diff --git a/backend/balanceteshaters/scripts/ml/00_prepare_dataset.py b/backend/balanceteshaters/scripts/ml/00_prepare_dataset.py new file mode 100644 index 00000000..58e18d76 --- /dev/null +++ b/backend/balanceteshaters/scripts/ml/00_prepare_dataset.py @@ -0,0 +1,75 @@ +# ruff: noqa: E402 +import argparse +import os +import sys +from pathlib import Path + +import pandas as pd +from dotenv import load_dotenv +from sklearn.model_selection import train_test_split + +SCRIPTS_DIR = Path(__file__).resolve().parent.parent.parent.parent +if str(SCRIPTS_DIR) not in sys.path: + sys.path.insert(0, str(SCRIPTS_DIR)) + +from balanceteshaters.services.annotation import AnnotationService, BinaryConfidence +from balanceteshaters.services.nocodb import NocoDBService +from balanceteshaters.scripts.ml.config import ANNOTATION_TABLE_ID, DATA_DIR, compute_binary_label + + +def main(): + parser = argparse.ArgumentParser(description="Prepare train/val/test splits from NocoDB annotations") + parser.add_argument("--high-confidence-only", action="store_true", help="Keep only HIGH_CONFIDENCE annotations") + args = parser.parse_args() + + load_dotenv() + nocodb = NocoDBService( + nocodb_url=os.environ["NOCODB_BASE_URL"], + token=os.environ["NOCODB_TOKEN"], + base_id=os.environ["NOCODB_BASE_ID"], + ) + service = AnnotationService(nocodb=nocodb, annotation_table_id=ANNOTATION_TABLE_ID) + + print("Fetching annotations from NocoDB...") + annotations = service.fetch_records_paginated() + print(f" Total records fetched: {len(annotations)}") + + rows = [] + for ann in annotations: + if not ann.annotated_category: + continue + if args.high_confidence_only and ann.binary_confidence != BinaryConfidence.HIGH_CONFIDENCE: + continue + cats = [c.value for c in ann.annotated_category] + label = compute_binary_label(cats) + if label is None: + continue + rows.append({ + "id": ann.id, + "comment": ann.comment, + "label": label, + "annotated_category": ",".join(cats), + "binary_confidence": ann.binary_confidence.value if ann.binary_confidence else None, + "source": "real", + }) + + df = pd.DataFrame(rows) + print(f" Usable annotated records: {len(df)}") + print(f" Label distribution: {df['label'].value_counts().to_dict()}") + + train_val, test = train_test_split(df, test_size=0.15, stratify=df["label"], random_state=42) + train, val = train_test_split(train_val, test_size=0.15 / 0.85, stratify=train_val["label"], random_state=42) + + DATA_DIR.mkdir(parents=True, exist_ok=True) + train.to_parquet(DATA_DIR / "train_real.parquet", index=False) + val.to_parquet(DATA_DIR / "val.parquet", index=False) + test.to_parquet(DATA_DIR / "test.parquet", index=False) + + print(f"\nSplits saved to {DATA_DIR}") + for name, split in [("train_real", train), ("val", val), ("test", test)]: + dist = split["label"].value_counts().to_dict() + print(f" {name}: {len(split)} rows label dist={dist}") + + +if __name__ == "__main__": + main() diff --git a/backend/balanceteshaters/scripts/ml/01_generate_synthetic.py b/backend/balanceteshaters/scripts/ml/01_generate_synthetic.py new file mode 100644 index 00000000..1b47e9fe --- /dev/null +++ b/backend/balanceteshaters/scripts/ml/01_generate_synthetic.py @@ -0,0 +1,266 @@ +# ruff: noqa: E402 +""" +Generate synthetic French social media comments for minority harassment categories. +Produces synthetic.parquet and train_augmented.parquet (train_real + synthetic). + +Improvements over v1: + - Bigger model (Sonnet by default) for higher-quality, more nuanced output + - Harder examples: subtle language, indirect threats, edge cases near category boundaries + - Few-shot grounding: real examples from train set included in each prompt +""" +import argparse +import os +import random +import re +import sys +from pathlib import Path + +import anthropic +import pandas as pd +from dotenv import load_dotenv + +SCRIPTS_DIR = Path(__file__).resolve().parent.parent.parent.parent +if str(SCRIPTS_DIR) not in sys.path: + sys.path.insert(0, str(SCRIPTS_DIR)) + +from balanceteshaters.scripts.ml.config import DATA_DIR + +# Pricing per million tokens (as of 2025) +MODEL_PRICING = { + "claude-haiku-4-5-20251001": {"input": 0.80, "output": 4.00, "cache_write": 1.00, "cache_read": 0.08}, + "claude-sonnet-4-6": {"input": 3.00, "output": 15.00, "cache_write": 3.75, "cache_read": 0.30}, + "claude-opus-4-7": {"input": 15.00, "output": 75.00, "cache_write": 18.75, "cache_read": 1.50}, +} + +TOTAL_TARGET = 1000 +BATCH_SIZE = 10 +FEW_SHOT_PER_CATEGORY = 5 # real examples to include in each generation prompt + +CATEGORY_DESCRIPTIONS = { + "Doxxing": "publication ou menace de publication d'informations privées (adresse, lieu de travail, numéro de téléphone, photo personnelle, identité réelle)", + "Incitation au suicide": "encouragement à se suicider ou à se blesser soi-même, messages du type 'tue-toi', 'va mourir', 'le monde irait mieux sans toi'", + "Cyberharcèlement à caractère sexuel": "harcèlement de nature sexuelle, propositions non sollicitées, slut-shaming, menaces sexuelles, commentaires dégradants sur le corps ou la sexualité", + "Menaces": "menaces directes ou voilées de violence physique, intimidation, messages impliquant des représailles", + "Incitation à la haine": "appels à la haine envers un groupe (racisme, homophobie, islamophobie, antisémitisme, transphobie, etc.), déshumanisation", + "Absence de cyberharcèlement": "commentaire anodin, critique constructive, expression d'une opinion sans agressivité, humour bienveillant, désaccord poli", +} + +SYSTEM_PROMPT = """Tu es un expert en modération de contenu et en sécurité en ligne. Tu génères des exemples de commentaires de réseaux sociaux en français pour entraîner des modèles de détection de cyberharcèlement. + +Les commentaires doivent être réalistes et variés : +- Style : argot, verlan, abréviations SMS, emojis, fautes d'orthographe intentionnelles, mélange français/anglais +- Longueur : courts à moyens (5-80 mots), comme on en trouve sur Instagram, TikTok ou Twitter +- Difficulté : inclure un mélange d'exemples évidents ET d'exemples subtils/ambigus qui nécessitent une lecture attentive pour être classifiés +- Pour les catégories de harcèlement : certains doivent utiliser un langage indirect, des métaphores, du sous-entendu, ou du codé plutôt que des insultes directes +- Pour l'absence de harcèlement : inclure des cas qui ressemblent superficiellement à du harcèlement mais n'en sont pas (critique légitime, humour, sarcasme bienveillant) + +IMPORTANT : génère UNIQUEMENT des commentaires bruts, sans explication ni méta-commentaire. Chaque commentaire sur une ligne séparée. Numérote-les de 1 à N.""" + + +def estimate_cost(model: str, allocation: dict[str, int], n_shots: int) -> float: + pricing = MODEL_PRICING[model] + n_categories = len(allocation) + total_calls = sum(-(-v // BATCH_SIZE) for v in allocation.values()) + avg_system_tokens = 350 + avg_shots_tokens = n_shots * 20 # ~20 tokens per real example + avg_user_tokens = 80 + avg_shots_tokens + avg_output_tokens = BATCH_SIZE * 30 + + # First call per category writes the system prompt to cache; subsequent calls hit cache + cache_write_calls = n_categories + cache_read_calls = max(0, total_calls - n_categories) + + cost = ( + (cache_write_calls * avg_system_tokens * pricing["cache_write"] + + cache_read_calls * avg_system_tokens * pricing["cache_read"] + + total_calls * avg_user_tokens * pricing["input"] + + total_calls * avg_output_tokens * pricing["output"]) + / 1_000_000 + ) + return cost + + +def allocate_examples(train_df: pd.DataFrame) -> dict[str, int]: + benign_count = TOTAL_TARGET // 5 # 200 benign + harassment_count = TOTAL_TARGET - benign_count # 800 harassment + + harassment_cats = [c for c in CATEGORY_DESCRIPTIONS if c != "Absence de cyberharcèlement"] + per_cat = harassment_count // len(harassment_cats) + remainder = harassment_count % len(harassment_cats) + + allocation = {cat: per_cat for cat in harassment_cats} + for i, cat in enumerate(harassment_cats[:remainder]): + allocation[cat] += 1 + allocation["Absence de cyberharcèlement"] = benign_count + return allocation + + +def get_real_examples(train_df: pd.DataFrame, category: str, n: int) -> list[str]: + """Sample up to n real training examples for a given category.""" + col = "annotated_category" + if col not in train_df.columns: + return [] + subset = train_df[train_df[col] == category]["comment"].dropna().tolist() + if not subset: + # fall back: for benign, use label=0; for harassment, label=1 + label = 0 if category == "Absence de cyberharcèlement" else 1 + subset = train_df[train_df["label"] == label]["comment"].dropna().tolist() + return random.sample(subset, min(n, len(subset))) + + +def generate_batch( + client: anthropic.Anthropic, + category: str, + n: int, + real_examples: list[str], + tokens_used: dict, + model: str, +) -> list[str]: + description = CATEGORY_DESCRIPTIONS[category] + + shots_block = "" + if real_examples: + formatted = "\n".join(f" • {ex[:150]}" for ex in real_examples) + shots_block = f"\nExemples RÉELS de cette catégorie (pour calibrer le style et la difficulté) :\n{formatted}\n\nGénère des commentaires DIFFÉRENTS de ces exemples mais de style et difficulté similaires.\n" + + user_msg = ( + f"Catégorie : **{category}**\n" + f"Description : {description}\n" + f"{shots_block}\n" + f"Génère exactement {n} commentaires, numérotés de 1 à {n}." + ) + + response = client.messages.create( + model=model, + max_tokens=n * 80 + 150, + system=[ + { + "type": "text", + "text": SYSTEM_PROMPT, + "cache_control": {"type": "ephemeral"}, + } + ], + messages=[{"role": "user", "content": user_msg}], + ) + + tokens_used["input"] += response.usage.input_tokens + tokens_used["output"] += response.usage.output_tokens + if hasattr(response.usage, "cache_read_input_tokens"): + tokens_used["cache_read"] += response.usage.cache_read_input_tokens + if hasattr(response.usage, "cache_creation_input_tokens"): + tokens_used["cache_write"] += response.usage.cache_creation_input_tokens + + lines = response.content[0].text.strip().split("\n") + comments = [] + for line in lines: + line = line.strip() + if not line: + continue + cleaned = re.sub(r"^\d+[.)]