Ensuring Gym Success: Dianabol Uses & Dosage Explained- Read Now!

**The Influence of Social Media > idx)
self.word2idx = '':0, '':1
if vocab_path:
with open(vocab_path) as f:
for line in f:
word, idx = line.strip().split()
self.word2idxword = int(idx)

def encode(self, text):
return self.word2idx.get(tok, self.word2idx'') for tok in text.split()

# ----------------------------------------------------
# 1. Embedding Layer
# ----------------------------------------------------
class Embedder(nn.Module):
def __init__(self, vocab_size, embed_dim, padding_idx=0):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=padding_idx)

def forward(self, x): # x: batch, seq_len
return self.embedding(x) # -> batch, seq_len, embed_dim

# ----------------------------------------------------
# 2. Sequence Encoder
# ----------------------------------------------------
class Encoder(nn.Module):
"""
Encodes a sequence of word embeddings into a fixed-size vector.
Uses a bidirecti> 1 else 0)
def forward(self, x, lengths):
"""
Parameters
----------
x : Tensor batch, seq_len, embed
padded sequence of embeddings.
lengths : L