| import torch
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| import torch.nn as nn
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|
|
|
|
| MODEL_SAVE_PATH = "char_lm_model.pth"
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| SEQ_LENGTH = 32
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| EMBEDDING_DIM = 64
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| HIDDEN_DIM = 64
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|
|
|
|
| with open('dataset.txt', 'r', encoding='utf-8') as f:
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| text = f.read()
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|
|
| chars = sorted(list(set(text)))
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| vocab_size = len(chars)
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| char_to_idx = {ch: i for i, ch in enumerate(chars)}
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| idx_to_char = {i: ch for i, ch in enumerate(chars)}
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|
|
|
|
| class CharLM(nn.Module):
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| def __init__(self):
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| super(CharLM, self).__init__()
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| self.embedding = nn.Embedding(vocab_size, EMBEDDING_DIM)
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| self.rnn = nn.GRU(EMBEDDING_DIM, HIDDEN_DIM, batch_first=True)
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| self.fc = nn.Linear(HIDDEN_DIM, vocab_size)
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|
|
| def forward(self, x, hidden=None):
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| x = self.embedding(x)
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| out, hidden = self.rnn(x, hidden)
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| out = self.fc(out)
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| return out, hidden
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|
|
|
|
| model = CharLM()
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| model.load_state_dict(torch.load(MODEL_SAVE_PATH))
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| model.eval()
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|
|
| def generate_text(model, start_str, length=100, temperature=0.7, top_k=0):
|
| """
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| Generate text with temperature scaling and top-k sampling
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| """
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| model.eval()
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| chars = [ch for ch in start_str]
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| input_seq = torch.tensor([char_to_idx[ch] for ch in chars]).unsqueeze(0)
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| hidden = None
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|
|
| with torch.no_grad():
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| for _ in range(length):
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| outputs, hidden = model(input_seq, hidden)
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| logits = outputs[0, -1] / temperature
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|
|
| if top_k > 0:
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| top_vals, top_idx = torch.topk(logits, top_k)
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| logits[logits < top_vals[-1]] = -float('Inf')
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|
|
| probs = torch.softmax(logits, dim=-1)
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| next_char = torch.multinomial(probs, num_samples=1).item()
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| chars.append(idx_to_char[next_char])
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| input_seq = torch.tensor([[next_char]])
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|
|
| return ''.join(chars)
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|
|
|
|
| def chat():
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| print("Chat with the model! Type 'exit' to stop.")
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| while True:
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| user_input = input("You: ")
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| if user_input.lower() == 'exit':
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| break
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| response = generate_text(model, user_input, length=100, temperature=0.7, top_k=5)
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| print("Bot:", response)
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|
|
| chat()
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|
|
