generated from mwc/lab_tinylm
Initial commit
This commit is contained in:
10
.gitignore
vendored
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10
.gitignore
vendored
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@@ -0,0 +1,10 @@
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||||
# Python-generated files
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||||
__pycache__/
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||||
*.py[oc]
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build/
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dist/
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wheels/
|
||||
*.egg-info
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|
||||
# Virtual environments
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.venv
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1
.python-version
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1
.python-version
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@@ -0,0 +1 @@
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3.13
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2
chuck.txt
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2
chuck.txt
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@@ -0,0 +1,2 @@
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how much wood would a wood chuck chuck if a wood chuck could chuck wood
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a wood chuck would chuck all the wood it could chuck if a wood chuck could chuck wood
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21
pyproject.toml
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21
pyproject.toml
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@@ -0,0 +1,21 @@
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[project]
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name = "lab-language-model"
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version = "5.0.0"
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description = ""
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requires-python = ">=3.10,<4.0"
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dependencies = [
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"click>=8.3.1",
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"nltk>=3.9.2",
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"tabulate>=0.9.0",
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"tqdm>=4.67.3",
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]
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[project.scripts]
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tlm = "tlm.cli:cli"
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[tool.hatch.build.targets.wheel]
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packages = ["tlm"]
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0
tlm/__init__.py
Normal file
0
tlm/__init__.py
Normal file
88
tlm/cli.py
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88
tlm/cli.py
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@@ -0,0 +1,88 @@
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import click
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from .model import TinyLanguageModel
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from .helpers import read_mail_text, join
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from .tokenization import tokenize_text, tokenize_words
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@click.group()
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def cli():
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"""TinyLM - A simple n-gram language model."""
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pass
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@cli.command()
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@click.option('-l', "--length", default=50, help="Number of tokens to generate.")
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@click.option('-n', "--context-window-words", default=2, help="Number of words in the context window.")
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@click.option('-f', "--filepath", type=click.Path(exists=True), multiple=True, help="Text file(s) to use as training corpus. Can be specified multiple times.")
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@click.option('-g', "--gutenberg", multiple=True, help="NLTK Gutenberg corpus key(s). Can be specified multiple times.")
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@click.option('-G', "--list-gutenberg", is_flag=True, help="List available Gutenberg corpus keys.")
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@click.option('-m', "--mbox", type=click.Path(exists=True), help="Mbox file to use for training.")
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@click.option('-p', "--prompt", help="Prompt to start generation.")
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@click.option('-i', "--interact", is_flag=True, help="Drop into interactive shell after generating.")
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@click.option('-t', "--tokenize", 'tokenize_opts', multiple=True, type=click.Choice(['lower', 'char', 'alpha']), help="Preprocessing option (can be specified multiple times). 'lower': lowercase all input text. 'char': use characters as tokens instead of words.")
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@click.option('-v', "--verbose", is_flag=True, help="Display step-by-step generation as a table.")
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def generate(length, context_window_words, filepath, gutenberg, list_gutenberg, mbox, prompt, interact, tokenize_opts, verbose):
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"""Generate text using the language model."""
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import nltk
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# Handle --list-gutenberg: list available keys
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if list_gutenberg:
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nltk.download("gutenberg", quiet=True)
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from nltk.corpus import gutenberg as gutenberg_corpus
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click.echo("Available Gutenberg corpus keys:")
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for key in gutenberg_corpus.fileids():
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click.echo(f" {key}")
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return
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# Determine training corpus
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corpus = []
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if filepath:
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for fp in filepath:
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with open(fp, "r") as f:
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corpus.extend(tokenize_text(f.read(), tokenize_opts))
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if gutenberg:
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nltk.download("gutenberg", quiet=True)
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from nltk.corpus import gutenberg as gutenberg_corpus
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for key in gutenberg:
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corpus.extend(tokenize_words(gutenberg_corpus.words(key), tokenize_opts))
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if mbox:
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mail_text = read_mail_text(mbox)
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corpus.extend(tokenize_text(mail_text, tokenize_opts))
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if not corpus:
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raise click.UsageError("Must specify at least one of --text, --gutenberg, or --mbox for training data.")
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# Train and generate
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model = TinyLanguageModel(n=context_window_words)
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model.train(corpus)
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if prompt:
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prompt_tokens = tokenize_text(prompt, tokenize_opts)
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else:
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prompt_tokens = None
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join_fn = ''.join if 'char' in tokenize_opts else None
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display_join = join_fn or join
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if verbose:
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from tabulate import tabulate
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rows = []
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import textwrap
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def step_callback(pattern, options, chosen):
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opts = textwrap.fill(', '.join(sorted(set(options))), width=60)
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rows.append([display_join(list(pattern)), opts, chosen])
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output = model.generate(length, prompt=prompt_tokens, join_fn=join_fn, step_callback=step_callback)
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click.echo(tabulate(rows, headers=["Context", "Options", "Selected"], tablefmt="simple"))
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click.echo()
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else:
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output = model.generate(length, prompt=prompt_tokens, join_fn=join_fn)
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click.echo(output)
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if interact:
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import code
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code.interact(local=locals(), banner="Entering interactive shell. 'model' and 'output' are available.")
