Spaces:
Sleeping
Sleeping
File size: 3,773 Bytes
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | 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 (Updated for Kaggle)
# Change DATA_PATH to point to your uploaded dataset in Kaggle Inputs
DATA_PATH = "/kaggle/input/goemotions-mapped/goemotions_mapped.csv"
MODEL_NAME = "distilbert-base-uncased"
OUTPUT_DIR = "./sentiment_model_trained"
LOG_DIR = "./logs"
# Label mapping (Must match the Mood Music Hub system)
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 Kaggle Training Pipeline for {MODEL_NAME}...")
# Check for GPU
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f">>> 💻 Training Device: {device.upper()}")
# 2. Load and Prepare Data
if not os.path.exists(DATA_PATH):
print(f"!!! Error: Dataset not found at {DATA_PATH}. Make sure to add the CSV to Kaggle Inputs.")
return
df = pd.read_csv(DATA_PATH)
print(f">>> 📊 Dataset loaded: {len(df)} rows.")
df['label'] = df['emotion'].map(EMOTION_MAP)
df = df.dropna(subset=['label'])
df['label'] = df['label'].astype(int)
# Split data (85% Train, 15% Eval)
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
).to(device)
# 5. Training Arguments (Optimized for Kaggle GPU)
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=3,
per_device_train_batch_size=16 if device == "cuda" else 8,
per_device_eval_batch_size=16 if device == "cuda" else 8,
warmup_steps=500,
weight_decay=0.01,
logging_dir=LOG_DIR,
logging_steps=50,
eval_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
metric_for_best_model="eval_loss",
fp16=True if device == "cuda" else False, # Mixed precision for faster GPU training
report_to="none"
)
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...")
trainer.train()
# 8. Save Final Model
print(f">>> 💾 Saving best model to {OUTPUT_DIR}...")
trainer.save_model(OUTPUT_DIR)
tokenizer.save_pretrained(OUTPUT_DIR)
# 9. ZIP the output (Easy to download from Kaggle)
import shutil
shutil.make_archive("sentiment_model_ready", 'zip', OUTPUT_DIR)
print(">>> ✅ ZIP Archive created: sentiment_model_ready.zip")
print(">>> 🎉 Training Complete!")
if __name__ == "__main__":
train()
|