Add training scripts for distilled, flan. Add run service for flan
This commit is contained in:
William Jeynes
@@ -1,6 +1,7 @@
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# -- OURS --
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results/
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roberta_classifier/
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roberta_distilled_classifier/
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roberta_classifier*/
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output*
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@@ -0,0 +1,89 @@
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from pydantic import BaseModel
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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import torch
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from fastapi import FastAPI
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app = FastAPI()
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MODEL_PATH = "WillJeynes/LLMsForDisinformationAnalysis-Flan"
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INT_TO_LABEL = {
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0: "perfect",
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1: "story",
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2: "not specific",
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}
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LABEL_TO_INT = {v: k for k, v in INT_TO_LABEL.items()}
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tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
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model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_PATH)
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model.eval()
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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def format_prompt(text: str) -> str:
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return (
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"Classify the following event into one of these categories: "
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"perfect, story, not specific.\n\n"
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f"Event: {text}\n\n"
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"Category:"
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)
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def parse_generated_label(generated: str) -> int | None:
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generated = generated.strip().lower()
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for label_text, label_int in LABEL_TO_INT.items():
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if label_text in generated:
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return label_int
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return None
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class EvalRequest(BaseModel):
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answer: str
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@app.post("/evaluate")
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def evaluate(req: EvalRequest):
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prompt = format_prompt(req.answer)
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inputs = tokenizer(
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prompt,
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return_tensors="pt",
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truncation=True,
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padding=True,
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max_length=256,
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).to(device)
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with torch.no_grad():
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# Get the generated label
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outputs = model.generate(
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**inputs,
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max_new_tokens=8,
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)
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# Produce a confidence score
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decoder_input_ids = torch.tensor([[model.config.decoder_start_token_id]]).to(device)
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logits_output = model(**inputs, decoder_input_ids=decoder_input_ids)
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logits = logits_output.logits[:, 0, :]
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# Decode the generated text label
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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predicted_int = parse_generated_label(generated_text)
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# Extract probabilities
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label_token_ids = {
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label: tokenizer(label, add_special_tokens=False).input_ids[0]
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for label in LABEL_TO_INT.keys()
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}
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label_logits = torch.tensor(
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[logits[0, tid].item() for tid in label_token_ids.values()]
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)
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label_probs = torch.softmax(label_logits, dim=0).tolist()
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return {
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"generated": generated_text,
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"probabilities": [label_probs],
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}
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@@ -5,7 +5,7 @@ from fastapi import FastAPI
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app = FastAPI()
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MODEL_PATH = "./roberta_classifier"
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MODEL_PATH = "WillJeynes/LLMsForDisinformationAnalysis"
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tokenizer = RobertaTokenizer.from_pretrained(MODEL_PATH)
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model = RobertaForSequenceClassification.from_pretrained(MODEL_PATH)
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@@ -0,0 +1,234 @@
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from sklearn.utils import compute_class_weight
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from torch.nn import CrossEntropyLoss
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from transformers import Trainer, TrainingArguments, AutoTokenizer, AutoModelForSequenceClassification
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import torch
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
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from collections import Counter
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import sys
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import csv
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import numpy as np
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NUM_CLASSES = 3
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model_name = "distilbert/distilroberta-base" # Or MiniLM, or any other transformer model
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LABEL_PRIORITY = [
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("PERFECT", 0),
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("STORY", 1),
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("NSPECIFIC", 2),
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("REWORDING", 1),
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("TINCORRECT", -1),
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("DUPLICATE", -1),
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("", 0), # fallback to PERFECT
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]
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class WeightedTrainer(Trainer):
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def __init__(self, *args, class_weights=None, **kwargs):
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super().__init__(*args, **kwargs)
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self.class_weights = class_weights
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def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
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labels = inputs.get("labels")
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# print("DBG: Before forward")
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outputs = model(**inputs)
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# print("DBG: After forward")
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logits = outputs.get("logits")
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loss_fct = CrossEntropyLoss(
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weight=self.class_weights.to(logits.device).to(logits.dtype)
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)
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# loss_fct = CrossEntropyLoss()
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# print("DBG: Before loss")
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loss = loss_fct(logits, labels)
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# loss.backward()
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# print("DBG: After loss")
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return (loss, outputs) if return_outputs else loss
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def label_to_int(extra_info: str) -> int:
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"""
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Convert extra_info string to integer label using priority rules.
