File size: 3,448 Bytes
11a0fc5 | 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 | from datasets import load_dataset, Dataset
from transformers import (
AutoTokenizer, AutoModelForSequenceClassification,
TrainingArguments, Trainer
)
import torch
import numpy as np
from sklearn.metrics import accuracy_score, f1_score
import pandas as pd
MODEL_BASE = "google/muril-base-cased"
OUTPUT_DIR = "./muril-sentimix"
NUM_LABELS = 3
LABEL2ID = {"negative": 0, "neutral": 1, "positive": 2}
ID2LABEL = {0: "Negative", 1: "Neutral", 2: "Positive"}
# ── Load dataset ──────────────────────────────────────────────
raw = load_dataset("AmaanP314/youtube-comment-sentiment")["train"]
# Rename and encode columns
df = raw.to_pandas()[["CommentText", "Sentiment"]].dropna()
df = df.rename(columns={"CommentText": "text"})
df["labels"] = df["Sentiment"].str.lower().map(LABEL2ID)
df = df.dropna(subset=["labels"])
df["labels"] = df["labels"].astype(int)
# Sample 100k to keep training fast (remove cap if you have GPU + time)
df = df.sample(n=min(100_000, len(df)), random_state=42).reset_index(drop=True)
split = Dataset.from_pandas(df[["text", "labels"]]).train_test_split(test_size=0.1, seed=42)
# ── Tokenizer ─────────────────────────────────────────────────
tokenizer = AutoTokenizer.from_pretrained(MODEL_BASE)
def tokenize(batch):
return tokenizer(batch["text"], truncation=True, max_length=128, padding="max_length")
split = split.map(tokenize, batched=True)
split.set_format("torch", columns=["input_ids", "attention_mask", "labels"])
# ── Model ─────────────────────────────────────────────────────
model = AutoModelForSequenceClassification.from_pretrained(
MODEL_BASE, num_labels=NUM_LABELS, id2label=ID2LABEL, label2id={v: k for k, v in ID2LABEL.items()}
)
# ── Metrics ───────────────────────────────────────────────────
def compute_metrics(eval_pred):
logits, labels = eval_pred
preds = np.argmax(logits, axis=-1)
return {
"accuracy": accuracy_score(labels, preds),
"f1": f1_score(labels, preds, average="weighted")
}
# ── Training args ─────────────────────────────────────────────
args = TrainingArguments(
output_dir=OUTPUT_DIR,
num_train_epochs=4,
per_device_train_batch_size=16,
per_device_eval_batch_size=32,
evaluation_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
metric_for_best_model="f1",
learning_rate=2e-5,
warmup_ratio=0.1,
weight_decay=0.01,
fp16=torch.cuda.is_available(),
logging_steps=50,
report_to="none",
)
# ── Train ─────────────────────────────────────────────────────
trainer = Trainer(
model=model,
args=args,
train_dataset=split["train"],
eval_dataset=split["test"],
compute_metrics=compute_metrics,
)
trainer.train()
trainer.save_model(OUTPUT_DIR)
tokenizer.save_pretrained(OUTPUT_DIR)
print(f"Model saved to {OUTPUT_DIR}")
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