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if __name__ == "__main__":
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cli()
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63
tlm/helpers.py
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63
tlm/helpers.py
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@@ -0,0 +1,63 @@
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import mailbox
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import email
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from email.policy import default
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from tqdm import tqdm
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def rolling_window(iterable, n):
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"""Passes a rolling window over the iterable, yielding each n-length tuple.
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rolling_window(range(5), 3) -> (0, 1, 2), (1, 2, 3), (2, 3, 4)
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"""
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it = iter(iterable)
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try:
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window = [next(it) for _ in range(n)]
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while True:
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yield tuple(window)
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window = window[1:] + [next(it)]
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except StopIteration:
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return
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def read_mail_text(mbox_path):
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"""
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Extract and concatenate all plaintext content from an mbox file.
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"""
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texts = []
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mbox = mailbox.mbox(
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mbox_path,
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factory=lambda f: email.message_from_binary_file(f, policy=default)
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)
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for msg in tqdm(mbox):
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||||
if msg.is_multipart():
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for part in msg.walk():
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if part.get_content_type() == "text/plain":
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try:
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text = part.get_content()
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if text:
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texts.append(text.strip())
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except Exception:
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pass
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else:
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if msg.get_content_type() == "text/plain":
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try:
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text = msg.get_content()
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if text:
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texts.append(text.strip())
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except Exception:
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||||
pass
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return "\n\n".join(texts)
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def clean_corpus(corpus, max_length=10, remove_numbers=False, exclude=None):
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result = []
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for word in corpus:
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if max_length and len(word) > max_length:
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continue
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if remove_numbers and word.isnumeric():
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continue
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if exclude and word in exclude:
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continue
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result.append(word)
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return result
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def join(tokens, punctuation=".,?!:;'\""):
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"Joins text, but does not give extra space for punctuation"
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tokens = [t if t in punctuation else ' ' + t for t in tokens]
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return ''.join(tokens).strip()
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56
tlm/model.py
Normal file
56
tlm/model.py
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@@ -0,0 +1,56 @@
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import json
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import random
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from .helpers import rolling_window, join
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class TinyLanguageModel:
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def __init__(self, n=2):
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"Create a new model that looks at n words at a time."
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self.n = n
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self.model = {}
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def train(self, words):
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"Learn word patterns from a list of words."
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for sequence in rolling_window(words, self.n + 1):
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pattern = tuple(sequence[:-1])
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next_word = sequence[-1]
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if pattern not in self.model:
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self.model[pattern] = []
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self.model[pattern].append(next_word)
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def generate(self, length, prompt=None, join_fn=None, step_callback=None):
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"Create new words based on what the model learned."
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if not self.model:
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raise Exception("The model has not been trained")
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output = list(prompt or self.get_random_pattern())
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while len(output) < length:
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pattern = tuple(output[-self.n:])
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if pattern not in self.model:
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break
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options = self.model[pattern]
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chosen = random.choice(options)
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if step_callback:
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step_callback(pattern, options, chosen)
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output.append(chosen)
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return (join_fn or join)(output)
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def get_random_pattern(self):
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"Randomly chooses one of the observed patterns"
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return random.choice(list(self.model.keys()))
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def save(self, filepath):
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"Save the model to a file."
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||||
model_data = {
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"n": self.n,
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"model": {" ".join(k): v for k, v in self.model.items()}
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||||
}
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||||
with open(filepath, "w") as f:
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json.dump(model_data, f)
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||||
def load(self, filepath):
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||||
"Load a model from a file."
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||||
with open(filepath, "r") as f:
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data = json.load(f)
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||||
self.n = data["n"]
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||||
self.model = {tuple(k.split()): v for k, v in data["model"].items()}
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||||
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||||
31
tlm/tokenization.py
Normal file
31
tlm/tokenization.py
Normal file
@@ -0,0 +1,31 @@
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def compress_whitespace(text):
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||||
"""Collapse sequences of whitespace into a single space."""
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||||
import re
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||||
return re.sub(r'\s+', ' ', text).strip()
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||||
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def _is_alpha_token(token):
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return all(c.isalpha() or c in " '" for c in token)
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||||
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||||
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def tokenize_text(text, options):
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||||
"""Tokenize a raw text string according to the given options."""
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||||
if 'lower' in options:
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text = text.lower()
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words = text.split()
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||||
if 'alpha' in options:
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words = [w for w in words if _is_alpha_token(w)]
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||||
if 'char' in options:
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||||
return list(compress_whitespace(' '.join(words)))
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||||
return words
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||||
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||||
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||||
def tokenize_words(words, options):
|
||||
"""Apply tokenization options to an already word-tokenized sequence."""
