StarkQA-PrimeKG Loader¶

In this tutorial, we will explain how to load the StarkQA-PrimeKG dataset, which is a dataset for question answering over knowledge graphs of PrimeKG.

Prior information about the StarkQA-PrimeKG dataset can be found in the following repositories:

• https://github.com/snap-stanford/stark
• https://stark.stanford.edu/
• https://huggingface.co/datasets/snap-stanford/stark

First of all, we need to import the necessary libraries as follows.

In [1]:

# Import necessary libraries
import sys

sys.path.append("../../..")
from aiagents4pharma.talk2knowledgegraphs.datasets.starkqa_primekg import StarkQAPrimeKG

# Import necessary libraries import sys sys.path.append("../../..") from aiagents4pharma.talk2knowledgegraphs.datasets.starkqa_primekg import StarkQAPrimeKG

c:\Users\mulyadi\Repo\AIAgents4Pharma\venv\Lib\site-packages\pydantic\_internal\_fields.py:132: UserWarning: Field "model_id" in SysBioModel has conflict with protected namespace "model_".

You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.
  warnings.warn(
c:\Users\mulyadi\Repo\AIAgents4Pharma\venv\Lib\site-packages\pydantic\_internal\_fields.py:132: UserWarning: Field "model_id" in BasicoModel has conflict with protected namespace "model_".

You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.
  warnings.warn(
c:\Users\mulyadi\Repo\AIAgents4Pharma\venv\Lib\site-packages\pydantic\_internal\_fields.py:132: UserWarning: Field "model_data" in SimulateModelInput has conflict with protected namespace "model_".

You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.
  warnings.warn(

Load StarQA-PrimeKG¶

The StarkQAPrimeKG allows loading the data from the HuggingFace Hub if the data is not available locally.

Otherwise, the data is loaded from the local directory as defined in the local_dir.

In [2]:

# Define starkqa primekg data by providing a local directory where the data is stored
starkqa_data = StarkQAPrimeKG(local_dir="../../../../data/starkqa_primekg/")

# Define starkqa primekg data by providing a local directory where the data is stored starkqa_data = StarkQAPrimeKG(local_dir="../../../../data/starkqa_primekg/")

To load the dataframes of StarkQA and its split, we just need a method as follows.

In [3]:

# Invoke a method to load the data
starkqa_data.load_data()

# Get the StarkQAPrimeKG data, which are the QA pairs, split indices, and the node information
starkqa_df = starkqa_data.get_starkqa()
starkqa_split_indices = starkqa_data.get_starkqa_split_indicies()
starkqa_node_info = starkqa_data.get_starkqa_node_info()
query_embeddings = starkqa_data.get_query_embeddings()
node_embeddings = starkqa_data.get_node_embeddings()

# Invoke a method to load the data starkqa_data.load_data() # Get the StarkQAPrimeKG data, which are the QA pairs, split indices, and the node information starkqa_df = starkqa_data.get_starkqa() starkqa_split_indices = starkqa_data.get_starkqa_split_indicies() starkqa_node_info = starkqa_data.get_starkqa_node_info() query_embeddings = starkqa_data.get_query_embeddings() node_embeddings = starkqa_data.get_node_embeddings()

Loading StarkQAPrimeKG dataset...
Downloading files from snap-stanford/stark

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qa/prime/split/train.index:   0%|          | 0.00/30.9k [00:00<?, ?B/s]

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Loading StarkQAPrimeKG embeddings...
Downloading embeddings from text-embedding-ada-002

