Mirror smolinstruct eval data (hf)

.gitignore

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+.DS_Store
+*.ipynb

SMolInstruct.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+"""SMolInstruct: A Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset for Small Molecules"""
+
+
+import json
+import os
+
+import datasets
+
+
+# Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@article{yu2024llasmol,
+    title={LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset},
+    author={Botao Yu and Frazier N. Baker and Ziqi Chen and Xia Ning and Huan Sun},
+    journal={arXiv preprint arXiv:2402.09391},
+    year={2024}
+}
+"""
+
+# Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+SMolInstruct is a large-scale instruction tuning dataset for chemistry tasks and centers around small molecules. It contains a total of 14 chemistry tasks and over 3 million samples. It is designed to be large-scale, comprehensive, and high-quality.
+"""
+
+# Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://osu-nlp-group.github.io/LLM4Chem/"
+
+# Add the licence for the dataset here if you can find it
+_LICENSE = "cc-by-4.0"
+
+# Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+# _URLS = {
+#     "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
+#     "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
+# }
+
+
+TASKS = (
+    'forward_synthesis',
+    'retrosynthesis',
+    'molecule_captioning',
+    'molecule_generation',
+    'name_conversion-i2f',
+    'name_conversion-i2s',
+    'name_conversion-s2f',
+    'name_conversion-s2i',
+    'property_prediction-esol',
+    'property_prediction-lipo',
+    'property_prediction-bbbp',
+    'property_prediction-clintox',
+    'property_prediction-hiv',
+    'property_prediction-sider',
+)
+
+
+class SmolInstructDatasetConfig(datasets.BuilderConfig):
+    def __init__(self, tasks=None, sample_group='instruction_tuning', insert_core_tags=True, use_selfies=False, use_test_subset=False, use_first=None, **kwargs):
+        """BuilderConfig for MyDataset
+        Args:
+          data_url: `string`, url to the dataset (word or raw level)
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SmolInstructDatasetConfig, self).__init__(
+            **kwargs,
+        )
+        if tasks is None:
+            tasks = TASKS
+        else:
+            tasks = set(tasks)
+            all_tasks = set(TASKS)
+            assert len(tasks - all_tasks) == 0, 'Unsupported task(s): {tasks}'.format(tasks=(tasks - all_tasks))
+        self.tasks = tasks
+        self.sample_group = sample_group
+        self.insert_core_tags = insert_core_tags
+        self.use_selfies = use_selfies
+        if 'split' in kwargs:
+            self.split = kwargs['split']
+        else:
+            self.split = None
+        self.use_test_subset = use_test_subset
+        if use_first is not None:
+            assert use_first > 0, "use_first must be a positive integer."
+            use_first = int(use_first)
+        self.use_first = use_first
+
+
+class SMolInstruct(datasets.GeneratorBasedBuilder):
+    """SMolInstruct: A large-scale chemistry instruction tuning dataset."""
+
+    VERSION = datasets.Version("1.3.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    BUILDER_CONFIG_CLASS = SmolInstructDatasetConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    # BUILDER_CONFIGS = [
+    #     datasets.BuilderConfig(name="instruction_tuning", version=VERSION, description="Default set for instruction tuning."),
+    #     datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
+    # ]
+    # BUILDER_CONFIGS = [
+    #     CheMIDatasetConfig(
+    #         name='instruction',
+    #         tasks=TASKS,
+    #         sample_group='instruction_tuning',
+    #         description="Molecule instructions.",
+    #     ),
+    #     CheMIDatasetConfig(
+    #         name='raw',
+    #         tasks=TASKS,
+    #         sample_group=None,
+    #         description="Molecule raw data.",
+    #     ),
+    # ]
+
+    # DEFAULT_CONFIG_NAME = "instruction"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+
+        features = datasets.Features(
+            {
+                "sample_id": datasets.Value("string"),
+                "input": datasets.Value("string"),
+                "output": datasets.Value("string"),
+                "raw_input": datasets.Value("string"),
+                "raw_output": datasets.Value("string"),
+                "split": datasets.Value("string"),
+                "task": datasets.Value("string"),
+                'input_core_tag_left': datasets.Value("string"),
+                'input_core_tag_right': datasets.Value("string"),
+                'output_core_tag_left': datasets.Value("string"),
+                'output_core_tag_right': datasets.Value("string"),
+                'target': datasets.Value("string"),
+            }
+        )
+        
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        # urls = _URLS[self.config.name]
+        root = dl_manager.download_and_extract('./data.zip')
+
+        sample_group = self.config.sample_group
+        insert_core_tags = self.config.insert_core_tags
+        use_selfies = self.config.use_selfies
+        use_test_subset = self.config.use_test_subset
+        use_first = self.config.use_first
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "root": root,
+                    "sample_group": sample_group,
+                    "split": "train",
+                    "tasks": self.config.tasks,
+                    "insert_core_tags": insert_core_tags,
+                    "use_selfies": use_selfies,
+                    "use_test_subset": False,
+                    "use_first": use_first,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "root": root,
+                    "sample_group": sample_group,
+                    "split": "dev",
+                    "tasks": self.config.tasks,
+                    "insert_core_tags": insert_core_tags,
+                    "use_selfies": use_selfies,
