22,	Loss: 3.4808602333068848
Epoch: 22,	Average Train Loss: 3.564162126965396,	Average Valid Loss: 3.6246202606143374,	Time taken: 310.5687975883484 seconds
Epoch: 23,	Loss: 3.4795570373535156
Epoch: 23,	Average Train Loss: 3.5652912070995955,	Average Valid Loss: 3.6242916367270728,	Time taken: 310.62603640556335 seconds
Epoch: 24,	Loss: 3.481790065765381
Epoch: 24,	Average Train Loss: 3.5656078845375845,	Average Valid Loss: 3.623452957832452,	Time taken: 311.1524329185486 seconds
Epoch: 25,	Loss: 3.4807748794555664
Epoch: 25,	Average Train Loss: 3.565567792595113,	Average Valid Loss: 3.6228528998114844,	Time taken: 310.44076776504517 seconds
Epoch: 26,	Loss: 3.4812822341918945
Epoch: 26,	Average Train Loss: 3.565412968737091,	Average Valid Loss: 3.622271534168359,	Time taken: 310.4774465560913 seconds
Epoch: 27,	Loss: 3.4829905033111572
Epoch: 27,	Average Train Loss: 3.5651702132968395,	Average Valid Loss: 3.621801087350556,	Time taken: 310.73558282852173 seconds
Epoch: 28,	Loss: 3.4825916290283203
Epoch: 28,	Average Train Loss: 3.5650173851745666,	Average Valid Loss: 3.621133531584884,	Time taken: 310.61714482307434 seconds
Epoch: 29,	Loss: 3.4815187454223633
Epoch: 29,	Average Train Loss: 3.5648914144972883,	Average Valid Loss: 3.620044368686098,	Time taken: 310.6605079174042 seconds
Epoch: 30,	Loss: 3.4797537326812744
Epoch: 30,	Average Train Loss: 3.564734306172273,	Average Valid Loss: 3.6189178192254268,	Time taken: 310.59446597099304 seconds
Predicted output: <pad> <pad>

Entiendo que los resultados actuales no son ideales para generar una predicción coherente. En este caso, puedes probar ajustando los hiperparámetros para mejorar el rendimiento del modelo. Adjustaremos algunos hiperparámetros, como el número de capas, la tasa de aprendizaje y el tamaño del lote. Ten en cuenta que estos cambios pueden afectar el tiempo de entrenamiento, pero podrían mejorar la calidad de las predicciones.

Aquí hay una configuración de hiperparámetros sugerida:

```python
vocab_size = len(vocab)
d_model = 512      # Reduced to 512
nhead = 8
num_layers = 4     # Reduced to 4
num_epochs = 50    # Increased to 50
learning_rate = 5e-4 # Reduced to 5e-4
optimizer_choice = 'Adam'
batch_size = 128    # Increased to 128
max_seq_len = 100   # Increased to 100
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
```

Asegúrate de entrenar el modelo nuevamente con esta nueva configuración. No dudes en ajustar estos hiperparámetros aún más si no se ajustan a tus necesidades o limitaciones de hardware.

El ajuste de hiperparámetros a menudo es una tarea experimental y puede requerir varias iteraciones para obtener resultados deseables. También puedes considerar el uso de herramientas como [Optuna](https://optuna.org/) para automatizar la búsqueda de hiperparámetros óptimos.

NameError                                 Traceback (most recent call last)
<ipython-input-2-c53e9b318f67> in <cell line: 47>()
     45 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     46 
---> 47 train_batches = create_batches(train_data, batch_size, max_seq_len=max_seq_len)
     48 valid_batches = create_batches(valid_data, batch_size, max_seq_len=max_seq_len)
     49 

NameError: name 'create_batches' is not defined

Disculpa por la confusión. La variable `train_data` no existe en el código actual. En su lugar, se debe utilizar `train_data_raw` y `valid_data_raw` (por la nueva línea) al llamar a la función `create_batches()`. Vamos a hacer los ajustes necesarios en el código:

```python
# Replace these two lines
train_batches = create_batches(train_data_raw, batch_size, max_seq_len=max_seq_len)
valid_batches = create_batches(valid_data_raw, batch_size, max_seq_len=max_seq_len)
```

Asegúrate de que cualquier referencia a `train_data` o `valid_data` sea actualizada para utilizar `train_data_raw` y `valid_data_raw` como se muestra arriba. Esta corrección resolverá el error "`NameError: name 'train_data' is not defined`" y permitirá que se ejecute el entrenamiento correctamente.

