diff --git a/README.md b/README.md index d2d3539..5e2458f 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,16 @@ Dataset for labelling queries containing tasks in natural language, with a focus These queries were generated by prompting various commercially available LLMs and were pre-annotated using Gemini-2.5-flash-lite. They were then converted to a label studio supported format and then annotations were manually revised. +## Training + +```bash +uv run mini_lm.py +``` + +```bash +uv run inference.py --top-k +``` + ## Setup ```bash diff --git a/dataset_analysis.py b/dataset_analysis.py index 5069d5b..a33627c 100644 --- a/dataset_analysis.py +++ b/dataset_analysis.py @@ -55,7 +55,16 @@ if __name__ == "__main__": # number based menu to match file for file in annotated_dataset_list: print(f"{annotated_dataset_list.index(file)}: {file}") - _ = input("Enter the number of the annotated dataset to analyze: ") - path = annotated_dataset_list[int(_)] - counts = parse_annotated(path) - print(json.dumps(counts, indent=2)) + print("a: all annotated datasets") + selection = input("Enter the number of the annotated dataset to analyze: ") + if selection.lower() == "a": + combined_counts = {label.name: 0 for label in TokenLabel} + for dataset_path in annotated_dataset_list: + counts = parse_annotated(dataset_path) + for label, value in counts.items(): + combined_counts[label] += value + print(json.dumps(combined_counts, indent=2)) + else: + path = annotated_dataset_list[int(selection)] + counts = parse_annotated(path) + print(json.dumps(counts, indent=2)) diff --git a/dataset_convertor.py b/dataset_convertor.py new file mode 100644 index 0000000..db8dbad --- /dev/null +++ b/dataset_convertor.py @@ -0,0 +1,90 @@ +import json + + +def parse_json_dataset(path): + with open(path, "r") as f: + tasks = json.load(f) + + for task in tasks: + label_line = "" + text_line = "" + + full_text = task["data"]["text"] + def append_segment(segment_text, segment_label): + label = f"{segment_label} " + text = f"{segment_text} " + if len(text) > len(label): + label_line_part = f"{label + ' ' * (len(text) - len(label))}" + text_line_part = text + elif len(text) < len(label): + text_line_part = f"{text + ' ' * (len(label) - len(text))}" + label_line_part = label + else: + text_line_part = text + label_line_part = label + return text_line_part, label_line_part + + def append_gap(gap_text): + segments = [] + if not gap_text: + return segments + start = 0 + end = len(gap_text) + while start < end and gap_text[start].isspace(): + start += 1 + while end > start and gap_text[end - 1].isspace(): + end -= 1 + leading = gap_text[:start] + middle = gap_text[start:end] + trailing = gap_text[end:] + if leading: + text_line_append = leading + label_line_append = " " * len(leading) + segments.append((text_line_append, label_line_append)) + if middle: + text_part, label_part = append_segment(middle, "0") + segments.append((text_part, label_part)) + if trailing: + text_line_append = trailing + label_line_append = " " * len(trailing) + segments.append((text_line_append, label_line_append)) + return segments + + results = [] + annotations = task.get("annotations") or [] + if annotations: + results = annotations[0].get("result", []) + results = sorted(results, key=lambda item: item["value"]["start"]) + + cursor = 0 + for annotation in results: + start = annotation["value"]["start"] + end = annotation["value"]["end"] + label = annotation["value"]["labels"][0] + + if cursor < start: + gap_text = full_text[cursor:start] + for text_part, label_part in append_gap(gap_text): + text_line += text_part + label_line += label_part + + text_segment = full_text[start:end] + text_part, label_part = append_segment(text_segment, label) + text_line += text_part + label_line += label_part + cursor = end + + if cursor < len(full_text): + gap_text = full_text[cursor:] + for text_part, label_part in append_gap(gap_text): + text_line += text_part + label_line += label_part + print(text_line) + print(label_line) + print("\n") + + return tasks + + +if __name__ == "__main__": + parse_json_dataset("./datasets/annotated/ffmpeg_gemini_v1.json") diff --git a/datasets/annotated/ffmpeg_gemini_v1.json b/datasets/annotated/ffmpeg_gemini_v1.json index ddc5e8b..413e7da 100644 --- a/datasets/annotated/ffmpeg_gemini_v1.json +++ 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+import argparse + +from mini_lm import predict + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Run token classification inference.") + parser.add_argument("text", nargs="*", help="Input text to label.") + parser.add_argument("--top-k", type=int, default=3) + args = parser.parse_args() + + text = " ".join(args.text).strip() or "convert foo.mp4 to mkv" + predict(text, top_k=args.top_k) diff --git a/mini_lm.py b/mini_lm.py new file mode 100644 index 0000000..90fddb7 --- /dev/null +++ b/mini_lm.py @@ -0,0 +1,165 @@ +import json +import re +from functools import partial +from pathlib import Path + +import torch +from datasets import Dataset +from transformers import ( + AutoModelForTokenClassification, + AutoTokenizer, + DataCollatorForTokenClassification, + Trainer, + TrainingArguments, +) + +from