+                    "use_test_subset": False,
+                    "use_first": use_first,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "root": root,
+                    "sample_group": sample_group,
+                    "split": "test",
+                    "tasks": self.config.tasks,
+                    "insert_core_tags": insert_core_tags,
+                    "use_selfies": use_selfies,
+                    "use_test_subset": use_test_subset,
+                    "use_first": use_first,
+                },
+            ),
+        ]
+    
+    def _generate_instruction_examples(self, root, sample_group, split, tasks, insert_core_tags, use_selfies, use_test_subset, use_first):
+        if split == 'test' and use_test_subset is True:
+            real_split = 'test_subset'
+        else:
+            real_split = split
+
+        for task in tasks:
+            with open(os.path.join(root, 'sample', sample_group, real_split, task + '.json'), 'r') as fs:
+                sample_record = json.load(fs)
+                assert sample_record['task'] == task, (sample_record['task'], task, os.path.join(root, 'sample', sample_group, real_split, task + '.json'))
+                assert sample_record['split'] == real_split
+                template_name = sample_record['template_name']
+                samples = sample_record['samples']
+
+            with open(os.path.join(root, 'template', template_name, task + '.json'), 'r') as f:
+                templates = json.load(f)
+            if use_selfies:
+                for template in templates:
+                    input_template = template['input']
+                    output_template = template['output']
+                    input_template = input_template.replace("SMILES", "SELFIES")
+                    output_template = output_template.replace("SMILES", "SELFIES")
+                    template['input'] = input_template
+                    template['output'] = output_template
+
+            data = []
+            with open(os.path.join(root, 'raw_selfies' if use_selfies else 'raw', split, task + '.jsonl'), 'r') as fr:
+                for line in fr:
+                    item = json.loads(line)
+                    data.append(item)
+
+            with open(os.path.join(root, 'core_tag', task + '.json'), 'r') as f:
+                core_tags = json.load(f)
+            input_core_tag_left = core_tags['input'][0]
+            input_core_tag_right = core_tags['input'][1]
+            if use_selfies and input_core_tag_left == '<SMILES>':
+                assert input_core_tag_right == '</SMILES>'
+                input_core_tag_left = '<SELFIES>'
+                input_core_tag_right = '</SELFIES>'
+            output_core_tag_left = core_tags['output'][0]
+            output_core_tag_right = core_tags['output'][1]
+            if use_selfies and output_core_tag_left == '<SMILES>':
+                assert output_core_tag_right == '</SMILES>'
+                output_core_tag_left = '<SELFIES>'
+                output_core_tag_right = '</SELFIES>'
+            
+            for sample_item in (samples if use_first is None else samples[:use_first]):
+                try:
+                    data_item = data[sample_item['idx']]
+                except IndexError:
+                    raise IndexError('In %s for %s, data index exceeds the number of samples. The data size is %d, while the index is %d.' % (real_split, task, len(data), sample_item['idx']))
+                assert data_item['task'] == task
+                assert data_item['split'] == split
+                template_id = sample_item['template_id']
+                template = templates[template_id]
+                input_template = template['input']
+                output_template = template['output']
+                input_data = data_item['input']
+                if insert_core_tags and input_core_tag_left is not None:
+                    assert input_core_tag_right is not None
+                    input_data_str = '%s %s %s' % (input_core_tag_left, input_data, input_core_tag_right)
+                else:
+                    input_data_str = input_data
+                input_str = input_template.replace('<INPUT>', input_data_str)
+                output_data = data_item['output']
+                if isinstance(output_data, str):
+                    target = None
+                elif isinstance(output_data, dict):
+                    target = sample_item['target']
+                    output_data = output_data[target]
+                else:
+                    raise ValueError
+                if insert_core_tags and output_core_tag_left is not None:
+                    assert output_core_tag_right is not None
+                    output_data_str = '%s %s %s' % (output_core_tag_left, output_data, output_core_tag_right)
+                else:
+                    output_data_str = output_data
+                output_str = output_template.replace('<OUTPUT>', output_data_str)
+                sample_id = '%s.%s.%d.%s' % (task, split, sample_item['idx'], str(target))
+                output_sample = {
+                    'sample_id': sample_id,
+                    'input': input_str,
+                    'output': output_str,
+                    'raw_input': input_data,
+                    'raw_output': output_data,
+                    'task': task,
+                    'split': real_split,
+                    'input_core_tag_left': input_core_tag_left,
+                    'input_core_tag_right': input_core_tag_right,
+                    'output_core_tag_left': output_core_tag_left,
+                    'output_core_tag_right': output_core_tag_right,
+                    'target': target,
+                }
+                yield sample_id, output_sample
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, *args, **kwargs):
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+
+        return self._generate_instruction_examples(*args, **kwargs)

data.zip

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