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"""
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if extra_info is None:
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extra_info = ""
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extra_info = extra_info.strip()
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# Handle empty string explicitly
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if extra_info == "":
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for key, value in LABEL_PRIORITY:
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if key == "":
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return value
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raise ValueError("Empty extra_info but no empty mapping defined")
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# Split words (case-insensitive)
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tokens = set(extra_info.upper().split())
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# Priority matching
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for key, value in LABEL_PRIORITY:
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if key == "":
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continue
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if key in tokens:
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return value
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raise ValueError(f"Unknown label content: '{extra_info}'")
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def load_dataset_from_csv(path):
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texts = []
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labels = []
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removed_rows = 0
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with open(path, newline="", encoding="utf-8") as f:
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reader = csv.DictReader(f)
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for i, row in enumerate(reader, start=1):
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text = row["event"]
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label_str = row["extra_info"]
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try:
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label_int = label_to_int(label_str)
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except Exception as e:
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print(f"ERROR converting label on line {i}: {label_str}")
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print(e)
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sys.exit(1)
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# Skip rows marked for removal
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if label_int == -1:
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removed_rows += 1
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continue
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texts.append(text)
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labels.append(label_int)
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print(f"Loaded {len(texts)} samples (removed {removed_rows})")
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return texts, labels
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def compute_metrics(eval_pred):
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logits, labels = eval_pred
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preds = logits.argmax(axis=1)
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return {
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"accuracy": accuracy_score(labels, preds),
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"f1": f1_score(labels, preds, average="weighted", zero_division=0),
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"precision": precision_score(labels, preds, average="weighted", zero_division=0),
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"recall": recall_score(labels, preds, average="weighted", zero_division=0),
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}
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def main():
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torch.multiprocessing.set_start_method('fork')
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print("CUDA available:", torch.cuda.is_available())
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print("CUDA device count:", torch.cuda.device_count())
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print("Current device:", torch.cuda.current_device() if torch.cuda.is_available() else "CPU")
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texts, labels = load_dataset_from_csv("../../data/classify.csv")
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSequenceClassification.from_pretrained(
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model_name,
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num_labels=NUM_CLASSES
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)
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print("Dataset size:", len(texts))
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print("Label distribution:")
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print(Counter(labels))
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train_texts, val_texts, train_labels, val_labels = train_test_split(
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texts,
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labels,
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test_size=0.2,
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random_state=42,
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stratify=labels
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)
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class_weights = compute_class_weight(
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class_weight="balanced",
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classes=np.unique(train_labels),
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y=train_labels
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)
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class_weights = torch.tensor(class_weights, dtype=torch.float)
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print("Class weights:", class_weights)
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train_encodings = tokenizer(
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train_texts,
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truncation=True,
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padding=True,
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max_length=256
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)
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val_encodings = tokenizer(
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val_texts,
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truncation=True,
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padding=True,
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max_length=256
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)
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class TextDataset(torch.utils.data.Dataset):
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def __init__(self, encodings, labels):
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self.encodings = encodings
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self.labels = labels
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def __getitem__(self, idx):
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# print(f"DBG: Loading item {idx}")
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item = {
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key: torch.tensor(val[idx])
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for key, val in self.encodings.items()
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}
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item["labels"] = torch.tensor(self.labels[idx])