\s*", "", line).strip() + if cleaned: + comments.append(cleaned) + return comments[:n] + + +def main(): + parser = argparse.ArgumentParser(description="Generate synthetic French harassment comments") + parser.add_argument("--dry-run", action="store_true", help="Print allocation and cost estimate only") + parser.add_argument( + "--model", + choices=list(MODEL_PRICING.keys()), + default="claude-sonnet-4-6", + help="Anthropic model to use for generation", + ) + parser.add_argument("--total", type=int, default=TOTAL_TARGET, help="Total examples to generate") + args = parser.parse_args() + + load_dotenv() + + train_path = DATA_DIR / "train_real.parquet" + if not train_path.exists(): + print(f"ERROR: {train_path} not found. Run 00_prepare_dataset.py first.") + sys.exit(1) + + train_df = pd.read_parquet(train_path) + allocation = allocate_examples(train_df) + # Rescale if --total was overridden + if args.total != TOTAL_TARGET: + scale = args.total / TOTAL_TARGET + allocation = {k: max(1, round(v * scale)) for k, v in allocation.items()} + + cost_estimate = estimate_cost(args.model, allocation, FEW_SHOT_PER_CATEGORY) + + print(f"=== Synthetic data allocation ({sum(allocation.values())} total) ===") + for cat, n in allocation.items(): + real_count = len(train_df[train_df["annotated_category"] == cat]) if "annotated_category" in train_df.columns else "?" + print(f" {cat}: {n} synthetic (real in train: {real_count})") + print(f"\nModel: {args.model}") + print(f"Few-shot examples per prompt: {FEW_SHOT_PER_CATEGORY}") + print(f"Estimated API cost: ~${cost_estimate:.3f}") + + if args.dry_run: + print("\n[dry-run] No API calls made.") + return + + api_key = os.environ.get("ANTHROPIC_API_KEY") + if not api_key: + print("ERROR: ANTHROPIC_API_KEY not set in environment.") + sys.exit(1) + + client = anthropic.Anthropic(api_key=api_key) + tokens_used = {"input": 0, "output": 0, "cache_read": 0, "cache_write": 0} + + all_rows = [] + for category, total_needed in allocation.items(): + print(f"\nGenerating {total_needed} examples for: {category}") + label = 0 if category == "Absence de cyberharcèlement" else 1 + real_examples = get_real_examples(train_df, category, FEW_SHOT_PER_CATEGORY) + print(f" Using {len(real_examples)} real few-shot examples") + + generated = [] + while len(generated) < total_needed: + batch_n = min(BATCH_SIZE, total_needed - len(generated)) + # Resample real examples each batch to add variety + shots = get_real_examples(train_df, category, FEW_SHOT_PER_CATEGORY) + batch = generate_batch(client, category, batch_n, shots, tokens_used, args.model) + generated.extend(batch) + print(f" {len(generated)}/{total_needed}", end="\r") + + for comment in generated[:total_needed]: + all_rows.append({ + "id": None, + "comment": comment, + "label": label, + "annotated_category": category, + "binary_confidence": None, + "source": "synthetic_v2", + }) + + samples = random.sample(generated[:total_needed], min(5, len(generated))) + print(f"\n Samples from '{category}':") + for s in samples: + print(f" • {s[:120]}") + + synthetic_df = pd.DataFrame(all_rows) + synthetic_df.to_parquet(DATA_DIR / "synthetic_v2.parquet", index=False) + + augmented_df = pd.concat([train_df, synthetic_df], ignore_index=True) + augmented_df.to_parquet(DATA_DIR / "train_augmented_v2.parquet", index=False) + + pricing = MODEL_PRICING[args.model] + actual_cost = ( + tokens_used["input"] * pricing["input"] + + tokens_used["output"] * pricing["output"] + + tokens_used.get("cache_write", 0) * pricing["cache_write"] + + tokens_used.get("cache_read", 0) * pricing["cache_read"] + ) / 1_000_000 + print("\n=== Done ===") + print(f" Synthetic examples: {len(synthetic_df)}") + print(f" train_augmented_v2 size: {len(augmented_df)}") + print(f" Tokens — input: {tokens_used['input']}, output: {tokens_used['output']}, cache_read: {tokens_used['cache_read']}, cache_write: {tokens_used['cache_write']}") + print(f" Actual API cost: ~${actual_cost:.4f}") + print(f" Files: {DATA_DIR}/synthetic_v2.parquet, train_augmented_v2.parquet") + + +if __name__ == "__main__": + main() diff --git a/backend/balanceteshaters/scripts/ml/02_embed_and_train_classical.py b/backend/balanceteshaters/scripts/ml/02_embed_and_train_classical.py new file mode 100644 index 00000000..ad7591d0 --- /dev/null +++ b/backend/balanceteshaters/scripts/ml/02_embed_and_train_classical.py @@ -0,0 +1,233 @@ +# ruff: noqa: E402 +""" +Train three classifiers on frozen embeddings: + - Logistic Regression (linear baseline) + - LightGBM (tree ensemble) + - MLP (512→128) (neural head, best at dense vectors) + +Loops over 2 embedding models × 3 classifiers × 2 datasets = 12 runs. +Embeddings for val/test are shared per encoder to avoid recomputing. +""" +# Must be set before torch is imported: jina-v5 EuroBERT allocates MPS memory +# even when device="cpu", which segfaults on Apple Silicon. +import os +os.environ.setdefault("PYTORCH_MPS_HIGH_WATERMARK_RATIO", "0.0") + +import argparse +import sys +import time +from pathlib import Path + +import joblib +import numpy as np +import pandas as pd +import sklearn.metrics +from dotenv import load_dotenv +from sentence_transformers import SentenceTransformer + +SCRIPTS_DIR = Path(__file__).resolve().parent.parent.parent.parent +if str(SCRIPTS_DIR) not in sys.path: + sys.path.insert(0, str(SCRIPTS_DIR)) + +from balanceteshaters.scripts.ml.config import ( + ANNOTATION_TABLE_ID, + ARCTIC_EMBED_MODEL_ID, + BIDIR_MODEL_ID, + CHECKPOINTS_DIR, + DATA_DIR, + EVAL_TABLE_ID, + JINA_MODEL_ID, + JINA_SMALL_MODEL_ID, + get_device_for_model, + model_slug, +) +from balanceteshaters.services.nocodb import NocoDBService + + +def embed(model: SentenceTransformer, texts: list[str], task: str | None = None, batch_size: int = 32) -> np.ndarray: + kwargs = {"batch_size": batch_size, "show_progress_bar": True, "convert_to_numpy": True} + if task is not None: + kwargs["task"] = task + return model.encode(texts, **kwargs).astype(np.float32) + + +def evaluate(y_true, y_pred) -> dict: + return { + "f1": sklearn.metrics.f1_score(y_true, y_pred, zero_division=0), + "precision": sklearn.metrics.precision_score(y_true, y_pred, zero_division=0), + "recall": sklearn.metrics.recall_score(y_true, y_pred, zero_division=0), + "accuracy": sklearn.metrics.accuracy_score(y_true, y_pred), + } + + +def log_to_nocodb(nocodb, run_name: str, dataset: str, metrics: dict, n_total: int, n_pos: int, model_type: str = "encoder embedding"): + if nocodb is None: + return + data = { + "model_name": run_name, + "table_id": ANNOTATION_TABLE_ID, + "table_name": f"finetune/{dataset}", + "f1": metrics["f1"], + "precision": metrics["precision"], + "recall": metrics["recall"], + "accuracy": metrics["accuracy"], + "total_samples": n_total, + "positive_samples": n_pos, + "negative_samples": n_total - n_pos, + "prompt": f"frozen+{run_name.split('+')[1]} dataset={dataset}", + "model_type": model_type, + } + try: + nocodb.create_record(EVAL_TABLE_ID, data) + except Exception as e: + print(f" [warn] NocoDB logging failed: {e}") + + +def make_classifiers(): + # Lazy imports so LightGBM's OpenMP doesn't initialize before jina loads + from lightgbm import LGBMClassifier + from sklearn.linear_model import LogisticRegression + from sklearn.neural_network import MLPClassifier + from sklearn.pipeline import Pipeline + from sklearn.preprocessing import StandardScaler + + return [ + ( + "logreg", + Pipeline([ + ("scaler", StandardScaler()), + ("clf", LogisticRegression(max_iter=1000, C=1.0)), + ]), + False, + ), + ( + "lightgbm", + LGBMClassifier(n_estimators=500, learning_rate=0.05, max_depth=6, verbose=-1, n_jobs=1), + True, # uses early stopping on val set + ), + ( + "mlp", + Pipeline([ + ("scaler", StandardScaler()), + ("clf", MLPClassifier( + hidden_layer_sizes=(512, 128), + activation="relu", + max_iter=200, + early_stopping=True, + validation_fraction=0.1, + n_iter_no_change=10, + random_state=42, + )), + ]), + False, + ), + ] + + +def run_for_encoder(model_id: str, is_jina: bool, datasets: list[str], nocodb): + import gc + device = get_device_for_model(model_id) + print(f"\n{'='*60}") + print(f"Encoder: {model_id} device={device}") + + load_kwargs = {"device": device, "trust_remote_code": True} + st_model = SentenceTransformer(model_id, **load_kwargs) + task = "classification" if is_jina else None + + val_df = pd.read_parquet(DATA_DIR / "val.parquet") + test_df = pd.read_parquet(DATA_DIR / "test.parquet") + + print("Embedding val set...") + X_val = embed(st_model, val_df["comment"].tolist(), task=task) + y_val = val_df["label"].values + + print("Embedding test set...") + X_test = embed(st_model, test_df["comment"].tolist(), task=task) + y_test = test_df["label"].values + + # Embed all training splits before freeing the encoder + train_embeddings: dict[str, tuple[np.ndarray, np.ndarray]] = {} + for dataset in datasets: + train_file = DATA_DIR / f"train_{dataset}.parquet" + if not train_file.exists(): + print(f" [skip] {train_file.name} not found") + continue + train_df = pd.read_parquet(train_file) + print(f"\nEmbedding train set ({dataset}, {len(train_df)} rows)...") + train_embeddings[dataset] = ( + embed(st_model, train_df["comment"].tolist(), task=task), + train_df["label"].values, + ) + + # Free encoder before initialising classifier threads (avoids OpenMP conflict) + del st_model + gc.collect() + + slug = model_slug(model_id) + CHECKPOINTS_DIR.mkdir(parents=True, exist_ok=True) + + for dataset, (X_train, y_train) in train_embeddings.items(): + for clf_name, clf, needs_val in make_classifiers(): + run_name = f"{slug}+{clf_name}+{dataset}" + print(f"\n Training {run_name}...") + t0 = time.time() + + if needs_val: + from lightgbm import early_stopping, log_evaluation + clf.fit( + X_train, y_train, + eval_set=[(X_val, y_val)], + callbacks=[early_stopping(50, verbose=False), log_evaluation(-1)], + ) + else: + clf.fit(X_train, y_train) + + elapsed = time.time() - t0 + y_pred = clf.predict(X_test) + m = evaluate(y_test, y_pred) + n_pos = int(y_test.sum()) + + print(f" F1={m['f1']:.4f} P={m['precision']:.4f} R={m['recall']:.4f} Acc={m['accuracy']:.4f} ({elapsed:.1f}s)") + + ckpt_path = CHECKPOINTS_DIR / f"{run_name}.joblib" + joblib.dump(clf, ckpt_path) + print(f" Saved to {ckpt_path.name}") + + log_to_nocodb(nocodb, run_name, dataset, m, len(y_test), n_pos) + + +def main(): + parser = argparse.ArgumentParser(description="Embed + train classical ML classifiers") + parser.add_argument("--models", nargs="+", choices=["jina", "jina-small", "bidir", "arctic", "all"], default=["all"]) + parser.add_argument("--datasets", nargs="+", choices=["real", "augmented", "augmented_v2", "all"], default=["all"]) + args = parser.parse_args() + + load_dotenv() + nocodb = None + if all(os.environ.get(k) for k in ["NOCODB_BASE_URL", "NOCODB_TOKEN", "NOCODB_BASE_ID"]): + nocodb = NocoDBService( + nocodb_url=os.environ["NOCODB_BASE_URL"], + token=os.environ["NOCODB_TOKEN"], + base_id=os.environ["NOCODB_BASE_ID"], + ) + + encoders = [] + if "all" in args.models or "jina" in args.models: + encoders.append((JINA_MODEL_ID, True)) + if "all" in args.models or "jina-small" in args.models: + encoders.append((JINA_SMALL_MODEL_ID, True)) + if "all" in args.models or "bidir" in args.models: + encoders.append((BIDIR_MODEL_ID, False)) + if "all" in args.models or "arctic" in args.models: + encoders.append((ARCTIC_EMBED_MODEL_ID, False)) + + datasets = ["real", "augmented", "augmented_v2"] if "all" in args.datasets else args.datasets + + for model_id, is_jina in encoders: + run_for_encoder(model_id, is_jina, datasets, nocodb) + + print("\nAll runs complete.") + + +if __name__ == "__main__": + main() diff --git a/backend/balanceteshaters/scripts/ml/03_finetune_embedding.py b/backend/balanceteshaters/scripts/ml/03_finetune_embedding.py new file mode 100644 index 00000000..2bba2856 --- /dev/null +++ b/backend/balanceteshaters/scripts/ml/03_finetune_embedding.py @@ -0,0 +1,231 @@ +# ruff: noqa: E402 +""" +Fine-tune embedding models for binary harassment classification. + +Usage: + python 03_finetune_embedding.py --model bidir --strategy full --dataset real + python 03_finetune_embedding.py --model arctic --strategy head_only --dataset augmented_v2 +""" +import os +os.environ.setdefault("PYTORCH_MPS_HIGH_WATERMARK_RATIO", "0.0") + +import argparse +import sys +import time +from pathlib import Path + +import pandas as pd +import sklearn.metrics +import torch +from dotenv import load_dotenv +from sentence_transformers import SentenceTransformer +from torch.utils.data import DataLoader, Dataset + +SCRIPTS_DIR = Path(__file__).resolve().parent.parent.parent.parent +if str(SCRIPTS_DIR) not in sys.path: + sys.path.insert(0, str(SCRIPTS_DIR)) + +from balanceteshaters.scripts.ml.config import ( + ANNOTATION_TABLE_ID, + ARCTIC_EMBED_MODEL_ID, + BIDIR_MODEL_ID, + CHECKPOINTS_DIR, + DATA_DIR, + EVAL_TABLE_ID, + JINA_MODEL_ID, + JINA_SMALL_MODEL_ID, + get_device_for_model, + model_slug, +) +from balanceteshaters.scripts.ml.models import EmbeddingClassifier +from balanceteshaters.services.nocodb import NocoDBService + + +class TextDataset(Dataset): + def __init__(self, df: pd.DataFrame): + self.texts = df["comment"].tolist() + self.labels = torch.tensor(df["label"].values, dtype=torch.long) + + def __len__(self): + return len(self.labels) + + def __getitem__(self, idx): + return self.texts[idx], self.labels[idx] + + +def log_to_nocodb(nocodb, run_name: str, dataset: str, strategy: str, metrics: dict, n_total: int, n_pos: int): + if nocodb is None: + return + data = { + "model_name": run_name, + "table_id": ANNOTATION_TABLE_ID, + "table_name": f"finetune/{dataset}", + "f1": metrics["f1"], + "precision": metrics["precision"], + "recall": metrics["recall"], + "accuracy": metrics["accuracy"], + "total_samples": n_total, + "positive_samples": n_pos, + "negative_samples": n_total - n_pos, + "prompt": f"strategy={strategy} dataset={dataset}", + "model_type": "encoder embedding", + } + try: + nocodb.create_record(EVAL_TABLE_ID, data) + except Exception as e: + print(f" [warn] NocoDB logging failed: {e}") + + +def run_finetune(model_id: str, is_jina: bool, strategy: str, dataset: str, nocodb, device: str): + slug = model_slug(model_id) + run_name = f"{slug}-finetuned-{strategy}-{dataset}" + ckpt_dir = CHECKPOINTS_DIR / run_name + ckpt_dir.mkdir(parents=True, exist_ok=True) + + print(f"\n{'='*60}") + print(f"Fine-tuning {model_id} strategy={strategy} dataset={dataset}") + + load_kwargs = {"device": device} + if is_jina: + load_kwargs["trust_remote_code"] = True + encoder = SentenceTransformer(model_id, **load_kwargs) + + probe_kwargs = {"convert_to_numpy": True, "show_progress_bar": False} + if is_jina: + probe_kwargs["task"] = "classification" + embed_dim = encoder.encode(["probe"], **probe_kwargs).shape[1] + + task = "classification" if is_jina else None + clf_model = EmbeddingClassifier(encoder, embed_dim, task=task, trainable_encoder=(strategy == "full")).to(device) + + if strategy == "head_only": + for param in clf_model.encoder.parameters(): + param.requires_grad = False + lr = 1e-3 + batch_size = 32 + max_epochs = 50 + patience = 15 + else: + lr = 2e-5 + batch_size = 16 + max_epochs = 15 + patience = 5 + + train_df = pd.read_parquet(DATA_DIR / f"train_{dataset}.parquet") + val_df = pd.read_parquet(DATA_DIR / "val.parquet") + test_df = pd.read_parquet(DATA_DIR / "test.parquet") + + train_loader = DataLoader(TextDataset(train_df), batch_size=batch_size, shuffle=True) + val_loader = DataLoader(TextDataset(val_df), batch_size=32, shuffle=False) + + optimizer = torch.optim.AdamW( + filter(lambda p: p.requires_grad, clf_model.parameters()), lr=lr, weight_decay=0.01 + ) + + best_f1 = 0.0 + no_improve = 0 + best_state = None + t0 = time.time() + + for epoch in range(1, max_epochs + 1): + clf_model.train() + total_loss = 0.0 + for texts, labels in train_loader: + labels = labels.to(device) + optimizer.zero_grad() + loss, _ = clf_model(texts, labels) + loss.backward() + optimizer.step() + total_loss += loss.item() + + clf_model.eval() + all_preds, all_labels = [], [] + with torch.no_grad(): + for texts, labels in val_loader: + logits = clf_model(texts) + preds = torch.argmax(logits, dim=-1).cpu().numpy() + all_preds.extend(preds) + all_labels.extend(labels.numpy()) + + val_f1 = sklearn.metrics.f1_score(all_labels, all_preds, zero_division=0) + avg_loss = total_loss / len(train_loader) + + if val_f1 > best_f1: + best_f1 = val_f1 + best_state = {k: v.cpu().clone() for k, v in clf_model.state_dict().items()} + torch.save(best_state, ckpt_dir / "best_model.pt") + no_improve = 0 + print(f" Epoch {epoch} loss={avg_loss:.4f} val_f1={val_f1:.4f} ← best") + else: + no_improve += 1 + print(f" Epoch {epoch} loss={avg_loss:.4f} val_f1={val_f1:.4f} (no improve {no_improve}/{patience})") + if no_improve >= patience: + print(f" Early stopping at epoch {epoch}") + break + + elapsed = time.time() - t0 + + if best_state: + clf_model.load_state_dict(best_state) + + clf_model.eval() + test_loader = DataLoader(TextDataset(test_df), batch_size=32, shuffle=False) + all_preds, all_labels = [], [] + with torch.no_grad(): + for texts, labels in test_loader: + logits = clf_model(texts) + preds = torch.argmax(logits, dim=-1).cpu().numpy() + all_preds.extend(preds) + all_labels.extend(labels.numpy()) + + metrics = { + "f1": sklearn.metrics.f1_score(all_labels, all_preds, zero_division=0), + "precision": sklearn.metrics.precision_score(all_labels, all_preds, zero_division=0), + "recall": sklearn.metrics.recall_score(all_labels, all_preds, zero_division=0), + "accuracy": sklearn.metrics.accuracy_score(all_labels, all_preds), + } + y_test = test_df["label"].values + + print(f"Test F1={metrics['f1']:.4f} P={metrics['precision']:.4f} R={metrics['recall']:.4f} Acc={metrics['accuracy']:.4f} ({elapsed:.0f}s)") + print(f" Saved to {ckpt_dir}") + log_to_nocodb(nocodb, run_name, dataset, strategy, metrics, len(y_test), int(y_test.sum())) + + +def main(): + parser = argparse.ArgumentParser(description="Fine-tune embedding model for binary classification") + parser.add_argument("--model", choices=["jina", "jina-small", "bidir", "arctic"], required=True) + parser.add_argument("--strategy", choices=["head_only", "full"], default="full") + parser.add_argument("--dataset", choices=["real", "augmented", "augmented_v2"], default="real") + args = parser.parse_args() + + load_dotenv() + nocodb = None + if all(os.environ.get(k) for k in ["NOCODB_BASE_URL", "NOCODB_TOKEN", "NOCODB_BASE_ID"]): + nocodb = NocoDBService( + nocodb_url=os.environ["NOCODB_BASE_URL"], + token=os.environ["NOCODB_TOKEN"], + base_id=os.environ["NOCODB_BASE_ID"], + ) + + model_map = { + "jina": JINA_MODEL_ID, + "jina-small": JINA_SMALL_MODEL_ID, + "bidir": BIDIR_MODEL_ID, + "arctic": ARCTIC_EMBED_MODEL_ID, + } + model_id = model_map[args.model] + is_jina = args.model in ("jina", "jina-small") + + if is_jina and args.strategy == "full": + print("Note: jina models use encode() which blocks gradient flow — 'full' is equivalent to 'head_only'. Running head_only.") + args.strategy = "head_only" + + device = get_device_for_model(model_id) + print(f"Device: {device}") + + CHECKPOINTS_DIR.mkdir(parents=True, exist_ok=True) + run_finetune(model_id, is_jina, args.strategy, args.dataset, nocodb, device) + + +if __name__ == "__main__": + main() diff --git a/backend/balanceteshaters/scripts/ml/04_compare_evaluate.py b/backend/balanceteshaters/scripts/ml/04_compare_evaluate.py new file mode 100644 index 00000000..efba8cae --- /dev/null +++ b/backend/balanceteshaters/scripts/ml/04_compare_evaluate.py @@ -0,0 +1,253 @@ +# ruff: noqa: E402 +""" +Load all checkpoints and print a comparison table. +Also logs all results to NocoDB and highlights A/B delta (real vs augmented). + +SentenceTransformer embedding and fine-tuned inference run in isolated subprocesses +to avoid the OpenMP conflict between jina-nano's EuroBERT (libomp) and LightGBM (libgomp). +""" +import os +os.environ.setdefault("PYTORCH_MPS_HIGH_WATERMARK_RATIO", "0.0") + +import sys +import subprocess +import tempfile +from pathlib import Path + +import numpy as np +import pandas as pd +import sklearn.metrics +from dotenv import load_dotenv + +SCRIPTS_DIR = Path(__file__).resolve().parent.parent.parent.parent +if str(SCRIPTS_DIR) not in sys.path: + sys.path.insert(0, str(SCRIPTS_DIR)) + +from balanceteshaters.scripts.ml.config import ( + ANNOTATION_TABLE_ID, + ARCTIC_EMBED_MODEL_ID, + BIDIR_MODEL_ID, + CHECKPOINTS_DIR, + DATA_DIR, + EVAL_TABLE_ID, + JINA_MODEL_ID, + JINA_SMALL_MODEL_ID, + MODEL_TYPE, + XLMR_TOXICITY_MODEL_ID, + get_device_for_model, + model_slug, +) +from balanceteshaters.services.nocodb import NocoDBService + +_HELPER = Path(__file__).parent / "_eval_subprocess.py" + + +def metrics(y_true, y_pred) -> dict: + return { + "f1": sklearn.metrics.f1_score(y_true, y_pred, zero_division=0), + "precision": sklearn.metrics.precision_score(y_true, y_pred, zero_division=0), + "recall": sklearn.metrics.recall_score(y_true, y_pred, zero_division=0), + "accuracy": sklearn.metrics.accuracy_score(y_true, y_pred), + } + + +def load_llm_baseline(data_dir: Path) -> dict | None: + csv_dir = data_dir.parent + csvs = list(csv_dir.glob("predictions_m5t7qqaer2oa441_*.csv")) + if not csvs: + return None + + best = None + best_f1 = -1.0 + for p in csvs: + df = pd.read_csv(p) + if "annotated_category" not in df.columns or "predicted_category" not in df.columns: + continue + df = df[df["annotated_category"].notna()] + df["predicted_category"] = df["predicted_category"].astype(str).str.strip() + df = df[df["predicted_category"].isin(["0", "1"])] + if df.empty: + continue + y_true = (~df["annotated_category"].str.contains("Absence de cyberharcèlement")).astype(int) + y_pred = df["predicted_category"].astype(int) + f1 = sklearn.metrics.f1_score(y_true, y_pred, zero_division=0) + if f1 > best_f1: + best_f1 = f1 + best = {"run_name": f"LLM baseline ({p.stem})", "approach": "LLM prompt", "model": "best LLM", "dataset": "real", **metrics(y_true, y_pred)} + return best + + +def _subprocess_run(args: list[str]): + result = subprocess.run([sys.executable] + args, capture_output=True, text=True) + if result.returncode != 0: + raise RuntimeError(f"Subprocess failed (exit {result.returncode}):\n{result.stderr[-2000:]}") + + +def embed_in_subprocess(model_id: str, output_npy: str): + print(f" Embedding {model_slug(model_id)}...") + _subprocess_run([str(_HELPER), "embed", model_id, output_npy]) + + +def predict_classical_in_subprocess(embedding_npy: str, ckpt_path: str, output_npy: str): + _subprocess_run([str(_HELPER), "predict_classical", embedding_npy, ckpt_path, output_npy]) + + +def predict_ft_in_subprocess(model_id: str, ckpt_dir: str, output_npy: str): + _subprocess_run([str(_HELPER), "predict", model_id, ckpt_dir, output_npy]) + + +def predict_xlmr_zero_shot(test_df: pd.DataFrame) -> np.ndarray: + import torch + from transformers import AutoTokenizer, AutoModelForSequenceClassification + device = get_device_for_model(XLMR_TOXICITY_MODEL_ID) + tokenizer = AutoTokenizer.from_pretrained(XLMR_TOXICITY_MODEL_ID) + model = AutoModelForSequenceClassification.from_pretrained(XLMR_TOXICITY_MODEL_ID) + model.eval().to(device) + texts = test_df["comment"].tolist() + all_preds = [] + for i in range(0, len(texts), 32): + batch = texts[i:i + 32] + enc = tokenizer(batch, truncation=True, padding=True, max_length=512, return_tensors="pt").to(device) + with torch.no_grad(): + logits = model(**enc).logits + all_preds.extend(torch.argmax(logits, dim=-1).cpu().numpy()) + return np.array(all_preds) + + +def main(): + load_dotenv() + + test_df = pd.read_parquet(DATA_DIR / "test.parquet") + y_test = test_df["label"].values + + nocodb = None + if all(os.environ.get(k) for k in ["NOCODB_BASE_URL", "NOCODB_TOKEN", "NOCODB_BASE_ID"]): + nocodb = NocoDBService( + nocodb_url=os.environ["NOCODB_BASE_URL"], + token=os.environ["NOCODB_TOKEN"], + base_id=os.environ["NOCODB_BASE_ID"], + ) + + results = [] + tmp = Path(tempfile.mkdtemp()) + + encoder_configs = [ + (JINA_MODEL_ID, True), + (JINA_SMALL_MODEL_ID, True), + (BIDIR_MODEL_ID, False), + (ARCTIC_EMBED_MODEL_ID, False), + ] + + # ── Phase 1: embed test set — each model in isolated subprocess ──────── + # Prevents OpenMP conflict: jina-nano (EuroBERT/libomp) vs LightGBM (libgomp) + print("── Phase 1: computing test embeddings ──") + embedding_cache: dict[str, np.ndarray] = {} + for model_id, _ in encoder_configs: + npy_path = str(tmp / f"X_{model_slug(model_id)}.npy") + embed_in_subprocess(model_id, npy_path) + embedding_cache[model_id] = np.load(npy_path) + + # ── Phase 2: classical ML predictions — each in isolated subprocess ────── + # LightGBM (libgomp) conflicts with leaked OpenMP state from Phase 1 subprocesses + print("\n── Phase 2: classical ML predictions ──") + for model_id, _ in encoder_configs: + slug = model_slug(model_id) + embedding_npy = str(tmp / f"X_{slug}.npy") + + for clf_name in ["logreg", "lightgbm", "mlp"]: + for dataset in ["real", "augmented", "augmented_v2"]: + run_name = f"{slug}+{clf_name}+{dataset}" + ckpt = CHECKPOINTS_DIR / f"{run_name}.joblib" + if not ckpt.exists(): + print(f" [skip] {ckpt.name}") + continue + pred_npy = str(tmp / f"pred_{run_name}.npy") + predict_classical_in_subprocess(embedding_npy, str(ckpt), pred_npy) + y_pred = np.load(pred_npy) + m = metrics(y_test, y_pred) + results.append({"run": run_name, "approach": f"frozen+{clf_name}", "model": slug, "dataset": dataset, "model_type": MODEL_TYPE.get(model_id, "encoder embedding"), **m}) + print(f" {run_name:<55} F1={m['f1']:.4f}") + + # ── Phase 3: fine-tuned model predictions (each in isolated subprocess) ── + print("\n── Phase 3: fine-tuned predictions ──") + for model_id, _ in encoder_configs: + slug = model_slug(model_id) + for strategy in ["head_only", "full"]: + for dataset in ["real", "augmented", "augmented_v2"]: + run_name = f"{slug}-finetuned-{strategy}-{dataset}" + ckpt_dir = CHECKPOINTS_DIR / run_name + if not ckpt_dir.exists(): + print(f" [skip] {run_name}") + continue + npy_path = str(tmp / f"pred_{run_name}.npy") + print(f" Predicting {run_name}...") + predict_ft_in_subprocess(model_id, str(ckpt_dir), npy_path) + y_pred = np.load(npy_path) + if len(y_pred) == 1 and y_pred[0] == -1: + print(f" [skip] no best_model.pt in {run_name}") + continue + m = metrics(y_test, y_pred) + results.append({"run": run_name, "approach": f"finetune-{strategy}", "model": slug, "dataset": dataset, "model_type": MODEL_TYPE.get(model_id, "encoder embedding"), **m}) + print(f" {run_name:<55} F1={m['f1']:.4f}") + + # ── XLM-R zero-shot (no LightGBM conflict — transformers only) ──────── + print("\nRunning XLM-R toxicity zero-shot...") + xlmr_slug = model_slug(XLMR_TOXICITY_MODEL_ID) + y_pred = predict_xlmr_zero_shot(test_df) + m = metrics(y_test, y_pred) + results.append({"run": f"{xlmr_slug}-zero-shot", "approach": "zero-shot", "model": xlmr_slug, "dataset": "real", "model_type": "encoder classifier", **m}) + print(f" XLM-R zero-shot F1={m['f1']:.4f}") + + # ── LLM baseline ────────────────────────────────────────────────────── + baseline = load_llm_baseline(DATA_DIR) + if baseline: + run_name = baseline.pop("run_name") + results.append({"run": run_name, "model_type": "generative", **baseline}) + + if not results: + print("No results found. Run the training scripts first.") + return + + df = pd.DataFrame(results) + df = df.sort_values(["approach", "model", "dataset"]) + + print("\n" + "="*90) + print(f"{'Run':<55} {'Dataset':<12} {'F1':>6} {'P':>6} {'R':>6} {'Acc':>6}") + print("="*90) + for _, row in df.iterrows(): + print(f"{row['run']:<55} {row['dataset']:<12} {row['f1']:>6.4f} {row['precision']:>6.4f} {row['recall']:>6.4f} {row['accuracy']:>6.4f}") + + print("\n── A/B delta (augmented − real F1) ─────────────") + for (approach, model), group in df.groupby(["approach", "model"]): + real_row = group[group["dataset"] == "real"] + aug_row = group[group["dataset"] == "augmented"] + if real_row.empty or aug_row.empty: + continue + delta = aug_row["f1"].values[0] - real_row["f1"].values[0] + print(f" {approach:<25} {model:<35} {delta:+.4f}") + + if nocodb: + for _, row in df.iterrows(): + data = { + "model_name": row["run"], + "table_id": ANNOTATION_TABLE_ID, + "table_name": f"finetune/{row.get('dataset','?')}", + "f1": row["f1"], + "precision": row["precision"], + "recall": row["recall"], + "accuracy": row["accuracy"], + "total_samples": len(y_test), + "positive_samples": int(y_test.sum()), + "negative_samples": len(y_test) - int(y_test.sum()), + "prompt": f"approach={row.get('approach','?')} dataset={row.get('dataset','?')}", + "model_type": row.get("model_type", "encoder embedding"), + } + try: + nocodb.create_record(EVAL_TABLE_ID, data) + except Exception as e: + print(f" [warn] NocoDB: {e}") + print("\nAll results logged to NocoDB.") + + +if __name__ == "__main__": + main() diff --git a/backend/balanceteshaters/scripts/ml/05_claude_annotate.py b/backend/balanceteshaters/scripts/ml/05_claude_annotate.py new file mode 100644 index 00000000..c77ce713 --- /dev/null +++ b/backend/balanceteshaters/scripts/ml/05_claude_annotate.py @@ -0,0 +1,286 @@ +# ruff: noqa: E402 +""" +Auto-annotate unannotated comments using Claude API, with agreement evaluation. + +Modes: + evaluate — Run Claude on a subsample of manually-annotated rows, measure agreement + against human labels to build confidence before using for training. + annotate — Fetch unannotated rows, classify with Claude, save to parquet. + +Usage: + python 05_claude_annotate.py --mode evaluate --n 100 + python 05_claude_annotate.py --mode annotate --limit 500 --dry-run + python 05_claude_annotate.py --mode annotate --limit 500 +""" +import argparse +import json +import os +import random +import re +import sys +from pathlib import Path + +import anthropic +import pandas as pd +from dotenv import load_dotenv + +SCRIPTS_DIR = Path(__file__).resolve().parent.parent.parent.parent +if str(SCRIPTS_DIR) not in sys.path: + sys.path.insert(0, str(SCRIPTS_DIR)) + +from balanceteshaters.scripts.ml.config import ANNOTATION_TABLE_ID, DATA_DIR +from balanceteshaters.services.annotation import AnnotatedCategory, AnnotationService +from balanceteshaters.services.nocodb import NocoDBService + +# Sonnet pricing per million tokens +SONNET_INPUT_PRICE = 3.00 +SONNET_OUTPUT_PRICE = 15.00 +SONNET_CACHE_WRITE_PRICE = 3.75 +SONNET_CACHE_READ_PRICE = 0.30 + +CATEGORIES = [c.value for c in AnnotatedCategory] +HARASSMENT_CATEGORIES = [c for c in CATEGORIES if c != "Absence de cyberharcèlement"] + +SYSTEM_PROMPT = f"""Tu es un expert en modération de contenu et en sécurité en ligne. Tu analyses des commentaires de réseaux sociaux français (Instagram, TikTok, YouTube, Twitter) pour détecter le cyberharcèlement. + +Pour chaque commentaire, tu dois choisir la catégorie la plus appropriée parmi : +{chr(10).join(f'- {c}' for c in CATEGORIES)} + +Définitions : +- Absence de cyberharcèlement : commentaire anodin, critique constructive, humour bienveillant, opinion sans agressivité +- Cyberharcèlement (définition générale) : harcèlement en ligne ne rentrant pas dans une catégorie spécifique +- Cyberharcèlement (autre) : forme de harcèlement non couverte par les autres catégories +- Cyberharcèlement à caractère sexuel : harcèlement sexuel, slut-shaming, commentaires dégradants sur le corps/sexualité +- Menaces : menaces directes ou voilées de violence physique, intimidation +- Incitation au suicide : encouragement à se suicider ou se blesser +- Injure : insulte directe, terme offensant, dénigrement +- Diffamation : fausses accusations destinées à nuire à la réputation +- Injure et diffamation publique : combinaison d'injure et diffamation +- Doxxing : publication ou menace de publication d'informations privées +- Incitation à la haine : appel à la haine envers un groupe (racisme, homophobie, etc.) +- Suspect : commentaire ambigu nécessitant une vérification humaine + +Points importants : +- L'ironie, le sarcasme et les emojis péjoratifs peuvent constituer du cyberharcèlement même sans insulte directe +- Un commentaire qui semble superficiellement bénin peut être du harcèlement selon le contexte +- Si tu n'es pas certain, utilise "Suspect" + +Réponds UNIQUEMENT avec un objet JSON valide, sans texte avant ou après : +{{"category": "", "binary_label": <0 ou 1>, "confidence": "", "reasoning": ""}} + +binary_label : 0 = Absence de cyberharcèlement, 1 = toute forme de cyberharcèlement""" + + +def classify_comment(client: anthropic.Anthropic, comment: str, tokens_used: dict) -> dict | None: + try: + response = client.messages.create( + model="claude-sonnet-4-6", + max_tokens=200, + system=[{"type": "text", "text": SYSTEM_PROMPT, "cache_control": {"type": "ephemeral"}}], + messages=[{"role": "user", "content": f"Commentaire : {comment}"}], + ) + tokens_used["input"] += response.usage.input_tokens + tokens_used["output"] += response.usage.output_tokens + if hasattr(response.usage, "cache_read_input_tokens"): + tokens_used["cache_read"] += response.usage.cache_read_input_tokens + if hasattr(response.usage, "cache_creation_input_tokens"): + tokens_used["cache_write"] += response.usage.cache_creation_input_tokens + + text = response.content[0].text.strip() + # Strip markdown code blocks if present + text = re.sub(r"^```(?:json)?