Downloading...
From (original): https://drive.google.com/uc?id=1MshwJttPZsHEM2cKA5T13SIrsLeBEdyU
From (redirected): https://drive.google.com/uc?id=1MshwJttPZsHEM2cKA5T13SIrsLeBEdyU&confirm=t&uuid=ae35d8f3-b8d6-454d-bb67-f8d73223508d
To: c:\Users\mulyadi\Repo\data\starkqa_primekg_test\text-embedding-ada-002\query\query_emb_dict.pt
100%|██████████| 72.0M/72.0M [00:02<00:00, 31.3MB/s]
Downloading...
From (original): https://drive.google.com/uc?id=16EJvCMbgkVrQ0BuIBvLBp-BYPaye-Edy
From (redirected): https://drive.google.com/uc?id=16EJvCMbgkVrQ0BuIBvLBp-BYPaye-Edy&confirm=t&uuid=a9359c11-415b-40f2-a72e-ab25fcd5372b
To: c:\Users\mulyadi\Repo\data\starkqa_primekg_test\text-embedding-ada-002\doc\candidate_emb_dict.pt
100%|██████████| 832M/832M [00:34<00:00, 24.1MB/s] 
c:\Users\mulyadi\Repo\AIAgents4Pharma\docs\notebooks\talk2knowledgegraphs\../../..\aiagents4pharma\talk2knowledgegraphs\datasets\starkqa_primekg.py:140: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  query_emb_dict = torch.load(query_emb_path)
c:\Users\mulyadi\Repo\AIAgents4Pharma\docs\notebooks\talk2knowledgegraphs\../../..\aiagents4pharma\talk2knowledgegraphs\datasets\starkqa_primekg.py:141: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  node_emb_dict = torch.load(node_emb_path)

Check StarQA-PrimeKG Dataframe¶

StarkQA dataframes contains the following columns:

• id: Unique identifier for each question and answer pair
• query: The synthesized question from the StarkQA dataset
• answer_ids: The unique identifier for the answer to the question (multiple answers are possible)

In [5]:

# Check a sample of the starkqa primekg dataframe
starkqa_df.head()

# Check a sample of the starkqa primekg dataframe starkqa_df.head()

Out[5]:

	id	query	answer_ids
0	0	Could you identify any skin diseases associate...	[95886]
1	1	What drugs target the CYP3A4 enzyme and are us...	[15450]
2	2	What is the name of the condition characterize...	[98851, 98853]
3	3	What drugs are used to treat epithelioid sarco...	[15698]
4	4	Can you supply a compilation of genes and prot...	[7161, 22045]

The current version of StarkQA-PrimeKG has about 11K records of question-and-answer pairs.

In [6]:

# Check dimensions of the starkqa primekg dataframe
starkqa_df.shape

# Check dimensions of the starkqa primekg dataframe starkqa_df.shape

Out[6]:

(11204, 3)

Check StarkQA-PrimeKG Node Information¶

StarkQA provides additional node information for PrimeKG as a dictionary for each node.

This allows us to further enrich the features of the knowledge graph nodes.

In [7]:

# Check the node information of PrimeKG
starkqa_node_info[0]

# Check the node information of PrimeKG starkqa_node_info[0]

Out[7]:

{'id': 9796,
 'type': 'gene/protein',
 'name': 'PHYHIP',
 'source': 'NCBI',
 'details': {'query': 'PHYHIP',
  '_id': '9796',
  '_score': 17.934021,
  'alias': ['DYRK1AP3', 'PAHX-AP', 'PAHXAP1'],
  'genomic_pos': {'chr': '8',
   'end': 22232101,
   'ensemblgene': 'ENSG00000168490',
   'start': 22219703,
   'strand': -1},
  'name': 'phytanoyl-CoA 2-hydroxylase interacting protein',
  'summary': 'Enables protein tyrosine kinase binding activity. Involved in protein localization. Located in cytoplasm. [provided by Alliance of Genome Resources, Apr 2022]'}}

Check StarQA-PrimeKG Embeddings (Query & Nodes)¶

Note that StarkQA has provided pre-loaded embeddings for the queries and nodes using the 'text-embedding-ada-002' model.

In [8]:

# Check query embeddings
query_embeddings

# Check query embeddings query_embeddings

Out[8]:

