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diff --git a/lib/classifier/tfidf.rb b/lib/classifier/tfidf.rb
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+# rbs_inline: enabled
+
+# Author::    Lucas Carlson  (mailto:lucas@rufy.com)
+# Copyright:: Copyright (c) 2024 Lucas Carlson
+# License::   LGPL
+
+require 'json'
+
+module Classifier
+  # TF-IDF vectorizer: transforms text to weighted feature vectors.
+  # Downweights common words, upweights discriminative terms.
+  #
+  # Example:
+  #   tfidf = Classifier::TFIDF.new
+  #   tfidf.fit(["Dogs are great pets", "Cats are independent"])
+  #   tfidf.transform("Dogs are loyal")  # => {:dog=>0.7071..., :loyal=>0.7071...}
+  #
+  class TFIDF
+    include Streaming
+
+    # @rbs @min_df: Integer | Float
+    # @rbs @max_df: Integer | Float
+    # @rbs @ngram_range: Array[Integer]
+    # @rbs @sublinear_tf: bool
+    # @rbs @vocabulary: Hash[Symbol, Integer]
+    # @rbs @idf: Hash[Symbol, Float]
+    # @rbs @num_documents: Integer
+    # @rbs @fitted: bool
+    # @rbs @dirty: bool
+    # @rbs @storage: Storage::Base?
+    # @rbs @min_word_length: Integer
+
+    attr_reader :vocabulary, :idf, :num_documents
+    attr_accessor :storage
+
+    # Creates a new TF-IDF vectorizer.
+    # - min_df/max_df: filter terms by document frequency (Integer for count, Float for proportion)
+    # - ngram_range: [1,1] for unigrams, [1,2] for unigrams+bigrams
+    # - sublinear_tf: use 1 + log(tf) instead of raw term frequency
+    # - min_word_length: minimum word length filter in tokenization
+    #
+    # @rbs (?min_df: Integer | Float, ?max_df: Integer | Float,
+    #       ?ngram_range: Array[Integer], ?sublinear_tf: bool, ?min_word_length: Integer) -> void
+    def initialize(min_df: 1, max_df: 1.0, ngram_range: [1, 1], sublinear_tf: false,
+                   min_word_length: Classifier.config.min_word_length)
+      validate_df!(min_df, 'min_df')
+      validate_df!(max_df, 'max_df')
+      validate_ngram_range!(ngram_range)
+
+      @min_df = min_df
+      @max_df = max_df
+      @ngram_range = ngram_range
+      @sublinear_tf = sublinear_tf
+      @vocabulary = {}
+      @idf = {}
+      @num_documents = 0
+      @fitted = false
+      @dirty = false
+      @storage = nil
+      @min_word_length = min_word_length
+    end
+
+    # Learns vocabulary and IDF weights from the corpus.
+    # @rbs (Array[String]) -> self
+    def fit(documents)
+      raise ArgumentError, 'documents must be an array' unless documents.is_a?(Array)
+      raise ArgumentError, 'documents cannot be empty' if documents.empty?
+
+      @num_documents = documents.size
+      document_frequencies = Hash.new(0)
+
+      documents.each do |doc|
+        terms = extract_terms(doc)
+        terms.each_key { |term| document_frequencies[term] += 1 }
+      end
+
+      @vocabulary = {}
+      @idf = {}
+      vocab_index = 0
+
+      document_frequencies.each do |term, df|
+        next unless within_df_bounds?(df, @num_documents)
+
+        @vocabulary[term] = vocab_index
+        vocab_index += 1
+
+        # IDF: log((N + 1) / (df + 1)) + 1
+        @idf[term] = Math.log((@num_documents + 1).to_f / (df + 1)) + 1
+      end
+
+      @fitted = true
+      @dirty = true
+      self
+    end
+
+    # Transforms a document into a normalized TF-IDF vector.
+    # @rbs (String) -> Hash[Symbol, Float]
+    def transform(document)
+      raise NotFittedError, 'TFIDF has not been fitted. Call fit first.' unless @fitted
+
+      terms = extract_terms(document)
+      result = {} #: Hash[Symbol, Float]
+
+      terms.each do |term, tf|
+        next unless @vocabulary.key?(term)
+
+        tf_value = @sublinear_tf && tf.positive? ? 1 + Math.log(tf) : tf.to_f
+        result[term] = (tf_value * @idf[term]).to_f
+      end
+
+      normalize_vector(result)
+    end
+
+    # Fits and transforms in one step.
+    # @rbs (Array[String]) -> Array[Hash[Symbol, Float]]
+    def fit_transform(documents)
+      fit(documents)
+      documents.map { |doc| transform(doc) }
+    end
+
+    # Returns vocabulary terms in index order.
+    # @rbs () -> Array[Symbol]
+    def feature_names
+      @vocabulary.keys.sort_by { |term| @vocabulary[term] }
+    end
+
+    # @rbs () -> bool
+    def fitted?
+      @fitted
+    end
+
+    # Returns true if there are unsaved changes.
+    # @rbs () -> bool
+    def dirty?
+      @dirty
+    end
+
+    # Saves the vectorizer to the configured storage.