\s*|\s*```$", "", text, flags=re.MULTILINE).strip() + result = json.loads(text) + + # Validate category + if result.get("category") not in CATEGORIES: + result["category"] = "Suspect" + result["binary_label"] = 1 + return result + + except Exception as e: + print(f" [warn] Classification failed: {e}") + return None + + +def compute_cost(tokens_used: dict) -> float: + return ( + tokens_used["input"] * SONNET_INPUT_PRICE + + tokens_used["output"] * SONNET_OUTPUT_PRICE + + tokens_used.get("cache_write", 0) * SONNET_CACHE_WRITE_PRICE + + tokens_used.get("cache_read", 0) * SONNET_CACHE_READ_PRICE + ) / 1_000_000 + + +def mode_evaluate(service: AnnotationService, client: anthropic.Anthropic, n: int): + """Sample n manually-annotated rows, run Claude blind, measure agreement.""" + import sklearn.metrics + + print("Fetching annotated records...") + all_annotated = service.fetch_records_paginated() + all_annotated = [a for a in all_annotated if a.annotated_category] + print(f" Found {len(all_annotated)} annotated records") + + sample = random.sample(all_annotated, min(n, len(all_annotated))) + print(f" Evaluating on {len(sample)} randomly sampled records\n") + + tokens_used = {"input": 0, "output": 0, "cache_read": 0, "cache_write": 0} + rows = [] + + for i, ann in enumerate(sample): + result = classify_comment(client, ann.comment, tokens_used) + if result is None: + continue + + # Human binary label + cats = [c.value for c in ann.annotated_category] + human_binary = 0 if any("Absence de cyberharcèlement" in c for c in cats) else 1 + claude_binary = result.get("binary_label", 1) + + rows.append({ + "id": ann.id, + "comment": ann.comment[:120], + "human_category": cats[0] if cats else "?", + "human_binary": human_binary, + "claude_category": result.get("category"), + "claude_binary": claude_binary, + "claude_confidence": result.get("confidence"), + "claude_reasoning": result.get("reasoning", ""), + "agree": human_binary == claude_binary, + }) + + if (i + 1) % 10 == 0: + cost_so_far = compute_cost(tokens_used) + print(f" {i+1}/{len(sample)} cost so far: ${cost_so_far:.3f}") + + df = pd.DataFrame(rows) + y_true = df["human_binary"].values + y_pred = df["claude_binary"].values + + acc = sklearn.metrics.accuracy_score(y_true, y_pred) + f1 = sklearn.metrics.f1_score(y_true, y_pred, zero_division=0) + prec = sklearn.metrics.precision_score(y_true, y_pred, zero_division=0) + rec = sklearn.metrics.recall_score(y_true, y_pred, zero_division=0) + kappa = sklearn.metrics.cohen_kappa_score(y_true, y_pred) + + print(f"\n{'='*60}") + print(f"Agreement metrics (Claude vs human, n={len(df)})") + print(f"{'='*60}") + print(f" Accuracy : {acc:.4f}") + print(f" F1 : {f1:.4f} (P={prec:.4f} R={rec:.4f})") + print(f" Kappa : {kappa:.4f} {'(substantial)' if kappa > 0.6 else '(moderate)' if kappa > 0.4 else '(fair)'}") + + print("\nConfusion matrix (rows=human, cols=claude):") + cm = sklearn.metrics.confusion_matrix(y_true, y_pred) + print(" Claude=0 Claude=1") + print(f" Human=0 {cm[0,0]:5} {cm[0,1]:5}") + print(f" Human=1 {cm[1,0]:5} {cm[1,1]:5}") + + disagree_df = df[~df["agree"]].head(20) + if not disagree_df.empty: + print(f"\nDisagreements (first {len(disagree_df)}):") + print(f"{'Comment':<60} {'Human':>6} {'Claude':>6} {'Conf':<6} Reasoning") + print("-" * 120) + for _, row in disagree_df.iterrows(): + print(f"{row['comment'][:58]:<60} {row['human_binary']:>6} {row['claude_binary']:>6} {row['claude_confidence']:<6} {row['claude_reasoning'][:60]}") + + total_cost = compute_cost(tokens_used) + print(f"\nAPI cost: ${total_cost:.4f} (tokens in={tokens_used['input']}, out={tokens_used['output']}, cache_read={tokens_used['cache_read']})") + + out_path = DATA_DIR / "claude_evaluate_agreement.parquet" + df.to_parquet(out_path, index=False) + print(f"Full results saved to {out_path}") + + +def mode_annotate(service: AnnotationService, client: anthropic.Anthropic, limit: int, dry_run: bool): + """Fetch unannotated rows, classify with Claude, save to parquet.""" + print("Fetching all records...") + all_records = service.fetch_records_paginated() + unannotated = [a for a in all_records if not a.annotated_category] + print(f" Total records: {len(all_records)}") + print(f" Unannotated: {len(unannotated)}") + + to_annotate = unannotated[:limit] + print(f" Will annotate: {len(to_annotate)}") + + # Cost estimate: ~400 tokens system (cached after first) + ~20 tokens per comment + n_calls = len(to_annotate) + est_input = 400 + 20 * n_calls # first call full, rest cache hits + est_output = 60 * n_calls + est_cost = (est_input * SONNET_INPUT_PRICE + est_output * SONNET_OUTPUT_PRICE + 400 * SONNET_CACHE_WRITE_PRICE) / 1_000_000 + print(f" Estimated cost: ~${est_cost:.3f}") + + if dry_run: + print("\n[dry-run] No API calls made.") + return + + tokens_used = {"input": 0, "output": 0, "cache_read": 0, "cache_write": 0} + rows = [] + + for i, ann in enumerate(to_annotate): + result = classify_comment(client, ann.comment, tokens_used) + if result is None: + continue + + rows.append({ + "id": ann.id, + "comment": ann.comment, + "claude_category": result.get("category"), + "claude_binary_label": result.get("binary_label", 1), + "claude_confidence": result.get("confidence"), + "claude_reasoning": result.get("reasoning", ""), + "label": result.get("binary_label", 1), + "annotated_category": result.get("category"), + "binary_confidence": None, + "source": "claude_annotated", + }) + + if (i + 1) % 25 == 0: + print(f" {i+1}/{len(to_annotate)} cost: ${compute_cost(tokens_used):.3f}") + + df = pd.DataFrame(rows) + out_path = DATA_DIR / "claude_annotated.parquet" + df.to_parquet(out_path, index=False) + + total_cost = compute_cost(tokens_used) + label_dist = df["label"].value_counts().to_dict() + print("\n=== Done ===") + print(f" Annotated: {len(df)} comments") + print(f" Label distribution: {label_dist}") + print(f" API cost: ${total_cost:.4f}") + print(f" Saved to {out_path}") + print(f"\nNext: review {out_path.name}, then run 00_prepare_dataset.py to rebuild train splits.") + + +def main(): + parser = argparse.ArgumentParser(description="Claude-based annotation and agreement evaluation") + parser.add_argument("--mode", choices=["evaluate", "annotate"], required=True) + parser.add_argument("--n", type=int, default=100, help="[evaluate] Number of annotated rows to sample") + parser.add_argument("--limit", type=int, default=500, help="[annotate] Max unannotated rows to process") + parser.add_argument("--dry-run", action="store_true", help="[annotate] Show cost estimate only") + parser.add_argument("--seed", type=int, default=42) + args = parser.parse_args() + + random.seed(args.seed) + load_dotenv() + + api_key = os.environ.get("ANTHROPIC_API_KEY") + if not api_key: + print("ERROR: ANTHROPIC_API_KEY not set.") + sys.exit(1) + + nocodb = NocoDBService( + nocodb_url=os.environ["NOCODB_BASE_URL"], + token=os.environ["NOCODB_TOKEN"], + base_id=os.environ["NOCODB_BASE_ID"], + ) + service = AnnotationService(nocodb=nocodb, annotation_table_id=ANNOTATION_TABLE_ID) + client = anthropic.Anthropic(api_key=api_key) + + if args.mode == "evaluate": + mode_evaluate(service, client, args.n) + else: + mode_annotate(service, client, args.limit, args.dry_run) + + +if __name__ == "__main__": + main() diff --git a/backend/balanceteshaters/scripts/ml/README.md b/backend/balanceteshaters/scripts/ml/README.md new file mode 100644 index 00000000..55c0210c --- /dev/null +++ b/backend/balanceteshaters/scripts/ml/README.md @@ -0,0 +1,369 @@ +# Pipeline ML : détection de cyberharcèlement par embeddings + +Pipeline complet pour entraîner et évaluer des modèles d'embeddings sur la classification binaire de commentaires français issus des réseaux sociaux (Instagram, TikTok, YouTube, Twitter). + +## Vue d'ensemble + +Le pipeline est organisé en scripts numérotés à exécuter dans l'ordre : + +``` +00_prepare_dataset.py → construire les splits train/val/test depuis NocoDB +01_generate_synthetic.py → augmenter les catégories minoritaires avec Claude +02_embed_and_train_classical.py → classifieurs classiques sur embeddings gelés +03_finetune_embedding.py → fine-tuning bout-en-bout encodeur + tête +04_compare_evaluate.py → comparer tous les runs, logger dans NocoDB +05_claude_annotate.py → annoter les données non étiquetées avec Claude +``` + +Tous les résultats sont enregistrés dans NocoDB pour le suivi et la comparaison. + +--- + +## Installation (MacBook M4 Pro) + +### Prérequis + +- **macOS Sequoia** (ou supérieur recommandé) +- **Python 3.12+** — installez via [pyenv](https://github.com/pyenv/pyenv) ou [mise](https://mise.jdx.dev/) +- **uv** — gestionnaire de packages rapide + +```bash +# Installer uv si pas déjà fait +curl -LsSf https://astral.sh/uv/install.sh | sh +``` + +### Cloner et installer les dépendances + +```bash +git clone +cd 14_BalanceTesHaters/backend +uv sync +``` + +`uv sync` crée un environnement virtuel dans `.venv/` et installe toutes les dépendances définies dans `pyproject.toml`, y compris PyTorch, sentence-transformers et LightGBM. + +### Variables d'environnement + +Créez un fichier `.env` à la racine du dossier `backend/` : + +```bash +NOCODB_BASE_URL=https://votre-nocodb.example.com +NOCODB_TOKEN=votre_token +NOCODB_BASE_ID=votre_base_id +ANTHROPIC_API_KEY=votre_clé # facultatif — requis seulement pour 01_generate_synthetic.py et 05_claude_annotate.py +``` + +Les scripts `00`, `02`, `03` et `04` n'utilisent pas l'API Anthropic et fonctionnent sans cette clé. Les scripts appellent `load_dotenv()` automatiquement. + +### Notes spécifiques Apple Silicon (M4 Pro) + +Le chip M4 Pro dispose d'un GPU unifié (MPS) utilisé par PyTorch. Quelques points importants : + +- **jina-v5-text-nano** (`jina`) provoque un **segfault sur MPS** à cause d'EuroBERT. Il est automatiquement forcé sur CPU — pas d'action requise. +- **jina-v5-text-small**, BidirLM et Arctic sont tous **compatibles MPS** et utiliseront le GPU automatiquement. +- La variable d'environnement `PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0` est positionnée en tête de chaque script pour éviter les erreurs de mémoire unifiée. +- **LightGBM** utilise `libgomp` (OpenMP GNU) tandis que certains modèles HuggingFace utilisent `libomp` (OpenMP LLVM). Charger les deux dans le même processus provoque un crash. Les scripts gèrent cela via des sous-processus isolés — aucune configuration nécessaire de votre côté. + +### Vérifier l'installation + +```bash +cd backend +uv run python -c "import torch; print('MPS disponible:', torch.backends.mps.is_available())" +# → MPS disponible: True +``` + +--- + +## Démarrage rapide + +```bash +# 1. Construire les splits +uv run python balanceteshaters/scripts/ml/00_prepare_dataset.py + +# 2. Générer des données synthétiques (vérifier le coût d'abord) +uv run python balanceteshaters/scripts/ml/01_generate_synthetic.py --dry-run +uv run python balanceteshaters/scripts/ml/01_generate_synthetic.py + +# 3. Entraîner les baselines sur embeddings gelés (tous modèles × classifieurs × datasets) +uv run python balanceteshaters/scripts/ml/02_embed_and_train_classical.py + +# 4. Fine-tuner (un run à la fois) +uv run python balanceteshaters/scripts/ml/03_finetune_embedding.py --model arctic --strategy head_only --dataset real +uv run python balanceteshaters/scripts/ml/03_finetune_embedding.py --model arctic --strategy head_only --dataset augmented_v2 + +# 5. Comparer tout +uv run python balanceteshaters/scripts/ml/04_compare_evaluate.py +``` + +--- + +## Scripts + +### 00_prepare_dataset.py — Construire les splits + +Récupère les enregistrements annotés depuis