{3461: tensor([[-0.0211, -0.0149,  0.0020,  ..., -0.0277, -0.0085, -0.0034]]),
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 8931: tensor([[-0.0299, -0.0173,  0.0056,  ..., -0.0206, -0.0150, -0.0108]]),
 6664: tensor([[-0.0368,  0.0045,  0.0026,  ..., -0.0055, -0.0094, -0.0178]]),
 3798: tensor([[ 0.0009, -0.0095,  0.0231,  ..., -0.0197, -0.0106, -0.0288]]),
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 11049: tensor([[-0.0303, -0.0067,  0.0083,  ..., -0.0103, -0.0154, -0.0345]]),
 282: tensor([[-0.0283, -0.0122,  0.0196,  ..., -0.0083, -0.0086, -0.0130]]),
 6202: tensor([[-0.0193,  0.0041,  0.0195,  ..., -0.0260,  0.0192, -0.0293]]),
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 301: tensor([[-0.0389, -0.0161,  0.0094,  ...,  0.0053, -0.0084,  0.0028]]),
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 5157: tensor([[-0.0083,  0.0038,  0.0484,  ..., -0.0021, -0.0126, -0.0264]]),
 1893: tensor([[-0.0167, -0.0300,  0.0212,  ..., -0.0049,  0.0103, -0.0332]]),
 9715: tensor([[-0.0421,  0.0096, -0.0070,  ..., -0.0183, -0.0085, -0.0240]]),
 5035: tensor([[ 0.0135, -0.0152,  0.0196,  ...,  0.0002, -0.0172, -0.0211]]),
 7840: tensor([[-0.0206, -0.0075,  0.0228,  ..., -0.0066, -0.0016, -0.0297]]),
 263: tensor([[-0.0455, -0.0012, -0.0076,  ..., -0.0150, -0.0115, -0.0194]]),
 8262: tensor([[-0.0040, -0.0248,  0.0245,  ..., -0.0117, -0.0179,  0.0021]]),
 4542: tensor([[-0.0264, -0.0104,  0.0091,  ..., -0.0264, -0.0253, -0.0286]]),
 1747: tensor([[-0.0442, -0.0188, -0.0050,  ..., -0.0126,  0.0088, -0.0022]]),
 2591: tensor([[-0.0181, -0.0045,  0.0056,  ..., -0.0131, -0.0238, -0.0094]]),
 11195: tensor([[ 0.0031,  0.0058,  0.0209,  ..., -0.0231, -0.0199, -0.0149]]),
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 7794: tensor([[-0.0130, -0.0068,  0.0004,  ..., -0.0010,  0.0005, -0.0131]]),
 5108: tensor([[-0.0207, -0.0108, -0.0030,  ..., -0.0177, -0.0030, -0.0309]]),
 9628: tensor([[-0.0342, -0.0105, -0.0038,  ...,  0.0036, -0.0090, -0.0261]]),
 2881: tensor([[ 1.4016e-02,  8.4547e-03,  2.4417e-02,  ...,  3.1906e-05,
          -1.8790e-02, -2.5777e-02]]),
 6027: tensor([[-0.0382, -0.0027, -0.0081,  ..., -0.0082, -0.0094, -0.0283]]),
 4852: tensor([[-0.0060, -0.0109,  0.0152,  ..., -0.0127,  0.0011, -0.0110]]),
 10626: tensor([[-0.0292,  0.0069,  0.0047,  ..., -0.0240,  0.0077, -0.0296]]),
 3222: tensor([[-0.0063, -0.0075,  0.0018,  ..., -0.0137, -0.0174, -0.0066]]),
 7550: tensor([[-0.0225,  0.0103,  0.0101,  ...,  0.0034, -0.0151, -0.0197]]),
 2755: tensor([[-0.0193,  0.0010,  0.0057,  ..., -0.0279, -0.0138, -0.0026]]),
 1583: tensor([[-0.0216, -0.0035,  0.0238,  ..., -0.0374, -0.0145, -0.0165]]),
 4786: tensor([[-0.0413,  0.0099, -0.0013,  ..., -0.0176, -0.0098, -0.0242]]),
 2637: tensor([[-1.6846e-02, -3.6519e-06,  2.3528e-02,  ..., -2.7720e-02,
          -1.8667e-02, -3.9210e-02]]),
 7432: tensor([[-0.0206,  0.0204,  0.0211,  ..., -0.0160, -0.0083, -0.0101]]),
 1718: tensor([[-0.0133,  0.0006,  0.0037,  ..., -0.0130, -0.0145, -0.0342]]),
