package saga
Text processing and NLP extensions for Nx
Install
dune-project
Dependency
Authors
Maintainers
Sources
raven-1.0.0.alpha1.tbz
sha256=8e277ed56615d388bc69c4333e43d1acd112b5f2d5d352e2453aef223ff59867
sha512=369eda6df6b84b08f92c8957954d107058fb8d3d8374082e074b56f3a139351b3ae6e3a99f2d4a4a2930dd950fd609593467e502368a13ad6217b571382da28c
doc/src/saga.tokenizers/trainers.ml.html
Source file trainers.ml
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(** Training module for tokenization models. This module provides a unified interface for training different tokenization models. It wraps the actual training implementations in Bpe.Trainer and Wordpiece.Trainer. *) type training_result = { model : Models.t; special_tokens : string list } (** Training result *) type bpe_config = { vocab_size : int; min_frequency : int; show_progress : bool; special_tokens : string list; limit_alphabet : int; initial_alphabet : char list; continuing_subword_prefix : string option; end_of_word_suffix : string option; } (** Configuration types for each trainer *) type wordpiece_config = { vocab_size : int; min_frequency : int; show_progress : bool; special_tokens : string list; limit_alphabet : int; initial_alphabet : char list; continuing_subword_prefix : string; } type wordlevel_config = { vocab_size : int; min_frequency : int; show_progress : bool; special_tokens : string list; } type unigram_config = { vocab_size : int; show_progress : bool; special_tokens : string list; shrinking_factor : float; unk_token : string option; max_piece_length : int; n_sub_iterations : int; } (** Main trainer type *) type t = | BPE of bpe_config | WordPiece of wordpiece_config | WordLevel of wordlevel_config | Unigram of unigram_config (** Read lines from files *) let read_files files = let lines = ref [] in List.iter (fun file -> let ic = open_in file in try while true do lines := input_line ic :: !lines done with End_of_file -> close_in ic) files; List.rev !lines (** Read lines from iterator *) let read_iterator iterator = let lines = ref [] in let rec loop () = match iterator () with | None -> () | Some line -> lines := line :: !lines; loop () in loop (); List.rev !lines (** Train BPE model *) let train_bpe (config : bpe_config) lines existing_model = let trainer_config = Bpe.Trainer. { min_frequency = config.min_frequency; vocab_size = config.vocab_size; show_progress = config.show_progress; special_tokens = config.special_tokens; limit_alphabet = Some config.limit_alphabet; initial_alphabet = config.initial_alphabet; continuing_subword_prefix = config.continuing_subword_prefix; end_of_word_suffix = config.end_of_word_suffix; max_token_length = None; } in let trainer = Bpe.Trainer.create trainer_config in Bpe.Trainer.feed trainer lines; (* Create or use existing BPE model *) let base_model = match existing_model with | Some (Models.BPE bpe) -> Bpe.create { vocab = bpe.vocab; merges = bpe.merges; cache_capacity = bpe.cache_capacity; dropout = bpe.dropout; unk_token = bpe.unk_token; continuing_subword_prefix = bpe.continuing_subword_prefix; end_of_word_suffix = bpe.end_of_word_suffix; fuse_unk = bpe.fuse_unk; byte_fallback = bpe.byte_fallback; ignore_merges = false; } | _ -> (* Create empty model *) Bpe.create { vocab = Hashtbl.create 100; merges = []; cache_capacity = 10000; dropout = None; unk_token = None; continuing_subword_prefix = config.continuing_subword_prefix; end_of_word_suffix = config.end_of_word_suffix; fuse_unk = false; byte_fallback = false; ignore_merges = false; } in let special_tokens = Bpe.Trainer.train trainer base_model in (* Convert back to Models.t *) let vocab_list = Bpe.get_vocab base_model in let vocab_tbl = Hashtbl.create (List.length vocab_list) in List.iter (fun (token, id) -> Hashtbl.add vocab_tbl token id) vocab_list; let trained_model = Models.BPE { vocab = vocab_tbl; merges = []; (* TODO: Get merges from trained model *) cache_capacity = 10000; dropout = None; unk_token = None; continuing_subword_prefix = config.continuing_subword_prefix; end_of_word_suffix = config.end_of_word_suffix; fuse_unk = false; byte_fallback = false; } in { model = trained_model; special_tokens } (** Train WordPiece model *) let train_wordpiece (config : wordpiece_config) lines existing_model = let trainer_config = Wordpiece.Trainer. { min_frequency = config.min_frequency; vocab_size = config.vocab_size; show_progress = config.show_progress; special_tokens = config.special_tokens; limit_alphabet = Some config.limit_alphabet; initial_alphabet = config.initial_alphabet; continuing_subword_prefix = config.continuing_subword_prefix; end_of_word_suffix = None; (* WordPiece doesn't use end_of_word_suffix typically *) } in let trainer = Wordpiece.Trainer.create trainer_config in Wordpiece.Trainer.feed trainer lines; (* Create or use existing WordPiece model *) let base_model = match existing_model with | Some (Models.WordPiece wp) -> Wordpiece.create { vocab = wp.vocab; unk_token = wp.unk_token; max_input_chars_per_word = wp.max_input_chars_per_word; continuing_subword_prefix = config.continuing_subword_prefix; } | _ -> (* Create empty model *) Wordpiece.create { vocab = Hashtbl.create 100; unk_token = "[UNK]"; max_input_chars_per_word = 100; continuing_subword_prefix = config.continuing_subword_prefix; } in let special_tokens = Wordpiece.Trainer.train trainer base_model in (* Convert back to Models.t *) let vocab_list = Wordpiece.get_vocab base_model in let vocab_tbl = Hashtbl.create (List.length vocab_list) in List.iter (fun (token, id) -> Hashtbl.add vocab_tbl token id) vocab_list; let trained_model = Models.WordPiece { vocab = vocab_tbl; unk_token = "[UNK]"; max_input_chars_per_word = 100 } in { model = trained_model; special_tokens } (** Train WordLevel model *) let train_wordlevel (config : wordlevel_config) lines _existing_model = let vocab_size = config.vocab_size in let min_frequency = config.min_frequency in let special_tokens = config.special_tokens in (* Simple word-level tokenization: just count word frequencies *) let word_counts = Hashtbl.create 10000 in List.iter (fun line -> let words = Str.split (Str.regexp "[ \t\n]+") line in List.iter (fun word -> let count = try Hashtbl.find word_counts word with Not_found -> 0 in Hashtbl.replace word_counts word (count + 1)) words) lines; (* Sort by frequency and take top vocab_size *) let sorted = Hashtbl.fold (fun word count acc -> (word, count) :: acc) word_counts [] |> List.sort (fun (_, c1) (_, c2) -> compare c2 c1) in (* Build vocabulary *) let vocab = Hashtbl.create vocab_size in let rec build_vocab words id = match words with | [] -> () | _ when id >= vocab_size -> () | (word, count) :: rest -> if count >= min_frequency then ( Hashtbl.add vocab word id; build_vocab rest (id + 1)) else build_vocab rest id in (* Add special tokens first *) List.iteri (fun i token -> Hashtbl.add vocab token i) special_tokens; build_vocab sorted (List.length special_tokens); let model = Models.WordLevel { vocab; unk_token = "<unk>" } in { model; special_tokens } (** Train Unigram model *) let train_unigram (_config : unigram_config) _lines _existing_model = (* Minimal unigram-style trainer: build token frequency over whitespace tokens and assign probabilities proportional to frequency. *) let lines = _lines in let freq = Hashtbl.create 10000 in List.iter (fun line -> let words = Str.split (Str.regexp "[ \t\n]+") line in List.iter (fun w -> let c = match Hashtbl.find_opt freq w with Some x -> x | None -> 0 in Hashtbl.replace freq w (c + 1)) words) lines; let total = float_of_int (Hashtbl.fold (fun _ c acc -> acc + c) freq 0) in let vocab = Hashtbl.fold (fun w c acc -> (w, float_of_int c /. max 1.0 total) :: acc) freq [] in let model = Models.Unigram { vocab } in { model; special_tokens = [] } (** Main training function *) let train trainer ~files ?model () = let lines = read_files files in match trainer with | BPE config -> train_bpe config lines model | WordPiece config -> train_wordpiece config lines model | WordLevel config -> train_wordlevel config lines model | Unigram config -> train_unigram config lines model (** Train from iterator *) let train_from_iterator trainer ~iterator ?model () = let lines = read_iterator iterator in match trainer with | BPE config -> train_bpe config lines model | WordPiece config -> train_wordpiece config lines model | WordLevel config -> train_wordlevel config lines model | Unigram config -> train_unigram config lines model (** Constructors *) let bpe ?