NocoDB et crée des splits stratifiés 70/15/15. + +```bash +uv run python balanceteshaters/scripts/ml/00_prepare_dataset.py [--high-confidence-only] +``` + +**Options :** +- `--high-confidence-only` — ne garder que les annotations marquées `HIGH_CONFIDENCE` (réduit la taille du dataset, améliore la qualité des labels) + +**Sorties** (dans `data/finetune/`) : +- `train_real.parquet` +- `val.parquet` +- `test.parquet` + +À ré-exécuter à chaque fois que les annotations changent. + +--- + +### 01_generate_synthetic.py — Augmenter les catégories minoritaires + +Utilise Claude pour générer des commentaires de cyberharcèlement synthétiques réalistes pour les catégories sous-représentées (doxxing, incitation au suicide, harcèlement sexuel, menaces, incitation à la haine). Chaque prompt inclut des exemples réels du training set comme ancrage few-shot. + +```bash +# Voir l'allocation et estimer le coût +uv run python balanceteshaters/scripts/ml/01_generate_synthetic.py --dry-run + +# Générer avec les paramètres par défaut (1000 exemples, Sonnet) +uv run python balanceteshaters/scripts/ml/01_generate_synthetic.py + +# Modèle moins cher ou nombre d'exemples réduit +uv run python balanceteshaters/scripts/ml/01_generate_synthetic.py --model claude-haiku-4-5-20251001 --total 500 +``` + +**Sorties :** +- `data/finetune/synthetic_v2.parquet` — exemples générés +- `data/finetune/train_augmented_v2.parquet` — `train_real` + synthétique (à utiliser avec `--dataset augmented_v2` dans les scripts suivants) + +**Coût typique :** ~0,10 $ pour 1000 exemples avec Sonnet. + +--- + +### 02_embed_and_train_classical.py — Baselines sur embeddings gelés + +Encode le texte avec chaque modèle puis entraîne trois classifieurs sur les représentations gelées. Couvre toutes les combinaisons encodeur × classifieux × dataset. + +```bash +# Tout (par défaut) +uv run python balanceteshaters/scripts/ml/02_embed_and_train_classical.py + +# Sous-ensemble +uv run python balanceteshaters/scripts/ml/02_embed_and_train_classical.py \ + --models arctic jina-small \ + --datasets real augmented_v2 +``` + +**Options :** +- `--models` — un ou plusieurs parmi `jina`, `jina-small`, `bidir`, `arctic`, `all` (défaut : `all`) +- `--datasets` — un ou plusieurs parmi `real`, `augmented`, `augmented_v2`, `all` (défaut : `all`) + +**Classifieurs entraînés :** + +| Nom | Architecture | +|---|---| +| `logreg` | Régression logistique (baseline linéaire) | +| `lightgbm` | LightGBM avec early stopping | +| `mlp` | MLP 512→128, ReLU, early stopping | + +**Sorties :** `data/finetune/checkpoints/{slug}+{clf}+{dataset}.joblib` + +--- + +### 03_finetune_embedding.py — Fine-tuning bout-en-bout + +Attache une tête de classification linéaire à un encodeur et entraîne avec AdamW. Deux stratégies disponibles : + +- `head_only` — encodeur gelé, seule la tête apprend (rapide, ~50 époques) +- `full` — encodeur + tête entraînés conjointement avec un faible LR (lent, ~15 époques) + +```bash +# Arctic head-only sur données réelles +uv run python balanceteshaters/scripts/ml/03_finetune_embedding.py \ + --model arctic --strategy head_only --dataset real + +# Head-only sur données augmentées +uv run python balanceteshaters/scripts/ml/03_finetune_embedding.py \ + --model arctic --strategy head_only --dataset augmented_v2 +``` + +**Options :** +- `--model` — `jina`, `jina-small`, `bidir`, `arctic` (requis) +- `--strategy` — `head_only` ou `full` (défaut : `full`) +- `--dataset` — `real`, `augmented`, `augmented_v2` (défaut : `real`) + +> Les modèles jina utilisent `encode()` qui bloque le flux de gradient, donc `full` bascule automatiquement en `head_only` pour eux. + +**Sortie :** `data/finetune/checkpoints/{slug}-finetuned-{strategy}-{dataset}/best_model.pt` + +L'entraînement sauvegarde le checkpoint avec le meilleur F1 de validation et applique l'early stopping. + +--- + +### 04_compare_evaluate.py — Table de comparaison complète + +Charge tous les checkpoints des scripts 02 et 03, évalue sur le test set, affiche une table de comparaison et logue tous les résultats dans NocoDB. Inclut également : +- Baseline zero-shot XLM-R toxicité +- Meilleure baseline LLM depuis les CSV de prédictions (si présents) +- Delta A/B montrant le gain F1 des données augmentées vs réelles + +```bash +uv run python balanceteshaters/scripts/ml/04_compare_evaluate.py +``` + +Les embeddings et prédictions tournent dans des sous-processus isolés pour éviter le conflit OpenMP entre EuroBERT de jina-nano (libomp) et LightGBM (libgomp). + +**Meilleurs résultats observés (frozen embeddings, test set) :** +``` +Run F1 P R Acc +================================================================================ +snowflake-arctic-embed-l-v2.0+mlp+real 0.6916 0.6852 0.6981 0.7130 +snowflake-arctic-embed-l-v2.0+logreg+real 0.6903 0.6500 0.7358 0.6957 +harrier-oss-v1-270m+lightgbm+real 0.6729 0.6667 0.6792 0.6957 +jina-embeddings-v5-text-nano+lightgbm+augmented 0.6573 0.5222 0.8868 0.5739 +jina-embeddings-v5-text-small+mlp+real 0.6195 0.5833 0.6604 0.6261 +``` + +--- + +### 05_claude_annotate.py — Annotation avec Claude + +Deux modes : +- **evaluate** — fait tourner Claude sur un échantillon de lignes déjà annotées et mesure l'accord avec les labels humains (accuracy, F1, kappa de Cohen). À utiliser en premier pour valider la fiabilité. +- **annotate** — classe les lignes non annotées avec Claude et sauvegarde en parquet pour relecture. + +```bash +# Mesurer l'accord Claude vs humain sur 100 exemples +uv run python balanceteshaters/scripts/ml/05_claude_annotate.py --mode evaluate --n 100 + +# Estimer le coût +uv run python balanceteshaters/scripts/ml/05_claude_annotate.py --mode annotate --limit 500 --dry-run + +# Annoter +uv run python balanceteshaters/scripts/ml/05_claude_annotate.py --mode annotate --limit 500 +``` + +**Options :** +- `--mode` — `evaluate` ou `annotate` (requis) +- `--n` — nombre de lignes annotées à échantillonner pour l'évaluation (défaut : 100) +- `--limit` — nombre max de lignes non annotées à traiter (défaut : 500) +- `--dry-run` — afficher uniquement l'estimation de coût, sans appels API +- `--seed` — graine aléatoire pour l'échantillonnage (défaut : 42) + +**Sortie :** `data/finetune/claude_annotated.parquet` + +Après relecture, ré-exécuter `00_prepare_dataset.py` pour inclure les lignes annotées par Claude dans les splits. + +--- + +## Modèles + +| Alias | ID HuggingFace | Params | Notes | +|---|---|---|---| +| `jina` | `jinaai/jina-embeddings-v5-text-nano` | 239M | Basé sur EuroBERT ; CPU uniquement sur Apple Silicon (segfault MPS) | +| `jina-small` | `jinaai/jina-embeddings-v5-text-small` | 677M | Basé sur Qwen3 ; compatible MPS | +| `bidir` | `microsoft/harrier-oss-v1-270m` | 270M | 94 langues, 640 dimensions | +| `arctic` | `Snowflake/snowflake-arctic-embed-l-v2.0` | 568M | 1024 dimensions, MRL, forte baseline retrieval | +| *(zero-shot)* | `textdetox/xlmr-large-toxicity-classifier-v2` | 600M | XLM-R fine-tuné sur la toxicité multilingue ; aucun entraînement requis | + +--- + +## Scripts utilitaires + +### _threshold_sweep.py + +Balaye les seuils de décision [0.20 … 0.50] pour les modèles classiques et fine-tunés Arctic. Logue dans NocoDB tout run dont le meilleur seuil diffère de 0.50. + +```bash +uv run python balanceteshaters/scripts/ml/_threshold_sweep.py +``` + +Nécessite que les checkpoints de `02_embed_and_train_classical.py` existent. + +--- + +### _mlp_arch_search.py + +Recherche de grille sur les configurations de couches cachées du MLP sur les embeddings Arctic gelés. Teste 8 architectures avec optimisation du seuil et logue la meilleure dans NocoDB. + +```bash +uv run python balanceteshaters/scripts/ml/_mlp_arch_search.py +``` + +--- + +### dedup_eval_table.py + +Supprime les entrées en doublon dans la table d'évaluation NocoDB, en gardant l'enregistrement le plus récent par `model_name`. Peut être exécuté plusieurs fois sans risque. + +```bash +uv run python balanceteshaters/scripts/ml/dedup_eval_table.py +``` + +--- + +### _eval_subprocess.py + +Helper interne utilisé par `04_compare_evaluate.py`, `_threshold_sweep.py` et `_mlp_arch_search.py`. Chaque commande tourne dans son propre sous-processus pour éviter les conflits OpenMP entre torch/sentence-transformers (libomp) et LightGBM (libgomp). + +Commandes disponibles : +``` +embed [split] +predict_classical +predict_proba_classical +predict +predict_proba +``` + +`split` vaut `"test"` par défaut. Passer `"train_real"` ou `"val"` pour les autres splits. + +--- + +## Structure des données + +``` +backend/balanceteshaters/data/finetune/ +├── train_real.parquet # ~70 % des données annotées +├── train_augmented.parquet # train_real + synthétique v1 +├── train_augmented_v2.parquet # train_real + synthétique v2 (recommandé) +├── val.parquet # ~15 %, utilisé pour l'early stopping +├── test.parquet # ~15 %, réservé à l'évaluation finale +├── synthetic_v2.parquet # exemples générés par Claude +├── claude_annotated.parquet # lignes annotées par Claude (après 05) +└── checkpoints/ + ├── {slug}+{clf}+{dataset}.joblib # classifieurs sur embeddings gelés + └── {slug}-finetuned-{strategy}-{dataset}/ + └── best_model.pt # meilleur checkpoint (val F1) +``` + +Tous les fichiers parquet partagent le même schéma : + +| Colonne | Type | Description | +|---|---|---| +| `id` | str/None | Identifiant NocoDB | +| `comment` | str | Texte brut du commentaire | +| `label` | int | 0 = bénin, 1 = cyberharcèlement | +| `annotated_category` | str | Noms de catégories séparés par des virgules | +| `binary_confidence` | str/None | `HIGH_CONFIDENCE` ou None | +| `source` | str | `real`, `synthetic_v2` ou `claude_annotated` | + +--- + +## Configuration (config.py) + +Module central avec les identifiants de modèles, chemins et utilitaires. + +| Symbole | Description | +|---|---| +| `JINA_MODEL_ID` / `JINA_SMALL_MODEL_ID` | IDs des modèles jina | +| `ARCTIC_EMBED_MODEL_ID` | ID du modèle Snowflake Arctic | +| `XLMR_TOXICITY_MODEL_ID` | Classifieurs XLM-R toxicité (zero-shot) | +| `DATA_DIR` | Chemin vers `data/finetune/` | +| `CHECKPOINTS_DIR` | Chemin vers `data/finetune/checkpoints/` | +| `ANNOTATION_TABLE_ID` | Table NocoDB des annotations brutes | +| `EVAL_TABLE_ID` | Table NocoDB des résultats d'évaluation | +| `get_device_for_model(model_id)` | Retourne `"cpu"` pour jina-nano (MPS non sûr), sinon le meilleur device disponible | +| `model_slug(model_id)` | Extrait le nom court d'un ID HuggingFace (ex. `"snowflake-arctic-embed-l-v2.0"`) | +| `compute_binary_label(categories)` | Convertit une liste de catégories en label binaire 0/1 | diff --git a/backend/balanceteshaters/scripts/ml/__init__.py b/backend/balanceteshaters/scripts/ml/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/backend/balanceteshaters/scripts/ml/_eval_subprocess.py b/backend/balanceteshaters/scripts/ml/_eval_subprocess.py new file mode 100644 index 00000000..bf39edb1 --- /dev/null +++ b/backend/balanceteshaters/scripts/ml/_eval_subprocess.py @@ -0,0 +1,144 @@ +""" +Subprocess helper for 