Updated with test code for Google Colab

README.md

@@ -49,4 +49,96 @@ dataset_info:
     num_examples: 33577
   download_size: 6999588
   dataset_size: 24777921
+language:
+- en
 ---
+
+Code to test on Colab
+
+!pip install -q transformers[torch] tokenizers datasets evaluate rouge_score sentencepiece huggingface_hub --upgrade
+
+from huggingface_hub import notebook_login
+
+notebook_login()
+
+import nltk
+from datasets import load_dataset
+import evaluate
+import numpy as np
+from transformers import T5Tokenizer, DataCollatorForSeq2Seq
+from transformers import T5ForConditionalGeneration, Seq2SeqTrainingArguments, Seq2SeqTrainer
+
+# Load and split the dataset
+dataset = load_dataset("ajsbsd/presto")
+dataset = dataset["train"].train_test_split(test_size=0.2)
+#dataset = load_dataset("csv", data_files="./JEOPARDY_CSV.csv")
+#dataset = dataset["train"].train_test_split(test_size=0.2)
+# Load the tokenizer, model, and data collator
+tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")
+model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small")
+data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)
+
+# We prefix our tasks with "answer the question"
+prefix = "answer the question: "
+
+# Define our preprocessing function
+def preprocess_function(examples):
+    """Add prefix to the sentences, tokenize the text, and set the labels"""
+    # The "inputs" are the tokenized answer:
+    inputs = [prefix + doc for doc in examples["inputs"]]
+    model_inputs = tokenizer(inputs, max_length=128, truncation=True)
+
+    # The "labels" are the tokenized outputs:
+    labels = tokenizer(text_target=examples["targets"], max_length=512, truncation=True)
+    model_inputs["labels"] = labels["input_ids"]
+    return model_inputs
+
+# Map the preprocessing function across our dataset
+tokenized_dataset = dataset.map(preprocess_function, batched=True)
+
+# Set up Rouge score for evaluation
+nltk.download("punkt", quiet=True)
+metric = evaluate.load("rouge")
+
+def compute_metrics(eval_preds):
+    preds, labels = eval_preds
+
+    # decode preds and labels
+    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
+    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
+
+    # rougeLSum expects newline after each sentence
+    decoded_preds = ["\n".join(nltk.sent_tokenize(pred.strip())) for pred in decoded_preds]
+    decoded_labels = ["\n".join(nltk.sent_tokenize(label.strip())) for label in decoded_labels]
+
+    result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
+    return result
+
+# Set up training arguments
+training_args = Seq2SeqTrainingArguments(
+    output_dir="./results",
+    evaluation_strategy="epoch",
+    learning_rate=3e-4,
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=4,
+    weight_decay=0.01,
+    save_total_limit=3,
+    num_train_epochs=2,
+    predict_with_generate=True,
+    push_to_hub=False
+)
+
+# Set up trainer
+trainer = Seq2SeqTrainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_dataset["train"],
+    eval_dataset=tokenized_dataset["test"],
+    tokenizer=tokenizer,
+    data_collator=data_collator,
+    compute_metrics=compute_metrics
+)
+
+# Train the model
+trainer.train()