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Implement ensemble into final model structure

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This commit is contained in:
William Jeynes
2026-03-24 19:07:24 +00:00
parent 624d45bc53
commit 8f939d54c4
9 changed files with 71 additions and 347 deletions
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supporting/RAGAS_Service/ensemble_service.py
+5 -11
View File
@@ -3,21 +3,15 @@ from fastapi import FastAPI
import torch
import torch.nn as nn
import os
# Embedding model
from sentence_transformers import SentenceTransformer
from huggingface_hub import hf_hub_download
# Roberta
from transformers import RobertaTokenizer, RobertaForSequenceClassification
# Flan (seq2seq)
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
app = FastAPI()
############################################
# ----------- REQUEST SCHEMA ---------------
# SCHEMA
############################################
class EvalRequest(BaseModel):
@@ -26,7 +20,7 @@ class EvalRequest(BaseModel):
############################################
# ----------- LOGREG MODEL -----------------
# REGRESSION MODEL
############################################
HF_REPO_ID = "WillJeynes/LLMsForDisinformationAnalysis-Regression"
@@ -72,7 +66,7 @@ logreg_model.eval()
############################################
# ----------- ROBERTA MODEL ----------------
# ROBERTA
############################################
ROBERTA_PATH = "WillJeynes/LLMsForDisinformationAnalysis"
@@ -83,7 +77,7 @@ roberta_model.eval()
############################################
# ----------- FLAN MODEL -------------------
# FLAN
############################################
FLAN_PATH = "WillJeynes/LLMsForDisinformationAnalysis-Flan"
@@ -126,7 +120,7 @@ def parse_generated_label(generated: str):
############################################
# ----------- MAIN ENDPOINT ---------------
# ENDPOINT
############################################
@app.post("/evaluate")
supporting/RAGAS_Service/train_distilled.py
-234
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@@ -1,234 +0,0 @@
from sklearn.utils import compute_class_weight
from torch.nn import CrossEntropyLoss
from transformers import Trainer, TrainingArguments, AutoTokenizer, AutoModelForSequenceClassification
import torch
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
from collections import Counter
import sys
import csv
import numpy as np
NUM_CLASSES = 3
model_name = "distilbert/distilroberta-base" # Or MiniLM, or any other transformer model
LABEL_PRIORITY = [
("PERFECT", 0),
("STORY", 1),
("NSPECIFIC", 2),
("REWORDING", 1),
("TINCORRECT", -1),
("DUPLICATE", -1),
("", 0), # fallback to PERFECT
]
class WeightedTrainer(Trainer):
def __init__(self, *args, class_weights=None, **kwargs):
super().__init__(*args, **kwargs)
self.class_weights = class_weights
def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
labels = inputs.get("labels")
# print("DBG: Before forward")
outputs = model(**inputs)
# print("DBG: After forward")
logits = outputs.get("logits")
loss_fct = CrossEntropyLoss(
weight=self.class_weights.to(logits.device).to(logits.dtype)
)
# loss_fct = CrossEntropyLoss()
# print("DBG: Before loss")
loss = loss_fct(logits, labels)
# loss.backward()
# print("DBG: After loss")
return (loss, outputs) if return_outputs else loss
def label_to_int(extra_info: str) -> int:
"""
Convert extra_info string to integer label using priority rules.
"""
if extra_info is None:
extra_info = ""
extra_info = extra_info.strip()
# Handle empty string explicitly
if extra_info == "":
for key, value in LABEL_PRIORITY:
if key == "":
return value
raise ValueError("Empty extra_info but no empty mapping defined")
# Split words (case-insensitive)
tokens = set(extra_info.upper().split())
# Priority matching
for key, value in LABEL_PRIORITY:
if key == "":
continue
if key in tokens:
return value
raise ValueError(f"Unknown label content: '{extra_info}'")
def load_dataset_from_csv(path):
texts = []
labels = []
removed_rows = 0
with open(path, newline="", encoding="utf-8") as f:
reader = csv.DictReader(f)
for i, row in enumerate(reader, start=1):
text = row["event"]
label_str = row["extra_info"]
try:
label_int = label_to_int(label_str)
except Exception as e:
print(f"ERROR converting label on line {i}: {label_str}")
print(e)
sys.exit(1)
# Skip rows marked for removal
if label_int == -1:
removed_rows += 1
continue
texts.append(text)
labels.append(label_int)
print(f"Loaded {len(texts)} samples (removed {removed_rows})")
return texts, labels
def compute_metrics(eval_pred):
logits, labels = eval_pred
preds = logits.argmax(axis=1)
return {
"accuracy": accuracy_score(labels, preds),
"f1": f1_score(labels, preds, average="weighted", zero_division=0),
"precision": precision_score(labels, preds, average="weighted", zero_division=0),
"recall": recall_score(labels, preds, average="weighted", zero_division=0),
}
def main():
torch.multiprocessing.set_start_method('fork')
print("CUDA available:", torch.cuda.is_available())
print("CUDA device count:", torch.cuda.device_count())
print("Current device:", torch.cuda.current_device() if torch.cuda.is_available() else "CPU")
texts, labels = load_dataset_from_csv("../../data/classify.csv")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(
model_name,
num_labels=NUM_CLASSES
)
print("Dataset size:", len(texts))
print("Label distribution:")
print(Counter(labels))
train_texts, val_texts, train_labels, val_labels = train_test_split(
texts,
labels,
test_size=0.2,
random_state=42,
stratify=labels
)
class_weights = compute_class_weight(
class_weight="balanced",
classes=np.unique(train_labels),
y=train_labels
)
class_weights = torch.tensor(class_weights, dtype=torch.float)
print("Class weights:", class_weights)
train_encodings = tokenizer(
train_texts,
truncation=True,
padding=True,
max_length=256
)
val_encodings = tokenizer(
val_texts,
truncation=True,
padding=True,
max_length=256
)
class TextDataset(torch.utils.data.Dataset):
def __init__(self, encodings, labels):
self.encodings = encodings
self.labels = labels
def __getitem__(self, idx):
# print(f"DBG: Loading item {idx}")
item = {
key: torch.tensor(val[idx])
for key, val in self.encodings.items()
}
item["labels"] = torch.tensor(self.labels[idx])
return item
def __len__(self):
return len(self.labels)
training_args = TrainingArguments(
output_dir="./results",
learning_rate=2e-5,
per_device_train_batch_size=32,
# gradient_accumulation_steps=2,
num_train_epochs=15,
weight_decay=0.01,
load_best_model_at_end=True,
eval_strategy="epoch",
save_strategy="epoch",
metric_for_best_model="f1",
greater_is_better=True,
dataloader_num_workers=4,
dataloader_pin_memory=True,
# warmup_steps=100,
)
train_dataset = TextDataset(train_encodings, train_labels)
val_dataset = TextDataset(val_encodings, val_labels)
trainer = WeightedTrainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=val_dataset,
compute_metrics=compute_metrics,
class_weights=class_weights
)
trainer.train()
metrics = trainer.evaluate()
print("Final evaluation metrics:")
for k, v in metrics.items():
print(f"{k}: {v}")
trainer.save_model("./roberta_distilled_classifier")
tokenizer.save_pretrained("./roberta_distilled_classifier")
if __name__ == "__main__":
main()