commit 039a467a9f673e3eed6c767e2a1d2008eb01871e Author: Chris Proctor Date: Mon Mar 9 12:28:21 2026 -0400 initial commit diff --git a/.envrc b/.envrc new file mode 100644 index 0000000..8624131 --- /dev/null +++ b/.envrc @@ -0,0 +1 @@ +source .venv/bin/activate diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..505a3b1 --- /dev/null +++ b/.gitignore @@ -0,0 +1,10 @@ +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info + +# Virtual environments +.venv diff --git a/chuck.txt b/chuck.txt new file mode 100644 index 0000000..50620c8 --- /dev/null +++ b/chuck.txt @@ -0,0 +1,2 @@ +how much wood would a wood chuck chuck if a wood chuck could chuck wood +a wood chuck would chuck all the wood it could chuck if a wood chuck could chuck wood diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..9e7279b --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,24 @@ +[project] +name = "lab-embeddings" +version = "0.1.0" +description = "Embeddings lab as part of MWC" +requires-python = ">=3.13" +dependencies = [ + "click>=8.3.1", + "gensim", + "nltk>=3.9.3", + "numpy", + "tabulate>=0.9.0", + "tqdm>=4.67.3", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project.scripts] +tlm = "tlm.cli:cli" +wp = "wordplay.cli:cli" + +[tool.hatch.build.targets.wheel] +packages = ["tlm", "wordplay"] diff --git a/tlm/__init__.py b/tlm/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tlm/cli.py b/tlm/cli.py new file mode 100644 index 0000000..d453da8 --- /dev/null +++ b/tlm/cli.py @@ -0,0 +1,106 @@ +import click +from .model import TinyLanguageModel +from .helpers import read_mail_text, join +from .tokenization import tokenize_text, tokenize_words + + +@click.group() +def cli(): + """TinyLM - A language model with learned word embeddings.""" + pass + + +@cli.command() +@click.option('-n', "--context-window-words", default=None, type=int, help="Number of words in the context window. (default: 2)") +@click.option('-d', "--embedding-dim", default=None, type=int, help="Dimension of the word embeddings. (default: 32)") +@click.option('-e', "--epochs", default=5, show_default=True, help="Number of training epochs.") +@click.option('-r', "--learning-rate", default=0.05, show_default=True, help="Learning rate for gradient descent.") +@click.option('-f', "--filepath", type=click.Path(exists=True), multiple=True, help="Text file(s) to train on. Can be specified multiple times.") +@click.option('-g', "--gutenberg", multiple=True, help="NLTK Gutenberg corpus key(s). Can be specified multiple times.") +@click.option('-G', "--list-gutenberg", is_flag=True, help="List available Gutenberg corpus keys.") +@click.option('-m', "--mbox", type=click.Path(exists=True), help="Mbox file to train on.") +@click.option('-o', "--output", default="model.json", show_default=True, help="File path to save the trained model.") +@click.option('-R', "--resume", 'resume_path', type=click.Path(exists=True), default=None, help="Load a saved model and continue training it.") +@click.option('-t', "--tokenize", 'tokenize_opts', multiple=True, type=click.Choice(['lower', 'char', 'alpha']), help="Tokenization options (can be specified multiple times).") +def train(context_window_words, embedding_dim, epochs, learning_rate, filepath, gutenberg, list_gutenberg, mbox, output, resume_path, tokenize_opts): + """Train a language model on a corpus and save it to a file.""" + import nltk + + if list_gutenberg: + nltk.download("gutenberg", quiet=True) + from nltk.corpus import gutenberg as gutenberg_corpus + click.echo("Available Gutenberg corpus keys:") + for key in gutenberg_corpus.fileids(): + click.echo(f" {key}") + return + + corpus = [] + if filepath: + for fp in filepath: + with open(fp) as f: + corpus.extend(tokenize_text(f.read(), tokenize_opts)) + if gutenberg: + nltk.download("gutenberg", quiet=True) + from nltk.corpus import gutenberg as gutenberg_corpus + for key in gutenberg: + corpus.extend(tokenize_words(gutenberg_corpus.words(key), tokenize_opts)) + if mbox: + corpus.extend(tokenize_text(read_mail_text(mbox), tokenize_opts)) + + if not corpus: + raise click.UsageError("No training data provided. Use --filepath, --gutenberg, or --mbox.") + + if resume_path: + if context_window_words is not None or embedding_dim is not None: + raise click.UsageError("-n/--context-window-words and -d/--embedding-dim are ignored when resuming a saved model. Remove these options.") + model = TinyLanguageModel() + model.load(resume_path) + model.train(corpus, epochs=epochs, lr=learning_rate, resume=True) + else: + model = TinyLanguageModel(n=context_window_words or 2, embedding_dim=embedding_dim or 32) + model.train(corpus, epochs=epochs, lr=learning_rate) + model.save(output) + click.echo(f"Model saved to {output}") + + +@cli.command() +@click.option('-m', "--model", 'model_path', required=True, type=click.Path(exists=True), help="Trained model file to load.") +@click.option('-l', "--length", default=50, show_default=True, help="Number of tokens to generate.") +@click.option('-p', "--prompt", help="Prompt to start generation.") +@click.option('-v', "--verbose", is_flag=True, help="Display step-by-step generation as a table.") +@click.option('-i', "--interact", is_flag=True, help="Drop into interactive shell after generating.") +@click.option('-t', "--tokenize", 'tokenize_opts', multiple=True, type=click.Choice(['lower', 'char', 'alpha']), help="Tokenization options for the prompt.") +def generate(model_path, length, prompt, verbose, interact, tokenize_opts): + """Generate text using a trained model.""" + model = TinyLanguageModel() + model.load(model_path) + + join_fn = ''.join if 'char' in tokenize_opts else None + display_join = join_fn or join + + prompt_tokens = tokenize_text(prompt, tokenize_opts) if prompt else None + + if verbose: + from tabulate import tabulate + import textwrap + rows = [] + + def step_callback(context, options, chosen): + opts = textwrap.fill(', '.join(options), width=60) + rows.append([display_join(list(context)), opts, chosen]) + + output = model.generate(length, prompt=prompt_tokens, join_fn=join_fn, step_callback=step_callback) + click.echo(tabulate(rows, headers=["Context", "Options", "Selected"], tablefmt="simple")) + click.echo() + else: + output = model.generate(length, prompt=prompt_tokens, join_fn=join_fn) + + click.echo(output) + + if interact: + import code + code.interact(local=locals(), banner="Entering interactive shell. 'model' and 'output' are available.") + + +if __name__ == "__main__": + cli() diff --git a/tlm/helpers.py b/tlm/helpers.py new file mode 100644 index 0000000..63a68f3 --- /dev/null +++ b/tlm/helpers.py @@ -0,0 +1,61 @@ +import mailbox +import email +from email.policy import default +from tqdm import tqdm +import numpy as np + + +def rolling_window(iterable, n): + """Passes a rolling window over the iterable, yielding each n-length tuple. + rolling_window(range(5), 3) -> (0, 1, 2), (1, 2, 3), (2, 3, 4) + """ + it = iter(iterable) + try: + window = [next(it) for _ in range(n)] + while True: + yield tuple(window) + window = window[1:] + [next(it)] + except StopIteration: + return + + +def read_mail_text(mbox_path): + """ + Extract and concatenate all plaintext content from an mbox file. + """ + texts = [] + mbox = mailbox.mbox( + mbox_path, + factory=lambda f: email.message_from_binary_file(f, policy=default) + ) + for msg in tqdm(mbox): + if msg.is_multipart(): + for part in msg.walk(): + if part.get_content_type() == "text/plain": + try: + text = part.get_content() + if text: + texts.append(text.strip()) + except Exception: + pass + else: + if msg.get_content_type() == "text/plain": + try: + text = msg.get_content() + if text: + texts.append(text.strip()) + except Exception: + pass + return "\n\n".join(texts) + + +def join(tokens, punctuation=".,?!:;'\""): + "Joins text, but does not give extra space before punctuation." + tokens = [t if t in punctuation else ' ' + t for t in tokens] + return ''.join(tokens).strip() + + +def softmax(x): + "Convert a vector of scores (logits) into a probability distribution." + e = np.exp(x - x.max()) + return e / e.sum() diff --git a/tlm/model.py b/tlm/model.py new file mode 100644 index 0000000..0217c00 --- /dev/null +++ b/tlm/model.py @@ -0,0 +1,170 @@ +import json +import random +import numpy as np +from .helpers import rolling_window, join, softmax + +class TinyLanguageModel: + """ + A language