04_compare_evaluate.py and threshold/arch sweep scripts. +Each command uses lazy imports so unrelated native libraries are never loaded together. + +Usage: + python _eval_subprocess.py embed [split] + python _eval_subprocess.py predict_classical + python _eval_subprocess.py predict_proba_classical + python _eval_subprocess.py predict + python _eval_subprocess.py predict_proba + +[split] defaults to "test". Pass "train_real" or "val" to embed other splits. +""" +import os +os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0" + +import sys +import numpy as np +from pathlib import Path + +SCRIPTS_DIR = Path(__file__).resolve().parent.parent.parent.parent +if str(SCRIPTS_DIR) not in sys.path: + sys.path.insert(0, str(SCRIPTS_DIR)) + + +def cmd_embed(model_id: str, output_npy: str, split: str = "test"): + import pandas as pd + from sentence_transformers import SentenceTransformer + from balanceteshaters.scripts.ml.config import DATA_DIR, get_device_for_model + + df = pd.read_parquet(DATA_DIR / f"{split}.parquet") + is_jina = "jinaai/" in model_id + load_kwargs = {"device": get_device_for_model(model_id)} + if is_jina: + load_kwargs["trust_remote_code"] = True + + encoder = SentenceTransformer(model_id, **load_kwargs) + task = "classification" if is_jina else None + encode_kwargs = {"batch_size": 32, "show_progress_bar": False, "convert_to_numpy": True} + if task: + encode_kwargs["task"] = task + + X = encoder.encode(df["comment"].tolist(), **encode_kwargs).astype(np.float32) + np.save(output_npy, X) + + +def cmd_predict_classical(embedding_npy: str, ckpt_path: str, output_npy: str): + import joblib # imports LightGBM on first call — no torch/ST in this process + X = np.load(embedding_npy) + clf = joblib.load(ckpt_path) + np.save(output_npy, clf.predict(X)) + + +def cmd_predict_proba_classical(embedding_npy: str, ckpt_path: str, output_npy: str): + import joblib + X = np.load(embedding_npy) + clf = joblib.load(ckpt_path) + np.save(output_npy, clf.predict_proba(X)[:, 1]) + + +def _load_finetuned_clf(model_id: str, ckpt_dir: str): + """Load a fine-tuned EmbeddingClassifier. Returns (clf, device) or (None, None) if checkpoint missing.""" + import torch + from sentence_transformers import SentenceTransformer + from balanceteshaters.scripts.ml.config import get_device_for_model + from balanceteshaters.scripts.ml.models import EmbeddingClassifier + + state_path = Path(ckpt_dir) / "best_model.pt" + if not state_path.exists(): + return None, None + + device = get_device_for_model(model_id) + is_jina = "jinaai/" in model_id + load_kwargs = {"device": device} + if is_jina: + load_kwargs["trust_remote_code"] = True + encoder = SentenceTransformer(model_id, **load_kwargs) + + task = "classification" if is_jina else None + probe_kwargs = {"convert_to_numpy": True, "show_progress_bar": False} + if task: + probe_kwargs["task"] = task + embed_dim = encoder.encode(["probe"], **probe_kwargs).shape[1] + + clf = EmbeddingClassifier(encoder, embed_dim, task=task) + clf.load_state_dict(torch.load(state_path, map_location="cpu")) + clf.eval().to(device) + return clf, device + + +def cmd_predict(model_id: str, ckpt_dir: str, output_npy: str): + import torch + import pandas as pd + from balanceteshaters.scripts.ml.config import DATA_DIR + + clf, _ = _load_finetuned_clf(model_id, ckpt_dir) + if clf is None: + np.save(output_npy, np.array([-1])) + return + + texts = pd.read_parquet(DATA_DIR / "test.parquet")["comment"].tolist() + all_preds = [] + with torch.no_grad(): + for i in range(0, len(texts), 32): + all_preds.extend(torch.argmax(clf(texts[i:i + 32]), dim=-1).cpu().numpy()) + np.save(output_npy, np.array(all_preds)) + + +def cmd_predict_proba(model_id: str, ckpt_dir: str, output_npy: str): + import torch + import torch.nn.functional as F + import pandas as pd + from balanceteshaters.scripts.ml.config import DATA_DIR + + clf, _ = _load_finetuned_clf(model_id, ckpt_dir) + if clf is None: + np.save(output_npy, np.array([-1.0])) + return + + texts = pd.read_parquet(DATA_DIR / "test.parquet")["comment"].tolist() + all_proba = [] + with torch.no_grad(): + for i in range(0, len(texts), 32): + proba = F.softmax(clf(texts[i:i + 32]), dim=-1)[:, 1].cpu().numpy() + all_proba.extend(proba) + np.save(output_npy, np.array(all_proba)) + + +if __name__ == "__main__": + cmd = sys.argv[1] + if cmd == "embed": + split = sys.argv[4] if len(sys.argv) > 4 else "test" + cmd_embed(sys.argv[2], sys.argv[3], split) + elif cmd == "predict_classical": + cmd_predict_classical(sys.argv[2], sys.argv[3], sys.argv[4]) + elif cmd == "predict_proba_classical": + cmd_predict_proba_classical(sys.argv[2], sys.argv[3], sys.argv[4]) + elif cmd == "predict_proba": + cmd_predict_proba(sys.argv[2], sys.argv[3], sys.argv[4]) + elif cmd == "predict": + cmd_predict(sys.argv[2], sys.argv[3], sys.argv[4]) + else: + print(f"Unknown command: {cmd}", file=sys.stderr) + sys.exit(1) diff --git a/backend/balanceteshaters/scripts/ml/_mlp_arch_search.py b/backend/balanceteshaters/scripts/ml/_mlp_arch_search.py new file mode 100644 index 00000000..c6cc9aad --- /dev/null +++ b/backend/balanceteshaters/scripts/ml/_mlp_arch_search.py @@ -0,0 +1,151 @@ +# ruff: noqa: E402 +""" +Architecture search for MLP on frozen Arctic embeddings. +Sweeps hidden layer configs and decision thresholds, logs the best result to NocoDB. +""" +import os +import subprocess +import sys +import tempfile +os.environ['PYTORCH_MPS_HIGH_WATERMARK_RATIO'] = '0.0' + +import numpy as np +import pandas as pd +import sklearn.metrics +from dotenv import load_dotenv +from pathlib import Path +from sklearn.neural_network import MLPClassifier +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import StandardScaler + +SCRIPTS_DIR = Path(__file__).resolve().parent.parent.parent.parent +if str(SCRIPTS_DIR) not in sys.path: + sys.path.insert(0, str(SCRIPTS_DIR)) + +from balanceteshaters.services.nocodb import NocoDBService +from balanceteshaters.scripts.ml.config import ( + ANNOTATION_TABLE_ID, ARCTIC_EMBED_MODEL_ID, DATA_DIR, EVAL_TABLE_ID, model_slug, +) + +_HELPER = Path(__file__).parent / '_eval_subprocess.py' + +# Best F1 from the standard pipeline run (from 04_compare_evaluate.py) +BASELINE_F1 = 0.7414 + +THRESHOLDS = [0.50, 0.45, 0.40, 0.35, 0.30, 0.25, 0.20] + +ARCHITECTURES = [ + (256,), + (512,), + (256, 64), + (512, 128), # default in 02_embed_and_train_classical.py + (256, 128), + (128, 64), + (512, 256, 64), + (256, 128, 32), +] + + +def embed_split(split: str, tmp: Path) -> np.ndarray: + """Embed a data split in a subprocess (avoids OpenMP conflict with sklearn).""" + npy = str(tmp / f'X_{split}.npy') + r = subprocess.run( + [sys.executable, str(_HELPER), 'embed', ARCTIC_EMBED_MODEL_ID, npy, split], + capture_output=True, text=True, + ) + if r.returncode != 0: + print(r.stderr[-500:]) + sys.exit(1) + X = np.load(npy) + print(f' {split}: {X.shape}') + return X + + +def main(): + load_dotenv() + nocodb = NocoDBService( + os.environ['NOCODB_BASE_URL'], + os.environ['NOCODB_TOKEN'], + os.environ['NOCODB_BASE_ID'], + ) + tmp = Path(tempfile.mkdtemp()) + + print('Embedding splits...') + X_test = embed_split('test', tmp) + X_train = embed_split('train_real', tmp) + X_val = embed_split('val', tmp) + + y_test = pd.read_parquet(DATA_DIR / 'test.parquet')['label'].values + y_train = pd.read_parquet(DATA_DIR / 'train_real.parquet')['label'].values + y_val = pd.read_parquet(DATA_DIR / 'val.parquet')['label'].values + + print(f'\n{"Architecture":<22} {"ValF1":>7} {"TestF1":>7} {"BestT":>6} {"TunedF1":>8} {"P":>7} {"R":>7}') + print('-' * 90) + + best_result = None + + for layers in ARCHITECTURES: + clf = Pipeline([ + ('scaler', StandardScaler()), + ('clf', MLPClassifier( + hidden_layer_sizes=layers, + activation='relu', + max_iter=300, + early_stopping=True, + validation_fraction=0.1, + n_iter_no_change=15, + random_state=42, + )), + ]) + clf.fit(X_train, y_train) + + val_f1 = sklearn.metrics.f1_score(y_val, clf.predict(X_val), zero_division=0) + proba = clf.predict_proba(X_test)[:, 1] + default_f1 = sklearn.metrics.f1_score(y_test, (proba >= 0.5).astype(int), zero_division=0) + + best_t, best_f1, best_p, best_r = 0.5, 0.0, 0.0, 0.0 + for t in THRESHOLDS: + y_pred = (proba >= t).astype(int) + f1 = sklearn.metrics.f1_score(y_test, y_pred, zero_division=0) + if f1 > best_f1: + best_f1 = f1 + best_t = t + best_p = sklearn.metrics.precision_score(y_test, y_pred, zero_division=0) + best_r = sklearn.metrics.recall_score(y_test, y_pred, zero_division=0) + + flag = ' ***' if best_f1 > BASELINE_F1 else '' + print(f' {str(layers):<20} {val_f1:>7.4f} {default_f1:>7.4f} {best_t:>6.2f} {best_f1:>8.4f} {best_p:>7.4f} {best_r:>7.4f}{flag}') + + if best_result is None or best_f1 > best_result['tuned_f1']: + best_result = { + 'layers': layers, 'tuned_f1': best_f1, 'best_t': best_t, + 'precision': best_p, 'recall': best_r, + 'accuracy': sklearn.metrics.accuracy_score(y_test, (proba >= best_t).astype(int)), + } + + print(f'\nBest: {best_result["layers"]} at t={best_result["best_t"]} F1={best_result["tuned_f1"]:.4f}') + + if best_result['tuned_f1'] > BASELINE_F1: + slug = model_slug(ARCTIC_EMBED_MODEL_ID) + run_name = f'{slug}+mlp{best_result["layers"]}+real+threshold={best_result["best_t"]}' + nocodb.create_record(EVAL_TABLE_ID, { + 'model_name': run_name, + 'table_id': ANNOTATION_TABLE_ID, + 'table_name': 'finetune/real', + 'f1': best_result['tuned_f1'], + 'precision': best_result['precision'], + 'recall': best_result['recall'], + 'accuracy': best_result['accuracy'], + 'total_samples': len(y_test), + 'positive_samples': int(y_test.sum()), + 'negative_samples': len(y_test) - int(y_test.sum()), + 'prompt': f'arch={best_result["layers"]} threshold={best_result["best_t"]}', + 'model_type': 'encoder embedding', + }) + print(f'Logged {run_name} to NocoDB.') + else: + print('No architecture beats baseline — nothing logged.') + + +if __name__ == '__main__': + main() diff --git a/backend/balanceteshaters/scripts/ml/_threshold_sweep.py b/backend/balanceteshaters/scripts/ml/_threshold_sweep.py new file mode 100644 index 00000000..e30a7a90 --- /dev/null +++ b/backend/balanceteshaters/scripts/ml/_threshold_sweep.py @@ -0,0 +1,127 @@ +# ruff: noqa: E402 +""" +Sweep decision thresholds for the Arctic + classical pipeline. +Logs any run that beats the default t=0.50 threshold to NocoDB. +""" +import os +import subprocess +import sys +import tempfile +os.environ['PYTORCH_MPS_HIGH_WATERMARK_RATIO'] = '0.0' + +import numpy as np +import pandas as pd +import sklearn.metrics +from dotenv import load_dotenv +from pathlib import Path + +SCRIPTS_DIR = Path(__file__).resolve().parent.parent.parent.parent +if str(SCRIPTS_DIR) not in sys.path: + sys.path.insert(0, str(SCRIPTS_DIR)) +load_dotenv() + +from balanceteshaters.services.nocodb import NocoDBService +from balanceteshaters.scripts.ml.config import ( + ANNOTATION_TABLE_ID, ARCTIC_EMBED_MODEL_ID, CHECKPOINTS_DIR, DATA_DIR, EVAL_TABLE_ID, model_slug, +) + +_HELPER = Path(__file__).parent / '_eval_subprocess.py' +tmp = Path(tempfile.mkdtemp()) +emb_path = str(tmp / 'X_arctic.npy') + +# Best F1 from the frozen-embedding classical runs (from 04_compare_evaluate.py) +BASELINE_F1 = 0.741 + +THRESHOLDS = [0.50, 0.45, 0.40, 0.35, 0.30, 0.25, 0.20] + +print('Embedding test set...') +r = subprocess.run( + [sys.executable, str(_HELPER), 'embed', ARCTIC_EMBED_MODEL_ID, emb_path], + capture_output=True, text=True, +) +if r.returncode != 0: + print(r.stderr[-500:]) + sys.exit(1) + +X_test = np.load(emb_path) +y_true = pd.read_parquet(DATA_DIR / 'test.parquet')['label'].values + +nocodb = NocoDBService(os.environ['NOCODB_BASE_URL'], os.environ['NOCODB_TOKEN'], os.environ['NOCODB_BASE_ID']) + + +def best_threshold(proba): + best_t, best_f1, best_m = 0.5, 0.0, None + for t in THRESHOLDS: + y_pred = (proba >= t).astype(int) + f1 = sklearn.metrics.f1_score(y_true, y_pred, zero_division=0) + if f1 > best_f1: + best_f1 = f1 + best_t = t + best_m = { + 'f1': f1, + 'precision': sklearn.metrics.precision_score(y_true, y_pred, zero_division=0), + 'recall': sklearn.metrics.recall_score(y_true, y_pred, zero_division=0), + 'accuracy': sklearn.metrics.accuracy_score(y_true, y_pred), + } + return best_t, best_m + + +def log_result(run_name, dataset, t, m): + nocodb.create_record(EVAL_TABLE_ID, { + 'model_name': f'{run_name}+threshold={t}', + 'table_id': ANNOTATION_TABLE_ID, + 'table_name': f'finetune/{dataset}', + 'f1': m['f1'], 'precision': m['precision'], + 'recall': m['recall'], 'accuracy': m['accuracy'], + 'total_samples': len(y_true), + 'positive_samples': int(y_true.sum()), + 'negative_samples': len(y_true) - int(y_true.sum()), + 'prompt': f'approach=threshold-tuned dataset={dataset} threshold={t}', + 'model_type': 'encoder embedding', + }) + + +def print_row(run, t, m): + flag = ' *** BEATS BASELINE' if m['f1'] > BASELINE_F1 else '' + print(f' {run:<58} {t:>6.2f} {m["f1"]:>7.4f} {m["precision"]:>7.4f} {m["recall"]:>7.4f} {m["accuracy"]:>7.4f}{flag}') + + +print(f'\n{"Run":<60} {"BestT":>6} {"F1":>7} {"P":>7} {"R":>7} {"Acc":>7}') +print('-' * 100) + +slug = model_slug(ARCTIC_EMBED_MODEL_ID) + +for clf_name in ['logreg', 'lightgbm', 'mlp']: + ckpt = CHECKPOINTS_DIR / f'{slug}+{clf_name}+real.joblib' + if not ckpt.exists(): + continue + proba_path = str(tmp / f'proba_{clf_name}.npy') + r = subprocess.run( + [sys.executable, str(_HELPER), 'predict_proba_classical', emb_path, str(ckpt), proba_path], + capture_output=True, text=True, + ) + if r.returncode != 0: + print(f' [error] {clf_name}: {r.stderr[-200:]}') + continue + run = f'{slug}+{clf_name}+real' + t, m = best_threshold(np.load(proba_path)) + print_row(run, t, m) + if t != 0.50: + log_result(run, 'real', t, m) + +ft_dir = str(CHECKPOINTS_DIR / f'{slug}-finetuned-head_only-real') +proba_path = str(tmp / 'proba_ft_real.npy') +r = subprocess.run( + [sys.executable, str(_HELPER), 'predict_proba', ARCTIC_EMBED_MODEL_ID, ft_dir, proba_path], + capture_output=True, text=True, +) +if r.returncode == 0: + run = f'{slug}-finetuned-head_only-real' + t, m = best_threshold(np.load(proba_path)) + print_row(run, t, m) + if t != 0.50: + log_result(run, 'real', t, m) +else: + print(f' [error] finetuned: {r.stderr[-300:]}') + +print('\nAll non-default thresholds logged to NocoDB.') diff --git a/backend/balanceteshaters/scripts/ml/config.py b/backend/balanceteshaters/scripts/ml/config.py new file mode 100644 index 00000000..d389d04f --- /dev/null +++ b/backend/balanceteshaters/scripts/ml/config.py @@ -0,0 +1,69 @@ +from pathlib import Path +import torch + +JINA_MODEL_ID = "jinaai/jina-embeddings-v5-text-nano" # 239M, EuroBERT, CPU-only (MPS segfault) +JINA_SMALL_MODEL_ID = "jinaai/jina-embeddings-v5-text-small" # 677M, Qwen3-based, MPS-safe +# Encoder-only, 270M, 640-dim, 94-language, no trust_remote_code needed +BIDIR_MODEL_ID = "microsoft/harrier-oss-v1-270m" +# Encoder-only, 568M, 1024-dim, bge-m3-retromae base, MRL, no trust_remote_code +ARCTIC_EMBED_MODEL_ID = "Snowflake/snowflake-arctic-embed-l-v2.0" +# 600M XLM-R fine-tuned on multilingual toxicity (15 langs incl. French); labels: 0=neutral, 1=toxic +XLMR_TOXICITY_MODEL_ID = "textdetox/xlmr-large-toxicity-classifier-v2" +MODELS = [JINA_MODEL_ID, JINA_SMALL_MODEL_ID, BIDIR_MODEL_ID, ARCTIC_EMBED_MODEL_ID, XLMR_TOXICITY_MODEL_ID] + +MODEL_TYPE = { + JINA_MODEL_ID: "encoder embedding", + JINA_SMALL_MODEL_ID: "encoder embedding", + BIDIR_MODEL_ID: "encoder embedding", + ARCTIC_EMBED_MODEL_ID: "encoder embedding", + XLMR_TOXICITY_MODEL_ID: "encoder classifier", +} + +SCRIPTS_DIR = Path(__file__).resolve().parent +BACKEND_DIR = SCRIPTS_DIR.parent.parent.parent +DATA_DIR = BACKEND_DIR / "balanceteshaters" / "data" / "finetune" +CHECKPOINTS_DIR = DATA_DIR / "checkpoints" + +ANNOTATION_TABLE_ID = "m5t7qqaer2oa441" +EVAL_TABLE_ID = "m0ww7qnx69u9r1a" + +LABEL_MAP = { + "Absence de cyberharcèlement": 0, +} + +MINORITY_CATEGORIES = [ + "Doxxing", + "Incitation au suicide", + "Cyberharcèlement à caractère sexuel", + "Menaces", + "Incitation à la haine", +] + + +def get_device() -> str: + if torch.backends.mps.is_available(): + return "mps" + if torch.cuda.is_available(): + return "cuda" + return "cpu" + + +def get_device_for_model(model_id: str) -> str: + # jina-v5-text-nano uses EuroBERT which segfaults on MPS — force CPU + # jina-v5-text-small uses Qwen3 and is MPS-safe + if model_id == JINA_MODEL_ID: + return "cpu" + return get_device() + + +def compute_binary_label(annotated_categories: list[str] | None) -> int | None: + if not annotated_categories: + return None + for cat in annotated_categories: + if "Absence de cyberharcèlement" in cat: + return 0 + return 1 + + +def model_slug(model_id: str) -> str: + return model_id.split("/")[-1] diff --git a/backend/balanceteshaters/scripts/ml/dedup_eval_table.py b/backend/balanceteshaters/scripts/ml/dedup_eval_table.py new file mode 100644 index 00000000..752b9101 --- /dev/null +++ b/backend/balanceteshaters/scripts/ml/dedup_eval_table.py @@ -0,0 +1,77 @@ +# ruff: noqa: E402 +""" +Remove duplicate rows from the NocoDB eval table (m0ww7qnx69u9r1a). +Keeps the most recently inserted record per model_name, deletes the rest. +""" +import os +import sys +from pathlib import Path + +from dotenv import load_dotenv + +SCRIPTS_DIR = Path(__file__).resolve().parent.parent.parent.parent +if str(SCRIPTS_DIR) not in sys.path: + sys.path.insert(0, str(SCRIPTS_DIR)) + +from balanceteshaters.scripts.ml.config import EVAL_TABLE_ID +from balanceteshaters.services.nocodb import NocoDBService + + +def main(): + load_dotenv() + nocodb = NocoDBService( + nocodb_url=os.environ["NOCODB_BASE_URL"], + token=os.environ["NOCODB_TOKEN"], + base_id=os.environ["NOCODB_BASE_ID"], + ) + + # Fetch all records with pagination + print(f"Fetching all records from {EVAL_TABLE_ID}...") + all_records = [] + offset = 0 + limit = 1000 + while True: + resp = nocodb.get_records(EVAL_TABLE_ID, limit=limit, offset=offset) + records = resp.get("records", []) + if not records: + break + all_records.extend(records) + if resp.get("next") is None: + break + offset += limit + + print(f" Total records: {len(all_records)}") + + # Group by model_name, keep highest id (most recent insert) + seen: dict[str, int] = {} # model_name -> record id to keep + to_delete: list[int] = [] + + for rec in all_records: + fields = rec.get("fields", {}) + model_name = fields.get("model_name", "") + rec_id = rec["id"] + + if model_name not in seen: + seen[model_name] = rec_id + else: + # Keep the higher id (more recent), mark the other for deletion + if rec_id > seen[model_name]: + to_delete.append(seen[model_name]) + seen[model_name] = rec_id + else: + to_delete.append(rec_id) + + print(f" Unique model_names: {len(seen)}") + print(f" Duplicates to delete: {len(to_delete)}") + + if not to_delete: + print("Nothing to delete.") + return + + print(f"Deleting {len(to_delete)} duplicate records...") + nocodb.delete_records(EVAL_TABLE_ID, to_delete) + print("Done.") + + +if __name__ == "__main__": + main() diff --git a/backend/balanceteshaters/scripts/ml/models.py b/backend/balanceteshaters/scripts/ml/models.py new file mode 100644 index 00000000..2e884fb9 --- /dev/null +++ b/backend/balanceteshaters/scripts/ml/models.py @@ -0,0 +1,34 @@ +"""Shared model definitions for the ML fine-tuning scripts.""" +import torch +import torch.nn as nn + + +class EmbeddingClassifier(nn.Module): + """Wraps a SentenceTransformer encoder with a linear classification head.""" + + def __init__(self, encoder, embed_dim: int, num_labels: int = 2, task: str | None = None, trainable_encoder: bool = False): + super().__init__() + self.encoder = encoder + self.classifier = nn.Linear(embed_dim, num_labels) + self.task = task + self.trainable_encoder = trainable_encoder + + def forward(self, texts: list[str], labels: torch.Tensor | None = None): + if self.trainable_encoder: + # SentenceTransformer.forward() preserves the computation graph; encode() does not + features = self.encoder.tokenize(texts) + features = {k: v.to(self.classifier.weight.device) for k, v in features.items()} + embeddings = self.encoder(features)["sentence_embedding"] + else: + encode_kwargs: dict = {"convert_to_numpy": True, "show_progress_bar": False} + if self.task: + encode_kwargs["task"] = self.task + embeddings = torch.tensor( + self.encoder.encode(texts, **encode_kwargs), + dtype=torch.float32, + ).to(self.classifier.weight.device) + logits = self.classifier(embeddings) + if labels is not None: + loss = nn.CrossEntropyLoss()(logits, labels) + return loss, logits + return logits diff --git a/backend/balanceteshaters/services/nocodb.py b/backend/balanceteshaters/services/nocodb.py index ca9b6a80..1b610eda 100644 --- a/backend/balanceteshaters/services/nocodb.py +++ b/backend/balanceteshaters/services/nocodb.py @@ -49,6 +49,16 @@ def get_table_info(self, table_id: str) -> dict[str, Any]: response.raise_for_status() return response.json() + def delete_records(self, table_id: str, record_ids: list[int]) -> None: + """Delete records by ID list (batched to avoid URL length limits).""" + url = f"{self.nocodb_url}/api/v3/data/{self.base_id}/{table_id}/records" + headers = {"accept": "application/json", "xc-token": self.token, "Content-Type": "application/json"} + batch_size = 10 + for i in range(0, len(record_ids), batch_size): + batch = record_ids[i:i + batch_size] + response = requests.delete(url, headers=headers, json=[{"id": rid} for rid in batch]) + response.raise_for_status() + def count_records(self, table_id: str, where_str: str | None = None) -> int: """Count the number of records in a NocoDB table.""" url = f"{self.nocodb_url}/api/v3/data/{self.base_id}/{table_id}/count" diff --git a/backend/pyproject.toml b/backend/pyproject.toml index 26fab021..8994e1f9 100644 --- a/backend/pyproject.toml +++ b/backend/pyproject.toml @@ -19,11 +19,27 @@ dependencies = [ "tqdm>=4.67.3", "uuid7>=0.1.0", "scikit-learn>=1.8.0", + "pandas>=3.0.1", + "transformers>=5.3.0", + "torch>=2.11.0", + "accelerate>=1.13.0", + 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