(vocab_size = 30000) ?(min_frequency = 0) ?(special_tokens = []) ?(limit_alphabet = 1000) ?(initial_alphabet = []) ?(continuing_subword_prefix = "") ?(end_of_word_suffix = "") ?(show_progress = true) ?max_token_length () = let _ = max_token_length in BPE { vocab_size; min_frequency; show_progress; special_tokens; limit_alphabet; initial_alphabet = List.map (fun s -> if String.length s > 0 then s.[0] else ' ') initial_alphabet; continuing_subword_prefix = (if continuing_subword_prefix = "" then None else Some continuing_subword_prefix); end_of_word_suffix = (if end_of_word_suffix = "" then None else Some end_of_word_suffix); } let wordpiece ?(vocab_size = 30000) ?(min_frequency = 0) ?(special_tokens = []) ?(limit_alphabet = 1000) ?(initial_alphabet = []) ?(continuing_subword_prefix = "##") ?(end_of_word_suffix = "") ?(unk_token = "[UNK]") ?(show_progress = true) () = let _ = (end_of_word_suffix, unk_token) in WordPiece { vocab_size; min_frequency; show_progress; special_tokens; limit_alphabet; initial_alphabet = List.map (fun s -> if String.length s > 0 then s.[0] else ' ') initial_alphabet; continuing_subword_prefix; } let word_level ?(vocab_size = 30000) ?(min_frequency = 0) ?(special_tokens = []) ?(show_progress = true) () = WordLevel { vocab_size; min_frequency; show_progress; special_tokens } let unigram ?(vocab_size = 8000) ?(n_sub_iterations = 2) ?(shrinking_factor = 0.75) ?(unk_token = "<unk>") ?(special_tokens = []) ?(show_progress = true) ?(initial_alphabet = []) ?(max_piece_length = 16) () = let _ = initial_alphabet in Unigram { vocab_size; show_progress; special_tokens; shrinking_factor; unk_token = (if unk_token = "" then None else Some unk_token); max_piece_length; n_sub_iterations; } let chars ?(min_frequency = 0) ?(special_tokens = []) ?(show_progress = true) () = (* Character-level tokenization is similar to word-level but with single characters *) WordLevel { vocab_size = 256; (* ASCII characters typically *) min_frequency; show_progress; special_tokens; } (** Serialization *) let to_json = function | BPE bpe -> `Assoc [ ("type", `String "BpeTrainer"); ("vocab_size", `Int bpe.vocab_size); ("min_frequency", `Int bpe.min_frequency); ("show_progress", `Bool bpe.show_progress); ( "special_tokens", `List (List.map (fun s -> `String s) bpe.special_tokens) ); ("limit_alphabet", `Int bpe.limit_alphabet); ( "initial_alphabet", `List (List.map (fun c -> `String (String.make 1 c)) bpe.initial_alphabet) ); ( "continuing_subword_prefix", match bpe.continuing_subword_prefix with | None -> `Null | Some s -> `String s ); ( "end_of_word_suffix", match bpe.end_of_word_suffix with | None -> `Null | Some s -> `String s ); ] | WordPiece wp -> `Assoc [ ("type", `String "WordPieceTrainer"); ("vocab_size", `Int wp.vocab_size); ("min_frequency", `Int wp.min_frequency); ("show_progress", `Bool wp.show_progress); ( "special_tokens", `List (List.map (fun s -> `String s) wp.special_tokens) ); ("limit_alphabet", `Int wp.limit_alphabet); ( "initial_alphabet", `List (List.map (fun c -> `String (String.make 1 c)) wp.initial_alphabet) ); ("continuing_subword_prefix", `String wp.continuing_subword_prefix); ] | WordLevel wl -> `Assoc [ ("type", `String "WordLevelTrainer"); ("vocab_size", `Int wl.vocab_size); ("min_frequency", `Int wl.min_frequency); ("show_progress", `Bool wl.show_progress); ( "special_tokens", `List (List.map (fun s -> `String s) wl.special_tokens) ); ] | Unigram ug -> `Assoc [ ("type", `String "UnigramTrainer"); ("vocab_size", `Int ug.vocab_size); ("show_progress", `Bool ug.show_progress); ( "special_tokens", `List (List.map (fun s -> `String s) ug.special_tokens) ); ("shrinking_factor", `Float ug.shrinking_factor); ( "unk_token", match ug.unk_token with None -> `Null | Some s -> `String s ); ("max_piece_length", `Int ug.max_piece_length); ("n_sub_iterations", `Int ug.n_sub_iterations); ] let of_json = function | `Assoc fields -> ( match List.assoc_opt "type" fields with | Some (`String "BpeTrainer") -> let vocab_size = match List.assoc_opt "vocab_size" fields with | Some (`Int i) -> i | _ -> 30000 in let min_frequency = match List.assoc_opt "min_frequency" fields with | Some (`Int i) -> i | _ -> 0 in let show_progress = match List.assoc_opt "show_progress" fields with | Some (`Bool b) -> b | _ -> true in let special_tokens = match List.assoc_opt "special_tokens" fields with | Some (`List tokens) -> List.map (function `String s -> s | _ -> "") tokens | _ -> [] in let limit_alphabet = match List.assoc_opt "limit_alphabet" fields with | Some (`Int i) -> i | _ -> 1000 in let initial_alphabet = match List.assoc_opt "initial_alphabet" fields with | Some (`List chars) -> List.map (function `String s -> s | _ -> "") chars | _ -> [] in let continuing_subword_prefix = match List.assoc_opt "continuing_subword_prefix" fields with | Some (`String s) -> s | _ -> "" in let end_of_word_suffix = match List.assoc_opt "end_of_word_suffix" fields with | Some (`String s) -> s | _ -> "" in bpe ~vocab_size ~min_frequency ~show_progress ~special_tokens ~limit_alphabet ~initial_alphabet ~continuing_subword_prefix ~end_of_word_suffix () | Some (`String "WordPieceTrainer") -> let vocab_size = match List.assoc_opt "vocab_size" fields with | Some (`Int i) -> i | _ -> 30000 in let min_frequency = match List.assoc_opt "min_frequency" fields with | Some (`Int i) -> i | _ -> 0 in let show_progress = match List.assoc_opt "show_progress" fields with | Some (`Bool b) -> b | _ -> true in let special_tokens = match List.assoc_opt "special_tokens" fields with | Some (`List tokens) -> List.map (function `String s -> s | _ -> "") tokens | _ -> [] in let limit_alphabet = match List.assoc_opt "limit_alphabet" fields with | Some (`Int i) -> i | _ -> 1000 in let initial_alphabet = match List.assoc_opt "initial_alphabet" fields with | Some (`List chars) -> List.map (function `String s -> s | _ -> "") chars | _ -> [] in let continuing_subword_prefix = match List.assoc_opt "continuing_subword_prefix" fields with | Some (`String s) -> s | _ -> "##" in wordpiece ~vocab_size ~min_frequency ~show_progress ~special_tokens ~limit_alphabet ~initial_alphabet ~continuing_subword_prefix () | Some (`String "WordLevelTrainer") -> let vocab_size = match List.assoc_opt "vocab_size" fields with | Some (`Int i) -> i | _ -> 30000 in let min_frequency = match List.assoc_opt "min_frequency" fields with | Some (`Int i) -> i | _ -> 0 in let show_progress = match List.assoc_opt "show_progress" fields with | Some (`Bool b) -> b | _ -> true in let special_tokens = match List.assoc_opt "special_tokens" fields with | Some (`List tokens) -> List.map (function `String s -> s | _ -> "") tokens | _ -> [] in word_level ~vocab_size ~min_frequency ~show_progress ~special_tokens () | Some (`String "UnigramTrainer") -> let vocab_size = match List.assoc_opt "vocab_size" fields with | Some (`Int i) -> i | _ -> 8000 in let show_progress = match List.assoc_opt "show_progress" fields with | Some (`Bool b) -> b | _ -> true in let special_tokens = match List.assoc_opt "special_tokens" fields with | Some (`List tokens) -> List.map (function `String s -> s | _ -> "") tokens | _ -> [] in let shrinking_factor = match List.assoc_opt "shrinking_factor" fields with | Some (`Float f) -> f | _ -> 0.75 in let unk_token = match List.assoc_opt "unk_token" fields with | Some (`String s) -> s | _ -> "<unk>" in let max_piece_length = match List.assoc_opt "max_piece_length" fields with | Some (`Int i) -> i | _ -> 16 in let n_sub_iterations = match List.assoc_opt "n_sub_iterations" fields with | Some (`Int i) -> i | _ -> 2 in unigram ~vocab_size ~show_progress ~special_tokens ~shrinking_factor ~unk_token ~max_piece_length ~n_sub_iterations () | _ -> failwith "Unknown trainer type") | _ -> failwith "Invalid trainer JSON"
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