Create app.py

app.py

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+# -*- coding: utf-8 -*-
+"""text_summarization_finetune.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1DC3LFNnBCIfmnKp8DvUFF8Q2FIhwc7RP
+
+# Text Summarization — Fine-tuning T5-small on CNN/DailyMail
+
+**Dataset:** `cnn_dailymail` (v3.0.0) — news articles with human-written highlights (summaries)
+
+**Model:** `t5-small` — lightweight encoder-decoder model, good fit for Colab's free GPU
+
+**Steps:**
+1. Install libraries
+2. Load & explore dataset
+3. Load tokenizer & model
+4. Preprocess (tokenize) data
+5. Set up training (Seq2SeqTrainer)
+6. Train
+7. Evaluate with ROUGE
+8. Run inference on a custom example
+9. Save & (optionally) push the model
+
+> Tip: In Colab go to **Runtime > Change runtime type > T4 GPU** before running.
+
+## 1. Install libraries
+"""
+
+!pip install -q transformers datasets evaluate rouge_score accelerate sentencepiece
+
+!pip install -q -U datasets huggingface_hub transformers
+
+"""## 2. Load & explore the dataset"""
+
+from datasets import load_dataset
+raw_datasets = load_dataset("abisee/cnn_dailymail", "3.0.0")
+
+train_dataset = raw_datasets["train"].shuffle(seed=42).select(range(3000))
+val_dataset   = raw_datasets["validation"].shuffle(seed=42).select(range(300))
+test_dataset  = raw_datasets["test"].shuffle(seed=42).select(range(300))
+
+print(train_dataset)
+print(train_dataset[0]["article"][:500])
+print("\n--- Summary ---")
+print(train_dataset[0]["highlights"])
+
+"""## 3. Load tokenizer & model"""
+
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+model_checkpoint = "t5-small"
+
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
+
+prefix = "summarize: "
+
+"""## 4. Preprocess (tokenize) the data"""
+
+max_input_length = 512
+max_target_length = 128
+
+def preprocess_function(examples):
+    inputs = [prefix + doc for doc in examples["article"]]
+    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)
+
+    labels = tokenizer(text_target=examples["highlights"], max_length=max_target_length, truncation=True)
+
+    model_inputs["labels"] = labels["input_ids"]
+    return model_inputs
+
+tokenized_train = train_dataset.map(preprocess_function, batched=True, remove_columns=train_dataset.column_names)
+tokenized_val   = val_dataset.map(preprocess_function, batched=True, remove_columns=val_dataset.column_names)
+tokenized_test  = test_dataset.map(preprocess_function, batched=True, remove_columns=test_dataset.column_names)
+
+"""## 5. Set up training"""
+
+import numpy as np
+import evaluate
+from transformers import DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer
+
+data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)
+
+rouge = evaluate.load("rouge")
+
+def compute_metrics(eval_pred):
+    predictions, labels = eval_pred
+    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)
+
+    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
+
+    result = rouge.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
+    result = {k: round(v * 100, 2) for k, v in result.items()}
+
+    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in predictions]
+    result["gen_len"] = round(np.mean(prediction_lens), 2)
+    return result
+
+training_args = Seq2SeqTrainingArguments(
+    output_dir="./t5-summarization-cnn",
+    eval_strategy="epoch",
+    save_strategy="epoch",
+    learning_rate=3e-4,
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=8,
+    weight_decay=0.01,
+    save_total_limit=2,
+    num_train_epochs=3,
+    predict_with_generate=True,
+    fp16=True,
+    logging_steps=50,
+    report_to="none",
+)
+
+trainer = Seq2SeqTrainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_train,
+    eval_dataset=tokenized_val,
+    data_collator=data_collator,
+    compute_metrics=compute_metrics,
+)
+
+"""## 6. Train"""
+
+trainer.train()
+
+"""## 7. Evaluate on the test set"""
+
+test_results = trainer.predict(tokenized_test)
+print(test_results.metrics)
+
+"""## 8. Try it on a custom example"""
+
+def summarize(text, max_length=128):
+    inputs = tokenizer(prefix + text, return_tensors="pt", truncation=True, max_length=max_input_length).to(model.device)
+    summary_ids = model.generate(
+        **inputs,
+        max_length=max_length,
+        num_beams=4,
+        length_penalty=2.0,
+        early_stopping=True,
+    )
+    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+sample_article = test_dataset[0]["article"]
+print("Original article:\n", sample_article[:800])
+print("\nReference summary:\n", test_dataset[0]["highlights"])
+print("\nModel summary:\n", summarize(sample_article))
+
+"""## 9. Save the model (and optionally push to Hugging Face Hub)"""
+
+save_dir = "./t5-summarization-cnn-final"
+trainer.save_model(save_dir)
+tokenizer.save_pretrained(save_dir)
+print("Model saved to", save_dir)
+
+"""## Notes & next steps
+- **Scaling up:** increase `train_dataset`/`val_dataset` sizes and `num_train_epochs` for better ROUGE scores (full dataset training takes hours even on T4 — good for a final run, not quick iteration).
+- **Bigger model:** swap `t5-small` for `t5-base`, `facebook/bart-base`, or `sshleifer/distilbart-cnn-12-6` if you have more GPU memory/time.
+- **Different domain:** swap the dataset for `samsum` (dialogue summarization) or `xsum` (very short summaries) by changing the `load_dataset(...)` call and the column names (`dialogue`/`summary` for samsum).
+"""