Principle:Pytorch Serve Text Classification
| Field | Value |
|---|---|
| source | Pytorch_Serve |
| domains | NLP, Classification |
| last_updated | 2026-02-13 18:52 GMT |
Overview
Text Classification is the principle of categorizing text documents into predefined classes using embedding-based representations and neural network classifiers that learn discriminative features from labeled training data.
Description
This principle addresses what text classification accomplishes as a fundamental NLP task. A text classification system maps variable-length input text to one or more discrete category labels. The pipeline consists of several stages:
- Tokenization -- Breaking raw text into tokens (words, subwords, or characters). Scriptable tokenizers enable TorchScript-compatible preprocessing that can be deployed alongside the model without Python runtime dependencies.
- Embedding layer -- Converting discrete tokens into dense vector representations. Pre-trained embeddings (Word2Vec, GloVe, FastText) or learned embeddings capture semantic relationships between tokens.
- Aggregation -- Reducing variable-length token sequences to fixed-length document representations via averaging, pooling, or recurrent/attention mechanisms.
- Classification head -- A fully connected layer (or stack of layers) that maps the document representation to class logits, followed by softmax for probability estimation.
import torch
import torch.nn as nn
class TextSentimentModel(nn.Module):
def __init__(self, vocab_size, embed_dim, num_classes):
super().__init__()
self.embedding = nn.EmbeddingBag(vocab_size, embed_dim, sparse=True)
self.fc = nn.Linear(embed_dim, num_classes)
def forward(self, text, offsets):
embedded = self.embedding(text, offsets)
return self.fc(embedded)
The SentencePiece tokenizer model (SPM) is particularly relevant for text classification serving, as it provides:
- Language-agnostic subword tokenization that handles out-of-vocabulary words by decomposing them into known subword units.
- Deterministic, scriptable tokenization that can be compiled into a TorchScript module for deployment.
Usage
Apply this principle when:
- Documents, reviews, messages, or other text inputs must be assigned to discrete categories (e.g., sentiment, topic, intent, spam detection).
- A labeled training dataset is available for supervised learning.
- The classification task requires real-time inference as part of a serving pipeline.
- Preprocessing (tokenization) must be bundled with the model to ensure consistency between training and inference.
- Lightweight, low-latency models are preferred over large Transformer-based classifiers.
Theoretical Basis
Text classification with embedding-based models relies on the distributional hypothesis -- words that appear in similar contexts have similar meanings. The EmbeddingBag layer is an optimized operation that combines embedding lookup with aggregation:
- For an input sequence of token indices
[t_1, t_2, ..., t_n], the embedding layer retrieves vectors[e_1, e_2, ..., e_n]. - The bag operation computes the mean (or sum) of these vectors:
d = (1/n) * sum(e_i). - This produces a fixed-dimensional document vector regardless of input length.
The classification layer applies a linear transformation followed by softmax:
logits = Wd + bP(class_k | d) = exp(logits_k) / sum(exp(logits_j))
Training uses cross-entropy loss:
L = -sum(y_k * log(P(class_k | d)))
where y_k is the one-hot target.
SentencePiece tokenization uses the unigram language model algorithm:
- A large initial vocabulary is created from the training corpus.
- Iteratively, the token whose removal causes the least increase in corpus perplexity is pruned.
- This process continues until the desired vocabulary size is reached.
- At inference time, the Viterbi algorithm finds the most probable segmentation of input text.
This ensures a compact, fixed-size vocabulary with robust handling of rare and unseen words.