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||||
if 'lower' in options:
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||||
words = [w.lower() for w in words]
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||||
if 'alpha' in options:
|
||||
words = [w for w in words if _is_alpha_token(w)]
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||||
if 'char' in options:
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||||
return list(compress_whitespace(' '.join(words)))
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||||
return list(words)
|
||||
208
uv.lock
generated
Normal file
208
uv.lock
generated
Normal file
@@ -0,0 +1,208 @@
|
||||
version = 1
|
||||
requires-python = ">=3.10, <4.0"
|
||||
|
||||
[[package]]
|
||||
name = "click"
|
||||
version = "8.3.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "colorama", marker = "platform_system == 'Windows'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/3d/fa/656b739db8587d7b5dfa22e22ed02566950fbfbcdc20311993483657a5c0/click-8.3.1.tar.gz", hash = "sha256:12ff4785d337a1bb490bb7e9c2b1ee5da3112e94a8622f26a6c77f5d2fc6842a", size = 295065 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/98/78/01c019cdb5d6498122777c1a43056ebb3ebfeef2076d9d026bfe15583b2b/click-8.3.1-py3-none-any.whl", hash = "sha256:981153a64e25f12d547d3426c367a4857371575ee7ad18df2a6183ab0545b2a6", size = 108274 },
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]
|
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|
||||
[[package]]
|
||||
name = "colorama"
|
||||
version = "0.4.6"
|
||||
source = { registry = "https://pypi.org/simple" }
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||||
sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697 }
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wheels = [
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|
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[[package]]
|
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name = "joblib"
|
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version = "1.5.3"
|
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source = { registry = "https://pypi.org/simple" }
|
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sdist = { url = "https://files.pythonhosted.org/packages/41/f2/d34e8b3a08a9cc79a50b2208a93dce981fe615b64d5a4d4abee421d898df/joblib-1.5.3.tar.gz", hash = "sha256:8561a3269e6801106863fd0d6d84bb737be9e7631e33aaed3fb9ce5953688da3", size = 331603 }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/7b/91/984aca2ec129e2757d1e4e3c81c3fcda9d0f85b74670a094cc443d9ee949/joblib-1.5.3-py3-none-any.whl", hash = "sha256:5fc3c5039fc5ca8c0276333a188bbd59d6b7ab37fe6632daa76bc7f9ec18e713", size = 309071 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "lab-language-model"
|
||||
version = "5.0.0"
|
||||
source = { editable = "." }
|
||||
dependencies = [
|
||||
{ name = "click" },
|
||||
{ name = "nltk" },
|
||||
{ name = "tabulate" },
|
||||
{ name = "tqdm" },
|
||||
]
|
||||
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "click", specifier = ">=8.3.1" },
|
||||
{ name = "nltk", specifier = ">=3.9.2" },
|
||||
{ name = "tabulate", specifier = ">=0.9.0" },
|
||||
{ name = "tqdm", specifier = ">=4.67.3" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "nltk"
|
||||
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|
||||
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|
||||
dependencies = [
|
||||
{ name = "click" },
|
||||
{ name = "joblib" },
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{ name = "regex" },
|
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{ name = "tqdm" },
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|
||||
[[package]]
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{ url = "https://files.pythonhosted.org/packages/d0/38/dde117c76c624713c8a2842530be9c93ca8b606c0f6102d86e8cd1ce8bea/regex-2026.1.15-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:619843841e220adca114118533a574a9cd183ed8a28b85627d2844c500a2b0db", size = 799778 },
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{ url = "https://files.pythonhosted.org/packages/95/e4/a3b9480c78cf8ee86626cb06f8d931d74d775897d44201ccb813097ae697/regex-2026.1.15-cp314-cp314t-win_arm64.whl", hash = "sha256:ca89c5e596fc05b015f27561b3793dc2fa0917ea0d7507eebb448efd35274a70", size = 274837 },
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]
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[[package]]
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name = "tabulate"
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version = "0.9.0"
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source = { registry = "https://pypi.org/simple" }
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sdist = { url = "https://files.pythonhosted.org/packages/ec/fe/802052aecb21e3797b8f7902564ab6ea0d60ff8ca23952079064155d1ae1/tabulate-0.9.0.tar.gz", hash = "sha256:0095b12bf5966de529c0feb1fa08671671b3368eec77d7ef7ab114be2c068b3c", size = 81090 }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl", hash = "sha256:024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f", size = 35252 },
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]
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[[package]]
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name = "tqdm"
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version = "4.67.3"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "colorama", marker = "platform_system == 'Windows'" },
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]
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sdist = { url = "https://files.pythonhosted.org/packages/09/a9/6ba95a270c6f1fbcd8dac228323f2777d886cb206987444e4bce66338dd4/tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb", size = 169598 }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/16/e1/3079a9ff9b8e11b846c6ac5c8b5bfb7ff225eee721825310c91b3b50304f/tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf", size = 78374 },
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]
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Reference in New Issue
Block a user