[Tutorials] Add a tutorial for PEFT data curation

This PR adds a new tutorial to demonstrate data curation for PEFT
use-cases.

Signed-off-by: Mehran Maghoumi <Maghoumi@users.noreply.github.com>

tutorials/peft-curation/README.md

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+# Curating Datasets for Parameter Efficient Fine-tuning
+
+This tutorial demonstrates the usage of NeMo Curator's Python API to curate a dataset for
+parameter-efficient fine-tuning (PEFT).
+
+In this tutorial, we use the [Enron Emails dataset](https://huggingface.co/datasets/neelblabla/enron_labeled_emails_with_subjects-llama2-7b_finetuning),
+which is a dataset of emails with corresponding classification labels for each email. Each email has
+a subject, a body and a category (class label). We demonstrate various filtering and processing
+operations that can be applied to each record.
+
+## Usage
+After installing the NeMo Curator package, you can simply run the following command:
+```
+python tutorials/peft-curation/main.py
+```

tutorials/peft-curation/docbuilder.py

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+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import re
+from typing import Set, Tuple
+
+import requests
+
+from nemo_curator.download.doc_builder import (
+    DocumentDownloader,
+    DocumentExtractor,
+    DocumentIterator,
+)
+
+
+class EmailsDownloader(DocumentDownloader):
+    def __init__(self, download_dir: str):
+        super().__init__()
+
+        if not os.path.isdir(download_dir):
+            os.makedirs(download_dir)
+
+        self._download_dir = download_dir
+        print("Download directory: ", self._download_dir)
+
+    def download(self, url: str) -> str:
+        filename = os.path.basename(url)
+        output_file = os.path.join(self._download_dir, filename)
+
+        if os.path.exists(output_file):
+            print(f"File '{output_file}' already exists, skipping download.")
+            return output_file
+
+        print(f"Downloading Enron emails dataset from '{url}'...")
+        response = requests.get(url)
+
+        with open(output_file, "wb") as file:
+            file.write(response.content)
+
+        return output_file
+
+
+class EmailsIterator(DocumentIterator):
+
+    def __init__(self):
+        super().__init__()
+        self._counter = -1
+        self._extractor = EmailsExtractor()
+        # The regular expression pattern to extract each email.
+        self._pattern = re.compile(r'\"<s>.*?<s>\"', re.DOTALL)
+
+    def iterate(self, file_path):
+        self._counter = -1
+        file_name = os.path.basename(file_path)
+
+        with open(file_path, 'r', encoding='utf-8') as file:
+            lines = file.readlines()
+
+        # Ignore the first line which contains the header.
+        file_content = "".join(lines[1:])
+        # Find all the emails in the file.
+        it = self._pattern.finditer(file_content)
+
+        for email in it:
+            self._counter += 1
+            content = email.group().strip('"').strip()
+            meta = {
+                "filename": file_name,
+                "id": f"email-{self._counter}",
+            }
+            extracted_content = self._extractor.extract(content)
+
+            # Skip if no content extracted
+            if not extracted_content:
+                continue
+
+            record = {**meta, **extracted_content}
+            yield record
+
+
+class EmailsExtractor(DocumentExtractor):
+    def __init__(self):
+        super().__init__()
+        # The regular expression pattern to extract subject/body/label into groups.
+        self._pattern = re.compile(r'Subject:: (.*?)\nBody:: (.*?)\n.*\[/INST\] (.*?) <s>', re.DOTALL)
+
+    def extract(self, content: str) -> Tuple[Set, str]:
+        matches = self._pattern.findall(content)
+
+        if not matches:
+            return None
+
+        matches = matches[0]
+
+        return {
+            "subject": matches[0].strip(),
+            "body": matches[1].strip(),
+            "category": matches[2].strip(),
+        }

tutorials/peft-curation/filters.py

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+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from nemo_curator.filters import DocumentFilter
+
+
+class FilterEmailsWithLongBody(DocumentFilter):
+    def __init__(self, max_length: int = 5000):
+        super().__init__()
+        self.max_length = max_length
+
+    def score_document(self, text: str) -> bool:
+        return isinstance(text, str) and len(text) <= self.max_length
+
+    def keep_document(self, score) -> bool:
+        return score
+
+
+class FilterEmptyEmails(DocumentFilter):
+    def score_document(self, text: str) -> bool:
+        return (
+            isinstance(text, str)
+            and (
+                len(text.strip()) > 0
+                or "Empty message" in text
+            )
+        )
+
+    def keep_document(self, score) -> bool:
+        return score