+    # @rbs () -> void
+    def save
+      raise ArgumentError, 'No storage configured' unless storage
+
+      storage.write(to_json)
+      @dirty = false
+    end
+
+    # Saves the vectorizer state to a file.
+    # @rbs (String) -> Integer
+    def save_to_file(path)
+      result = File.write(path, to_json)
+      @dirty = false
+      result
+    end
+
+    # Loads a vectorizer from the configured storage.
+    # @rbs (storage: Storage::Base) -> TFIDF
+    def self.load(storage:)
+      data = storage.read
+      raise StorageError, 'No saved state found' unless data
+
+      instance = from_json(data)
+      instance.storage = storage
+      instance
+    end
+
+    # Loads a vectorizer from a file.
+    # @rbs (String) -> TFIDF
+    def self.load_from_file(path)
+      from_json(File.read(path))
+    end
+
+    # Reloads the vectorizer from storage, raising if there are unsaved changes.
+    # @rbs () -> self
+    def reload
+      raise ArgumentError, 'No storage configured' unless storage
+      raise UnsavedChangesError, 'Unsaved changes would be lost. Call save first or use reload!' if @dirty
+
+      data = storage.read
+      raise StorageError, 'No saved state found' unless data
+
+      restore_from_json(data)
+      @dirty = false
+      self
+    end
+
+    # Force reloads the vectorizer from storage, discarding any unsaved changes.
+    # @rbs () -> self
+    def reload!
+      raise ArgumentError, 'No storage configured' unless storage
+
+      data = storage.read
+      raise StorageError, 'No saved state found' unless data
+
+      restore_from_json(data)
+      @dirty = false
+      self
+    end
+
+    # @rbs (?untyped) -> Hash[Symbol, untyped]
+    def as_json(_options = nil)
+      {
+        version: 1,
+        type: 'tfidf',
+        min_df: @min_df,
+        max_df: @max_df,
+        ngram_range: @ngram_range,
+        sublinear_tf: @sublinear_tf,
+        vocabulary: @vocabulary,
+        idf: @idf,
+        num_documents: @num_documents,
+        fitted: @fitted,
+        min_word_length: @min_word_length
+      }
+    end
+
+    # @rbs (?untyped) -> String
+    def to_json(_options = nil)
+      JSON.generate(as_json)
+    end
+
+    # Loads a vectorizer from JSON.
+    # @rbs (String | Hash[String, untyped]) -> TFIDF
+    def self.from_json(json)
+      data = json.is_a?(String) ? JSON.parse(json) : json
+      raise ArgumentError, "Invalid vectorizer type: #{data['type']}" unless data['type'] == 'tfidf'
+
+      instance = new(
+        min_df: data['min_df'],
+        max_df: data['max_df'],
+        ngram_range: data['ngram_range'],
+        sublinear_tf: data['sublinear_tf'],
+        min_word_length: data['min_word_length'] || Classifier.config.min_word_length
+      )
+
+      instance.instance_variable_set(:@vocabulary, symbolize_keys(data['vocabulary']))
+      instance.instance_variable_set(:@idf, symbolize_keys(data['idf']))
+      instance.instance_variable_set(:@num_documents, data['num_documents'])
+      instance.instance_variable_set(:@fitted, data['fitted'])
+      instance.instance_variable_set(:@dirty, false)
+      instance.instance_variable_set(:@storage, nil)
+
+      instance
+    end
+
+    # @rbs () -> Array[untyped]
+    def marshal_dump
+      [@min_df, @max_df, @ngram_range, @sublinear_tf, @vocabulary, @idf, @num_documents, @fitted,
+       @min_word_length]
+    end
+
+    # @rbs (Array[untyped]) -> void
+    def marshal_load(data)
+      @min_df, @max_df, @ngram_range, @sublinear_tf, @vocabulary, @idf, @num_documents, @fitted,
+        @min_word_length = data
+      @dirty = false
+      @storage = nil
+    end
+
+    # Loads a vectorizer from a checkpoint.
+    #
+    # @rbs (storage: Storage::Base, checkpoint_id: String) -> TFIDF
+    def self.load_checkpoint(storage:, checkpoint_id:)
+      raise ArgumentError, 'Storage must be File storage for checkpoints' unless storage.is_a?(Storage::File)
+
+      dir = File.dirname(storage.path)
+      base = File.basename(storage.path, '.*')
+      ext = File.extname(storage.path)
+      checkpoint_path = File.join(dir, "#{base}_checkpoint_#{checkpoint_id}#{ext}")
+
+      checkpoint_storage = Storage::File.new(path: checkpoint_path)
+      instance = load(storage: checkpoint_storage)
+      instance.storage = storage
+      instance
+    end
+
+    # Fits the vectorizer from an IO stream.
+    # Collects all documents from the stream, then fits the model.
+    # Note: All documents must be collected in memory for IDF calculation.