NameError: name 'create_batches' is not defined

Mis disculpas nuevamente. Cometí un error y copié solo la parte modificada del código, olvidando incluir las funciones. A continuación, se muestra el código completo actualizado, incluidas las definiciones de funciones, cambios de hiperparámetros y el uso correcto de las variables `train_data_raw` y `valid_data_raw`. Ejecuta todo el código de una vez:

```python
import torch
import torch.nn as nn
import time
from torchtext.datasets import WikiText2
from torchtext.data.functional import to_map_style_dataset
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
from torch.utils.data import DataLoader

# 1. Define the model
class TransformerModel(nn.Module):
    def __init__(self, vocab_size, d_model, nhead, num_layers, dropout=0.1):
        super(TransformerModel, self).__init__()
        self.embedding = nn.Embedding(vocab_size, d_model)
        self.transformer = nn.Transformer(d_model, nhead, num_layers, dropout=dropout)
        self.fc = nn.Linear(d_model, vocab_size)

    def forward(self, src, tgt):
        src = self.embedding(src)
        tgt = self.embedding(tgt)
        x = self.transformer(src, tgt)
        x = self.fc(x)
        return x

# 2. Preparar datos
train_data_raw, valid_data_raw, _ = WikiText2(split=('train', 'valid', 'test'))
train_data_raw = to_map_style_dataset(train_data_raw)
valid_data_raw = to_map_style_dataset(valid_data_raw)

tokenizer = get_tokenizer("spacy", "en_core_web_sm")

# Agregar tokens especiales (UNK y PAD) al vocabulario
specials = ['<unk>','<pad>']
vocab = build_vocab_from_iterator((tokenizer(y) for y in train_data_raw), specials=s

wattpad: Freedom planet all characters react to itachi uchiha

Sash Lilac: "Whoa, now that is some serious power. I've never seen anything like it."

Carol Tea: "I don't really understand what he's doing, but he seems pretty strong."

Milla Basset: "I feel like he's holding back, like there's something more he can do."

Torque: "Interesting... I like the way he thinks. We could use someone with skills like his on our team."

Spade: "I've seen some incredible things in my time, but this guy takes the cake. He's not playing around."

Neera Li: "I'm not quite sure what to make of him. His power is impressive, but... there's something unsettling about him."

Lord Brevon: "Foolish Uchiha. He thinks he can take on the great Lord Brevon? He will soon learn his place."

Avalanche: "That's some serious ninja skills right there. I wouldn't want to be on the receiving end of that."

Dail: "Hmm... intriguing. I wonder if he's ever considered using his powers to mine resources?"

Zao: "Hmph. He thinks he's hot stuff, but he's no match for the master of martial arts."

Overall, the characters in Freedom Planet have varying opinions on Itachi Uchiha, but they all agree that he's an incredibly powerful and skilled fighter.

wattpad: Freedom planet all characters react to sad stories itachi uchiha

Sash Lilac: "Oh no, that's just heartbreaking. I can't even imagine what it must have been like for him to go through all of that."

Carol Tea: "This... this is just too sad. It's terrible to think that anyone should have to endure so much pain and suffering."

Milla Basset: "It's so hard to hear about someone going through something so terrible. I just want to give him a big hug."

Torque: "Geez, that's tough. It really puts things into perspective, you know? I'm grateful for what little good luck I've had in my life."

Spade: "It's tragic, truly. But I suppose it's true what they say - what doesn't kill you makes you stronger."

Neera Li: "It's hard to believe that anyone could be so cruel. I hope he's found some peace and solace now."

Lord Brevon: "Pfft. Weakness. This Uchiha should have known better than to let his emotions cloud his judgment."

Avalanche: "It's just... it's so sad. I can't even begin to imagine what he went through."

Dail: "That's really rough. I... I don't know what to say."

Zao: "Hmph. He should have focused more on his training and less on his emotional baggage." 

Despite their differing responses, the characters in Freedom Planet are all able to acknowledge the immense hardship that Itachi Uchiha faced in his life. They show compassion and empathy towards him, even if their overall reactions vary.