tutorials/peft-curation/main.py

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+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+from functools import partial
+import json
+import os
+from typing import Any
+
+from docbuilder import EmailsDownloader, EmailsIterator
+from filters import FilterEmailsWithLongBody, FilterEmptyEmails
+
+from nemo_curator.utils.distributed_utils import get_client
+from nemo_curator.datasets import DocumentDataset
+from nemo_curator.modules.modify import Modify
+from nemo_curator import ScoreFilter, Sequential
+from nemo_curator.modifiers.pii_modifier import PiiModifier
+from nemo_curator.modifiers.unicode_reformatter import UnicodeReformatter
+from nemo_curator.utils.script_utils import add_distributed_args
+
+
+SCRIPT_DIR_PATH = os.path.dirname(os.path.abspath(__file__))
+DATA_DIR = os.path.join(SCRIPT_DIR_PATH, "data")
+DATASET_URL = "https://huggingface.co/datasets/neelblabla/enron_labeled_emails_with_subjects-llama2-7b_finetuning/raw/main/prompts_train.csv"
+
+
+def download_and_convert_to_jsonl() -> str:
+    """
+    Downloads the emails dataset and converts it to JSONL format.
+
+    Returns:
+        str: The path to the JSONL file.
+    """
+
+    # Download the dataset in raw format and convert it to JSONL.
+    downloader = EmailsDownloader(DATA_DIR)
+    output_path = os.path.join(DATA_DIR, "emails.jsonl")
+    raw_fp = downloader.download(DATASET_URL)
+
+    iterator = EmailsIterator()
+
+    # Parse the raw data and write it to a JSONL file.
+    with open(output_path, 'w') as f:
+        for record in iterator.iterate(raw_fp):
+            json_record = json.dumps(record, ensure_ascii=False)
+            f.write(json_record + "\n")
+
+    return output_path
+
+
+def redact_pii(dataset: DocumentDataset, text_field) -> DocumentDataset:
+    """
+    Redacts personally identifiable information (PII) from a given dataset.
+
+    Args:
+        dataset (DocumentDataset): The dataset containing documents with PII.
+
+    Returns:
+        DocumentDataset: The redacted dataset with PII replaced by a generic value.
+    """
+    redactor = Modify(
+        PiiModifier(
+            supported_entities=[
+                "ADDRESS",
+                "EMAIL_ADDRESS",
+                "LOCATION",
+                "PERSON",
+                "URL",
+                "PHONE_NUMBER",
+            ],
+            anonymize_action="replace",
+            device="cpu",
+        ),
+        text_field=text_field,
+    )
+    return redactor(dataset)
+
+
+def run_curation_pipeline(args: Any, jsonl_fp: str) -> str:
+    """
+    Run the curation pipeline on the dataset.
+
+    Args:
+        args (Any): Command-line arguments.
+        jsonl_fp (str): The path to the uncurated JSONL file.
+
+    Returns:
+        str: The path to the curated JSONL file.
+    """
+    client = get_client(args, args.device)
+    print(f"    Running the curation pipeline on '{jsonl_fp}'...")
+    orig_dataset = DocumentDataset.read_json(jsonl_fp, add_filename=True)
+    dataset = orig_dataset
+
+    # FIXME I wish it was possible to take two columns into account?
+    redact_pii_subject = partial(redact_pii, text_field="subject")
+    redact_pii_body = partial(redact_pii, text_field="body")
+
+    curation_steps = Sequential(
+        [
+            # Unify the text encoding to Unicode.
+            Modify(UnicodeReformatter(), text_field="subject"),
+            Modify(UnicodeReformatter(), text_field="body"),
+            Modify(UnicodeReformatter(), text_field="category"),
+            # Filter out empty emails.
+            ScoreFilter(FilterEmptyEmails(), text_field="subject"),
+            ScoreFilter(FilterEmptyEmails(), text_field="body"),
+            # Filter out emails that are too long.
+            ScoreFilter(FilterEmailsWithLongBody(), text_field="body"),
+            # Redact personally identifiable information (PII).
+            redact_pii_subject,
+            redact_pii_body,
+        ]
+    )
+    dataset = curation_steps(dataset)
+    dataset = dataset.persist()
+
+    print(f"    Original dataset length: {len(orig_dataset.df)}")
+    print(f"    After running the curation pipeline: {len(dataset.df)}")
+    print(f"    Writing to '{jsonl_fp}'...")
+    out_path = os.path.join(
+        os.path.dirname(jsonl_fp),
+        "curated",
+    )
+    os.makedirs(out_path, exist_ok=True)
+    dataset.to_json(out_path, write_to_filename=True)
+    client.close()
+    return os.path.join(out_path, os.path.basename(jsonl_fp))
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser = add_distributed_args(parser)
+    args = parser.parse_args()
+    # Limit the total number of workers to ensure we don't run out of memory.
+    args.n_workers = min(args.n_workers, 1)
+
+    # Prepare the download and JSONL directories.
+    if not os.path.isdir(DATA_DIR):
+        os.makedirs(DATA_DIR)
+
+    jsonl_fp = download_and_convert_to_jsonl()
+    run_curation_pipeline(args, jsonl_fp)
+
+
+if __name__ == "__main__":
+    main()

tutorials/tinystories/README.md

@@ -1,6 +1,6 @@
 # TinyStories
 
-This tutorial demonstrates the usage of NeMo Curator's Python API to curate the [TinyStories](https://arxiv.org/abs/2305.07759) dataset. TinyStories is a dataset of short stories generated by GPT-3.5 and GPT-4, featuring words that are undersood by 3 to 4-year olds. The small size of this dataset makes it ideal for creating and validating data curation pipelines on a local machine.
+This tutorial demonstrates the usage of NeMo Curator's Python API to curate the [TinyStories](https://arxiv.org/abs/2305.07759) dataset. TinyStories is a dataset of short stories generated by GPT-3.5 and GPT-4, featuring words that are understood by 3 to 4-year olds. The small size of this dataset makes it ideal for creating and validating data curation pipelines on a local machine.
 
 For simplicity, this tutorial uses the validation split of this dataset, which contains around 22,000 samples.