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59b54d3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | import os
import pandas as pd
import torch
from sklearn.model_selection import train_test_split
from transformers import (
AutoTokenizer,
AutoModelForSequenceClassification,
TrainingArguments,
Trainer,
DataCollatorWithPadding
)
from datasets import Dataset
# 1. Configuration
DATA_PATH = "data/goemotions_mapped.csv"
MODEL_NAME = "distilbert-base-uncased"
OUTPUT_DIR = "models/sentiment_model"
LOG_DIR = "./logs"
# Label mapping to ensure consistency
EMOTION_MAP = {
'Neutral': 0,
'Happy': 1,
'Sad': 2,
'Angry': 3,
'Fearful': 4
}
ID_TO_EMOTION = {v: k for k, v in EMOTION_MAP.items()}
def train():
print(f">>> 🚀 Starting Training Pipeline for {MODEL_NAME}...")
# 2. Load and Prepare Data
if not os.path.exists(DATA_PATH):
raise FileNotFoundError(f"Dataset not found at {DATA_PATH}")
df = pd.read_csv(DATA_PATH)
print(f">>> 📊 Dataset loaded: {len(df)} rows.")
# Convert labels to IDs
df['label'] = df['emotion'].map(EMOTION_MAP)
# Drop any rows with unmapped emotions if they exist
df = df.dropna(subset=['label'])
df['label'] = df['label'].astype(int)
# Split data
train_df, val_df = train_test_split(df, test_size=0.15, random_state=42, stratify=df['label'])
train_dataset = Dataset.from_pandas(train_df)
val_dataset = Dataset.from_pandas(val_df)
# 3. Tokenization
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
def tokenize_func(examples):
return tokenizer(examples['text'], truncation=True, padding=True, max_length=128)
print(">>> 🔤 Tokenizing dataset...")
train_dataset = train_dataset.map(tokenize_func, batched=True)
val_dataset = val_dataset.map(tokenize_func, batched=True)
# 4. Model Initialization
model = AutoModelForSequenceClassification.from_pretrained(
MODEL_NAME,
num_labels=len(EMOTION_MAP),
id2label=ID_TO_EMOTION,
label2id=EMOTION_MAP
)
# 5. Training Arguments
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=3,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
warmup_steps=500,
weight_decay=0.01,
logging_dir=LOG_DIR,
logging_steps=100,
eval_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
metric_for_best_model="eval_loss",
report_to="none" # Disable wandb etc for local runs
)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
# 6. Trainer API
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=val_dataset,
tokenizer=tokenizer,
data_collator=data_collator
)
# 7. Execute Training
print(">>> 🏋️ Training model (this may take a while)...")
trainer.train()
# 8. Save Final Model
print(f">>> 💾 Saving best model to {OUTPUT_DIR}...")
trainer.save_model(OUTPUT_DIR)
tokenizer.save_pretrained(OUTPUT_DIR)
print(">>> ✅ Training Complete!")
if __name__ == "__main__":
# Ensure directories exist
os.makedirs(os.path.dirname(OUTPUT_DIR), exist_ok=True)
train()
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