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return item
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def __len__(self):
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return len(self.labels)
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training_args = TrainingArguments(
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output_dir="./results",
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learning_rate=2e-5,
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per_device_train_batch_size=32,
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# gradient_accumulation_steps=2,
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num_train_epochs=15,
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weight_decay=0.01,
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load_best_model_at_end=True,
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eval_strategy="epoch",
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save_strategy="epoch",
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metric_for_best_model="f1",
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greater_is_better=True,
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dataloader_num_workers=4,
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dataloader_pin_memory=True,
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# warmup_steps=100,
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)
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train_dataset = TextDataset(train_encodings, train_labels)
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val_dataset = TextDataset(val_encodings, val_labels)
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trainer = WeightedTrainer(
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model=model,
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args=training_args,
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train_dataset=train_dataset,
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eval_dataset=val_dataset,
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compute_metrics=compute_metrics,
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class_weights=class_weights
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)
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trainer.train()
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metrics = trainer.evaluate()
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print("Final evaluation metrics:")
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for k, v in metrics.items():
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print(f"{k}: {v}")
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trainer.save_model("./roberta_distilled_classifier")
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tokenizer.save_pretrained("./roberta_distilled_classifier")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,227 @@
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from sklearn.utils import compute_class_weight
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Seq2SeqTrainer, Seq2SeqTrainingArguments, DataCollatorForSeq2Seq
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import torch
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
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from collections import Counter
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import sys
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import csv
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import numpy as np
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NUM_CLASSES = 3
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model_name = "google/flan-t5-base"
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INT_TO_LABEL = {
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0: "perfect",
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1: "story",
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2: "not specific",
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}
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LABEL_TO_INT = {v: k for k, v in INT_TO_LABEL.items()}
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LABEL_PRIORITY = [
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("PERFECT", 0),
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("STORY", 1),
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("NSPECIFIC", 2),
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("REWORDING", 1),
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("TINCORRECT", -1),
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("DUPLICATE", -1),
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("", 0),
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]
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def label_to_int(extra_info: str) -> int:
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if extra_info is None:
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extra_info = ""
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extra_info = extra_info.strip()
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if extra_info == "":
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for key, value in LABEL_PRIORITY:
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if key == "":
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return value
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raise ValueError("Empty extra_info but no empty mapping defined")
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tokens = set(extra_info.upper().split())
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for key, value in LABEL_PRIORITY:
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if key == "" :
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continue
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if key in tokens:
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return value
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raise ValueError(f"Unknown label content: '{extra_info}'")
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def load_dataset_from_csv(path):
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texts = []
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labels = []
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removed_rows = 0
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with open(path, newline="", encoding="utf-8") as f:
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reader = csv.DictReader(f)
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for i, row in enumerate(reader, start=1):
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text = row["event"]
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label_str = row["extra_info"]
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try:
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label_int = label_to_int(label_str)
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except Exception as e:
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print(f"ERROR converting label on line {i}: {label_str}")
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print(e)
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sys.exit(1)
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if label_int == -1:
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removed_rows += 1
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continue
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texts.append(text)
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labels.append(label_int)
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print(f"Loaded {len(texts)} samples (removed {removed_rows})")
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return texts, labels
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def format_prompt(text: str) -> str:
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return (
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"Classify the following event into one of these categories: "
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"perfect, story, not specific.\n\n"
