Create generate_from_starcoder.py

generate_from_starcoder.py

@@ -0,0 +1,44 @@
+from datasets import load_dataset, concatenate_datasets, Value, Features
+from transformers import GPT2Tokenizer
+
+
+new_features = Features({
+    'max_stars_repo_path': Value('string'),
+    'max_stars_repo_name': Value('string'),
+    'max_stars_count': Value('int64'),  # Ensure it is declared as int64
+    'id': Value('string'),
+    'content': Value('string')
+})
+
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+
+def count_tokens(row_data):
+    return {"n_tokens": len(tokenizer(row_data["content"])["input_ids"])}
+
+# Load subset in common programming language and JSON
+dc = load_dataset("bigcode/starcoderdata", data_dir="c", split="train").cast(new_features) #float
+dcpp = load_dataset("bigcode/starcoderdata", data_dir="cpp", split="train").cast(new_features) #float
+dpython = load_dataset("bigcode/starcoderdata", data_dir="python", split="train")
+djson = load_dataset("bigcode/starcoderdata", data_dir="json", split="train")
+djava = load_dataset("bigcode/starcoderdata", data_dir="java", split="train")
+
+# Remove the fields that we don't want
+seed = 42
+aggregated_dataset = concatenate_datasets([dc, dpython, dcpp, djson, djava])
+aggregated_dataset = aggregated_dataset.remove_columns(["id", "max_stars_repo_path", "max_stars_repo_name"])
+aggregated_dataset = aggregated_dataset.shuffle(seed=seed)
+
+# Filter with star
+qualified_subset = aggregated_dataset.filter(lambda x: x["max_stars_count"] > 300, num_proc=16)
+
+# Reduce the size
+n_sample = min(2_500_000, qualified_subset.num_rows)
+target_dataset = qualified_subset.shuffle(seed=seed).select(range(n_sample))
+
+# Add "n_tokens" field
+target_train_dataset = target_dataset['train'].map(count_tokens, num_proc=16)
+total_tokens = sum(target_train_dataset["n_tokens"])
+
+# Save dataset in parquet
+target_dataset_dir = "/data/filtered_starcoder"
+target_train_dataset.to_parquet(target_dataset_dir)