+    #
+    # @example Fit from a file
+    #   tfidf.fit_from_stream(File.open('corpus.txt'))
+    #
+    # @example With progress tracking
+    #   tfidf.fit_from_stream(io, batch_size: 500) do |progress|
+    #     puts "#{progress.completed} documents loaded"
+    #   end
+    #
+    # @rbs (IO, ?batch_size: Integer) { (Streaming::Progress) -> void } -> self
+    def fit_from_stream(io, batch_size: Streaming::DEFAULT_BATCH_SIZE)
+      reader = Streaming::LineReader.new(io, batch_size: batch_size)
+      total = reader.estimate_line_count
+      progress = Streaming::Progress.new(total: total)
+
+      documents = [] #: Array[String]
+
+      reader.each_batch do |batch|
+        documents.concat(batch)
+        progress.completed += batch.size
+        progress.current_batch += 1
+        yield progress if block_given?
+      end
+
+      fit(documents) unless documents.empty?
+      self
+    end
+
+    # TFIDF doesn't support train_from_stream (use fit_from_stream instead).
+    # This method raises NotImplementedError with guidance.
+    #
+    # @rbs (*untyped, **untyped) -> void
+    def train_from_stream(*) # steep:ignore
+      raise NotImplementedError, 'TFIDF uses fit_from_stream instead of train_from_stream'
+    end
+
+    # TFIDF doesn't support train_batch (use fit instead).
+    # This method raises NotImplementedError with guidance.
+    #
+    # @rbs (*untyped, **untyped) -> void
+    def train_batch(*) # steep:ignore
+      raise NotImplementedError, 'TFIDF uses fit instead of train_batch'
+    end
+
+    private
+
+    # Restores vectorizer state from JSON string.
+    # @rbs (String) -> void
+    def restore_from_json(json)
+      data = JSON.parse(json)
+
+      @min_df = data['min_df']
+      @max_df = data['max_df']
+      @ngram_range = data['ngram_range']
+      @sublinear_tf = data['sublinear_tf']
+      @vocabulary = self.class.send(:symbolize_keys, data['vocabulary'])
+      @idf = self.class.send(:symbolize_keys, data['idf'])
+      @num_documents = data['num_documents']
+      @fitted = data['fitted']
+    end
+
+    # @rbs (String) -> Hash[Symbol, Integer]
+    def extract_terms(document)
+      result = Hash.new(0)
+
+      if @ngram_range[0] <= 1
+        word_hash = document.clean_word_hash(@min_word_length)
+        word_hash.each { |term, count| result[term] += count }
+      end
+
+      return result if @ngram_range[1] <= 1
+
+      tokens = tokenize_for_ngrams(document)
+      (2..@ngram_range[1]).each do |n|
+        next if n < @ngram_range[0]
+
+        generate_ngrams(tokens, n).each { |ngram| result[ngram] += 1 }
+      end
+
+      result
+    end
+
+    # @rbs (String) -> Array[String]
+    def tokenize_for_ngrams(document)
+      document
+        .gsub(/[^\w\s]/, '')
+        .split
+        .map(&:downcase)
+        .reject { |w| w.length <= 2 || String::CORPUS_SKIP_WORDS.include?(w) }
+        .map(&:stem)
+    end
+
+    # @rbs (Array[String], Integer) -> Array[Symbol]
+    def generate_ngrams(tokens, n) # rubocop:disable Naming/MethodParameterName
+      return [] if tokens.size < n
+
+      tokens.each_cons(n).map { |gram| gram.join('_').intern }
+    end
+
+    # @rbs (Integer, Integer) -> bool
+    def within_df_bounds?(doc_freq, num_docs)
+      doc_freq.between?(
+        @min_df.is_a?(Float) ? (@min_df * num_docs).ceil : @min_df,
+        @max_df.is_a?(Float) ? (@max_df * num_docs).floor : @max_df
+      )
+    end
+
+    # @rbs (Hash[Symbol, Float]) -> Hash[Symbol, Float]
+    def normalize_vector(vector)
+      return vector if vector.empty?
+
+      magnitude = Math.sqrt(vector.values.sum { |v| v * v })
+      return vector if magnitude.zero?
+
+      vector.transform_values { |v| v / magnitude }
+    end
+
+    # @rbs (Integer | Float, String) -> void
+    def validate_df!(value, name)
+      raise ArgumentError, "#{name} must be an Integer or Float" unless value.is_a?(Float) || value.is_a?(Integer)
+      raise ArgumentError, "#{name} must be between 0.0 and 1.0" if value.is_a?(Float) && !value.between?(0.0, 1.0)
+      raise ArgumentError, "#{name} must be non-negative" if value.is_a?(Integer) && value.negative?
+    end
+
+    # @rbs (Array[Integer]) -> void
+    def validate_ngram_range!(range)
+      raise ArgumentError, 'ngram_range must be an array of two integers' unless range.is_a?(Array) && range.size == 2
+      raise ArgumentError, 'ngram_range values must be positive integers' unless range.all?(Integer) && range.all?(&:positive?)
+      raise ArgumentError, 'ngram_range[0] must be <= ngram_range[1]' if range[0] > range[1]
+    end
+
+    # @rbs (Hash[String, untyped]) -> Hash[Symbol, untyped]
+    def self.symbolize_keys(hash)
+      hash.transform_keys(&:to_sym)
+    end
+    private_class_method :symbolize_keys
+  end
+end