enums import TokenLabel + +MODEL_NAME = "microsoft/MiniLM-L12-H384-uncased" +MODEL_DIR = "./model" + +LABEL_LIST = [label.name for label in TokenLabel] +LABEL2ID = {label: i for i, label in enumerate(LABEL_LIST)} +ID2LABEL = {i: label for i, label in enumerate(LABEL_LIST)} +VALUE_TO_NAME = {label.value: label.name for label in TokenLabel} + + +def _normalize_label(label: str) -> str: + if label in LABEL2ID: + return label + if label in VALUE_TO_NAME: + return VALUE_TO_NAME[label] + raise ValueError(f"Unknown label: {label}") + + +def _text_to_tokens(text: str) -> tuple[list[str], list[tuple[int, int]]]: + tokens = [] + spans = [] + for match in re.finditer(r"\S+", text): + tokens.append(match.group(0)) + spans.append(match.span()) + return tokens, spans + + +def _extract_spans(task: dict) -> list[tuple[int, int, str]]: + annotations = task.get("annotations") or [] + if not annotations: + return [] + results = annotations[0].get("result", []) or [] + spans = [] + for result in results: + value = result.get("value", {}) + start = value.get("start") + end = value.get("end") + labels = value.get("labels") or [] + if start is None or end is None or not labels: + continue + spans.append((int(start), int(end), _normalize_label(labels[0]))) + return spans + + +def parse_annotated_dataset(path: str) -> list[dict]: + data = json.loads(Path(path).read_text(encoding="utf-8")) + if not isinstance(data, list): + raise ValueError("Annotated dataset must be a JSON array.") + + examples = [] + for task in data: + text = task["data"]["text"] + spans = _extract_spans(task) + tokens, token_spans = _text_to_tokens(text) + labels = [] + for token_start, token_end in token_spans: + assigned = "RAW_PHRASE" + for span_start, span_end, span_label in spans: + if token_start < span_end and token_end > span_start: + assigned = span_label + break + labels.append(assigned) + examples.append({"tokens": tokens, "labels": labels}) + + return examples + + +def tokenize_and_align(example, tokenizer, label2id): + tokenized = tokenizer(example["tokens"], is_split_into_words=True, truncation=True) + word_ids = tokenized.word_ids() + labels = [] + prev = None + + for word_id in word_ids: + if word_id is None: + labels.append(-100) + elif word_id != prev: + labels.append(label2id[example["labels"][word_id]]) + else: + labels.append(-100) + prev = word_id + + tokenized["labels"] = labels + return tokenized + + +def train(dataset_path: str = "./datasets/annotated/ffmpeg_gemini_v1.json", model_dir: str = MODEL_DIR): + tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) + model = AutoModelForTokenClassification.from_pretrained( + MODEL_NAME, num_labels=len(LABEL_LIST), id2label=ID2LABEL, label2id=LABEL2ID + ) + + examples = parse_annotated_dataset(dataset_path) + dataset = Dataset.from_list(examples) + dataset = dataset.map(partial(tokenize_and_align, tokenizer=tokenizer, label2id=LABEL2ID)) + dataset = dataset.train_test_split(test_size=0.1) + + training_args = TrainingArguments( + output_dir=model_dir, + learning_rate=3e-5, + per_device_train_batch_size=8, + num_train_epochs=5, + weight_decay=0.01, + logging_steps=10, + save_strategy="epoch", + ) + + data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer) + + trainer = Trainer( + model=model, + args=training_args, + train_dataset=dataset["train"], + eval_dataset=dataset["test"], + data_collator=data_collator, + ) + + trainer.train() + trainer.save_model(model_dir) + tokenizer.save_pretrained(model_dir) + + +def _load_model(model_dir: str = MODEL_DIR): + tokenizer = AutoTokenizer.from_pretrained(model_dir) + model = AutoModelForTokenClassification.from_pretrained(model_dir) + return tokenizer, model + + +def predict(text: str, model_dir: str = MODEL_DIR, top_k: int = 3): + tokenizer, model = _load_model(model_dir) + inputs = tokenizer(text.split(), return_tensors="pt", is_split_into_words=True) + outputs = model(**inputs) + + logits = outputs.logits[0] + probs = torch.softmax(logits, dim=-1) + k = min(max(1, top_k), probs.shape[-1]) + top_probs, top_ids = torch.topk(probs, k, dim=-1) + tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0]) + + for token, prob_row, id_row in zip(tokens, top_probs, top_ids): + preds = [ + f"{ID2LABEL.get(int(label_id), 'RAW_PHRASE')}:{float(score):.4f}" + for label_id, score in zip(id_row, prob_row) + ] + print(token, ", ".join(preds)) + + +if __name__ == "__main__": + train() diff --git a/pyproject.toml b/pyproject.toml index 60fc89f..4494f07 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,8 +5,13 @@ description = "Add your description here" readme = "README.md" requires-python = ">=3.12" dependencies = [ + "accelerate>=1.13.0", + "datasets>=4.8.4", + "git-filter-repo>=2.47.0", "google-genai>=1.70.0", "label-studio>=1.23.0", "label-studio-ml>=1.0.9", "loguru>=0.7.3", + "torch>=2.11.0", + "transformers>=5.5.0", ] diff --git a/uv.lock b/uv.lock index 75317c5..6e218c8 100644 --- a/uv.lock +++ b/uv.lock @@ -16,6 +16,140 @@ resolution-markers = [ "python_full_version >= '4' and sys_platform != 'emscripten' and sys_platform != 'win32'", ] +[[package]] +name = "accelerate" +version = "1.13.0" 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