model that uses learned embeddings to predict the next word. + + Instead of counting how often each word follows each context (as in the + matrix model), this model learns dense vector representations of words, + called embeddings, and uses them to estimate the probability of the next word. + + The model has two learned matrices: + + E: (vocab_size x embedding_dim) -- one row per word in the vocabulary, + representing that word as a dense vector + W: (embedding_dim x vocab_size) -- maps an embedding to one score per word + + To predict the next word, the model: + 1. Looks up the embedding E[i] for each word i in the context window. + 2. Averages those embeddings to get a single context vector. + 3. Computes logits = context_vector @ W (one score per vocabulary word). + 4. Applies softmax to turn scores into probabilities. + 5. Samples the next word from those probabilities. + + Training works by gradient descent: the model repeatedly sees (context, target) + pairs, makes a prediction, computes how wrong it was (cross-entropy loss), + and nudges E and W in the direction that reduces the loss. + """ + + def __init__(self, n=2, embedding_dim=32): + "Create a new model with context window of n words and given embedding size." + self.n = n + self.embedding_dim = embedding_dim + self.vocab = None + self.word_to_idx = None + self.E = None # input embeddings: (vocab_size, embedding_dim) + self.W = None # output weights: (embedding_dim, vocab_size) + self.b = None # output bias: (vocab_size,) + + def train(self, words, epochs=5, lr=0.05, resume=False): + """Learn word embeddings from a list of words using gradient descent. + + If resume=True, the existing vocabulary and learned matrices (E, W, b) are + kept and training continues from where it left off. Words in the corpus that + are not in the saved vocabulary are skipped with a warning. + """ + if resume: + oov = sorted({w for w in words if w not in self.word_to_idx}) + if oov: + print(f"Warning: {len(oov)} word(s) not in saved vocabulary will be skipped: {oov[:10]}{'...' if len(oov) > 10 else ''}") + else: + self.vocab = sorted(set(words)) + self.word_to_idx = {word: idx for idx, word in enumerate(self.vocab)} + self.initialize_matrices() + + training_data = [ + (window[:-1], window[-1]) + for window in rolling_window(words, self.n + 1) + if all(w in self.word_to_idx for w in window) + ] + + from tqdm import tqdm + for epoch in range(epochs): + total_loss = 0.0 + random.shuffle(training_data) + for context, target in tqdm(training_data, desc=f"Epoch {epoch + 1}/{epochs}", leave=False): + total_loss += self._step(context, target, lr) + print(f"Epoch {epoch + 1}/{epochs} loss={total_loss / len(training_data):.4f}") + + def initialize_matrices(self): + "Randomly initializes embedding, weight, and bias matrices" + vocab_size = len(self.vocab) + rng = np.random.default_rng(42) + self.E = rng.standard_normal((vocab_size, self.embedding_dim)) * 0.01 + self.W = rng.standard_normal((self.embedding_dim, vocab_size)) * 0.01 + self.b = np.zeros(vocab_size) + + def _context_embedding(self, context): + "Average the embeddings of the words in the context window." + indices = [self.word_to_idx[w] for w in context] + return self.E[indices].mean(axis=0) + + def _forward(self, context): + "Return (context_emb, probs) for a given context tuple." + ctx_emb = self._context_embedding(context) + logits = ctx_emb @ self.W + self.b + probs = softmax(logits) + return ctx_emb, probs + + def _step(self, context, target, lr): + "One gradient-descent step on a single (context, target) pair. Returns the loss." + ctx_emb, probs = self._forward(context) + target_idx = self.word_to_idx[target] + loss = -np.log(probs[target_idx] + 1e-12) + + # Gradient of cross-entropy loss w.r.t. logits: probs with 1 subtracted at target + d_logits = probs.copy() + d_logits[target_idx] -= 1.0 + + # Gradients for output weights and bias + d_W = ctx_emb[:, None] @ d_logits[None, :] # (embedding_dim, vocab_size) + d_b = d_logits # (vocab_size,) + + # Gradient flows back through averaging to each context word's embedding + d_ctx_emb = self.W @ d_logits # (embedding_dim,) + d_per_word = d_ctx_emb / len(context) + for idx in [self.word_to_idx[w] for w in context]: + self.E[idx] -= lr * d_per_word + + self.W -= lr * d_W + self.b -= lr * d_b + + return loss + + def generate(self, length, prompt=None, join_fn=None, step_callback=None): + "Create new text using the learned embeddings." + if self.E is None: + raise Exception("The model has not been trained.") + + output = list(prompt or self.get_random_prompt()) + # Drop any prompt tokens not in vocabulary + output = [w for w in output if w in self.word_to_idx] + if len(output) < self.n: + output = list(self.get_random_prompt()) + + while len(output) < length: + context = tuple(output[-self.n:]) + _, probs = self._forward(context) + chosen_idx = np.random.choice(len(self.vocab), p=probs) + chosen_word = self.vocab[chosen_idx] + + if step_callback: + top_indices = np.argsort(probs)[-10:][::-1] + top_words = [self.vocab[i] for i in top_indices if probs[i] > 0.001] + step_callback(context, top_words, chosen_word) + + output.append(chosen_word) + + return (join_fn or join)(output) + + def get_random_prompt(self): + "Return a random context window drawn from the vocabulary." + return tuple(random.choices(self.vocab, k=self.n)) + + def save(self, filepath): + "Save the model to a JSON file." + model_data = { + "n": self.n, + "embedding_dim": self.embedding_dim, + "vocab": self.vocab, + "E": self.E.tolist(), + "W": self.W.tolist(), + "b": self.b.tolist(), + } + with open(filepath, "w") as f: + json.dump(model_data, f) + + def load(self, filepath): + "Load a model from a JSON file." + with open(filepath) as f: + data = json.load(f) + self.n = data["n"] + self.embedding_dim = data["embedding_dim"] + self.vocab = data["vocab"] + self.word_to_idx = {word: idx for idx, word in enumerate(self.vocab)} + self.E = np.array(data["E"]) + self.W = np.array(data["W"]) + self.b = np.array(data["b"]) diff --git a/tlm/tokenization.py b/tlm/tokenization.py new file mode 100644 index 0000000..a9c2289 --- /dev/null +++ b/tlm/tokenization.py @@ -0,0 +1,31 @@ +def compress_whitespace(text): + """Collapse sequences of whitespace into a single space.""" + import re + return re.sub(r'\s+', ' ', text).strip() + + +def _is_alpha_token(token): + return all(c.isalpha() or c in " '" for c in token) + + +def tokenize_text(text, options): + """Tokenize a raw text string according to the given options.""" + if 'lower' in 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ctx.ensure_object(dict) + ctx.obj['model'] = wordplay.load_embeddings(size) + + +@cli.command() +@click.argument('word') +@click.option('-n', default=5, show_default=True, help='Number of synonyms to return.') +@click.pass_obj +def synonyms(obj, word, n): + """Find the n most similar words to WORD.""" + click.echo(wordplay.synonyms(obj['model'], word, n)) + + +@cli.command() +@click.argument('word1') +@click.argument('word2') +@click.pass_obj +def average(obj, word1, word2): + """Return a word which is the average of the two given words.""" + click.echo(wordplay.average(obj['model'], word1, word2)) + + +@cli.command() +@click.argument('words', nargs=-1, required=True) +@click.pass_obj +def outlier(obj, words): + """Given a list of words, identifies the word which is the outlier.""" + click.echo(wordplay.outlier(obj['model'], words)) + + +@cli.command() +@click.argument('words', nargs=-1, required=True) +@click.pass_obj +def sort(obj, words): + """Given a list of words, sorts them in order.""" + click.echo(wordplay.sort(obj['model'], words)) + + +if __name__ == '__main__': + cli() diff --git a/wordplay/wordplay.py b/wordplay/wordplay.py new file mode 100644 index 0000000..a8bfd8a --- /dev/null +++ b/wordplay/wordplay.py @@ -0,0 +1,28 @@ +import gensim.downloader + +EMBEDDING_MODELS = { + 'small': 'glove-wiki-gigaword-100', # ~128 MB + 'medium': 'glove-wiki-gigaword-300', # ~376 MB + 'large': 'word2vec-google-news-300', # ~1.6 GB +} + + +def load_embeddings(size='small'): + "Download (if needed) and load the embedding model for the given size." + return gensim.downloader.load(EMBEDDING_MODELS[size]) + + +def synonyms(model, word, n=5): + pass + + +def average(model, word1, word2): + pass + + +def outlier(model, words): + pass + + +def sort(model, words): + pass