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f"Event: {text}\n\n"
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"Category:"
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)
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def parse_generated_label(generated: str) -> int:
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generated = generated.strip().lower()
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for label_text, label_int in LABEL_TO_INT.items():
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if label_text in generated:
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return label_int
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print("invlid label:" + generated)
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return -1 # unknown / unparseable output
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class GenerativeTextDataset(torch.utils.data.Dataset):
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def __init__(self, texts, labels, tokenizer, max_input_length=256, max_target_length=8):
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self.tokenizer = tokenizer
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self.max_input_length = max_input_length
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self.max_target_length = max_target_length
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self.inputs = [format_prompt(t) for t in texts]
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# Convert integer labels to their text equivalents for the target sequence
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self.targets = [INT_TO_LABEL[l] for l in labels]
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self.int_labels = labels # keep originals for metric computation
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def __len__(self):
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return len(self.inputs)
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def __getitem__(self, idx):
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model_inputs = self.tokenizer(
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self.inputs[idx],
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max_length=self.max_input_length,
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truncation=True,
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padding=False,
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)
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target_encoding = self.tokenizer(
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self.targets[idx],
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max_length=self.max_target_length,
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truncation=True,
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padding=False,
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)
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# Seq2Seq convention: labels use -100 to ignore padding tokens in loss
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labels = target_encoding["input_ids"]
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labels = [token if token != self.tokenizer.pad_token_id else -100 for token in labels]
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model_inputs["labels"] = labels
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return {k: torch.tensor(v) for k, v in model_inputs.items()}
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def compute_metrics_generative(eval_pred, tokenizer):
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predictions, label_ids = eval_pred
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# Decode predictions
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# Replace -100 in labels before decoding
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label_ids = np.where(label_ids != -100, label_ids, tokenizer.pad_token_id)
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decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)
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decoded_labels = tokenizer.batch_decode(label_ids, skip_special_tokens=True)
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# Map decoded text back to integer labels
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pred_ints = [parse_generated_label(p) for p in decoded_preds]
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true_ints = [parse_generated_label(l) for l in decoded_labels]
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# Filter out any rows where parsing failed
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valid = [(p, t) for p, t in zip(pred_ints, true_ints) if t != -1]
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if not valid:
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return {"accuracy": 0.0, "f1": 0.0, "precision": 0.0, "recall": 0.0}
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preds_filtered, true_filtered = zip(*valid)
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return {
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"accuracy": accuracy_score(true_filtered, preds_filtered),
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"f1": f1_score(true_filtered, preds_filtered, average="weighted", zero_division=0),
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"precision": precision_score(true_filtered, preds_filtered, average="weighted", zero_division=0),
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"recall": recall_score(true_filtered, preds_filtered, average="weighted", zero_division=0),
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}
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def main():
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torch.multiprocessing.set_start_method('spawn', force=True)
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print("CUDA available:", torch.cuda.is_available())
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print("CUDA device count:", torch.cuda.device_count())
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texts, labels = load_dataset_from_csv("../../data/classify.csv")
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||||
print("Dataset size:", len(texts))
|
||||
print("Label distribution:", Counter(labels))
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
|
||||
|
||||
train_texts, val_texts, train_labels, val_labels = train_test_split(
|
||||
texts, labels,
|
||||
test_size=0.2,
|
||||
random_state=42,
|
||||
stratify=labels
|
||||
)
|
||||
|
||||
train_dataset = GenerativeTextDataset(train_texts, train_labels, tokenizer)
|
||||
val_dataset = GenerativeTextDataset(val_texts, val_labels, tokenizer)
|
||||
|
||||
data_collator = DataCollatorForSeq2Seq(
|
||||
tokenizer=tokenizer,
|
||||
model=model,
|
||||
padding=True,
|
||||
label_pad_token_id=-100,
|
||||
)
|
||||
|
||||
training_args = Seq2SeqTrainingArguments(
|
||||
output_dir="./results",
|
||||
learning_rate=5e-5,
|
||||
per_device_train_batch_size=16,
|
||||
per_device_eval_batch_size=16,
|
||||
num_train_epochs=10,
|
||||
weight_decay=0.01,
|
||||
eval_strategy="epoch",
|
||||
save_strategy="epoch",
|
||||
load_best_model_at_end=True,
|
||||
metric_for_best_model="f1",
|
||||
greater_is_better=True,
|
||||
predict_with_generate=True,
|
||||
generation_max_length=8,
|
||||
dataloader_num_workers=0,
|
||||
dataloader_pin_memory=False,
|
||||
fp16=False,
|
||||
max_grad_norm=1.0,
|
||||
)
|
||||
|
||||
trainer = Seq2SeqTrainer(
|
||||
model=model,
|
||||
args=training_args,
|
||||
train_dataset=train_dataset,
|
||||
eval_dataset=val_dataset,
|
||||
processing_class=tokenizer,
|
||||
data_collator=data_collator,
|
||||
compute_metrics=lambda ep: compute_metrics_generative(ep, tokenizer),
|
||||
)
|
||||
|
||||
trainer.train()
|
||||
|
||||
metrics = trainer.evaluate()
|
||||
print("\nFinal evaluation metrics:")
|
||||
for k, v in metrics.items():
|
||||
print(f" {k}: {v}")
|
||||
|
||||
trainer.save_model("./flan_classifier")
|
||||
tokenizer.save_pretrained("./flan_classifier")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user