 10890: tensor([[-0.0243, -0.0007,  0.0012,  ..., -0.0279, -0.0246, -0.0290]]),
 5393: tensor([[-0.0161, -0.0028,  0.0005,  ..., -0.0210, -0.0173, -0.0065]]),
 3340: tensor([[-0.0058,  0.0045,  0.0141,  ..., -0.0126, -0.0266, -0.0447]]),
 10744: tensor([[-0.0087,  0.0032,  0.0126,  ..., -0.0145,  0.0010, -0.0235]]),
 4930: tensor([[ 0.0019, -0.0159,  0.0239,  ..., -0.0260, -0.0080, -0.0148]]),
 6145: tensor([[-0.0336, -0.0138,  0.0042,  ...,  0.0028, -0.0238, -0.0264]]),
 9556: tensor([[-0.0266,  0.0160,  0.0119,  ..., -0.0332,  0.0035, -0.0333]]),
 5276: tensor([[-0.0249, -0.0171, -0.0046,  ..., -0.0148, -0.0331, -0.0007]]),
 2806: tensor([[-0.0253,  0.0025,  0.0157,  ..., -0.0097, -0.0120, -0.0190]]),
 6359: tensor([[-0.0121,  0.0085,  0.0228,  ..., -0.0089, -0.0112, -0.0441]]),
 10558: tensor([[-0.0372, -0.0042,  0.0059,  ..., -0.0126,  0.0089, -0.0099]]),
 1929: tensor([[-0.0071, -0.0245,  0.0167,  ..., -0.0014, -0.0072, -0.0162]]),
 8021: tensor([[-0.0301, -0.0062,  0.0052,  ..., -0.0185,  0.0231, -0.0267]]),
 4701: tensor([[-0.0150,  0.0067,  0.0085,  ..., -0.0185, -0.0103, -0.0030]]),
 10975: tensor([[-0.0315, -0.0001,  0.0382,  ..., -0.0209, -0.0104, -0.0328]]),
 1504: tensor([[-0.0284, -0.0047,  0.0022,  ..., -0.0261, -0.0080, -0.0111]]),
 2549: tensor([[-0.0462,  0.0265, -0.0033,  ..., -0.0363, -0.0099, -0.0108]]),
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 10817: tensor([[-0.0271,  0.0005, -0.0042,  ..., -0.0145, -0.0207, -0.0249]]),
 1466: tensor([[ 0.0177,  0.0082,  0.0281,  ..., -0.0007, -0.0251, -0.0250]]),
 8143: tensor([[-0.0379,  0.0032,  0.0163,  ..., -0.0114, -0.0099, -0.0331]]),
 4663: tensor([[ 0.0024, -0.0058,  0.0278,  ..., -0.0101, -0.0067, -0.0194]]),
 7898: tensor([[-0.0271,  0.0020,  0.0138,  ..., -0.0236, -0.0170, -0.0216]]),
 2964: tensor([[-0.0134, -0.0060,  0.0342,  ..., -0.0135, -0.0037, -0.0200]]),
 142: tensor([[-0.0090,  0.0050,  0.0235,  ...,  0.0077, -0.0058, -0.0242]]),
 9434: tensor([[-0.0245, -0.0001,  0.0214,  ..., -0.0286,  0.0054, -0.0256]]),
 5314: tensor([[-0.0032,  0.0069,  0.0213,  ..., -0.0314, -0.0245, -0.0121]]),
 8387: tensor([[-0.0062,  0.0067,  0.0239,  ..., -0.0205,  0.0095, -0.0178]]),
 11070: tensor([[-0.0368,  0.0049, -0.0020,  ..., -0.0160, -0.0137, -0.0250]]),
 2474: tensor([[-0.0212, -0.0045,  0.0287,  ..., -0.0248, -0.0203, -0.0085]]),
 7588: tensor([[-0.0413,  0.0144,  0.0240,  ..., -0.0168, -0.0144, -0.0479]]),
 7671: tensor([[-0.0209,  0.0123,  0.0218,  ..., -0.0429, -0.0229, -0.0168]]),
 9924: tensor([[-0.0265, -0.0109, -0.0003,  ...,  0.0009,  0.0022, -0.0144]]),
 1976: tensor([[ 3.1205e-05, -2.0946e-02,  2.0163e-02,  ..., -1.4112e-02,
          -7.9819e-03,  1.4805e-04]]),
 10507: tensor([[ 0.0123,  0.0172,  0.0205,  ..., -0.0380, -0.0196, -0.0421]]),
 3103: tensor([[-0.0036,  0.0013, -0.0056,  ..., -0.0238, -0.0146, -0.0325]]),
 6306: tensor([[-0.0215,  0.0270,  0.0291,  ...,  0.0019, -0.0234, -0.0215]]),
 2859: tensor([[-0.0257, -0.0119, -0.0046,  ..., -0.0223, -0.0233, -0.0084]]),
 5229: tensor([[ 0.0035,  0.0089,  0.0061,  ..., -0.0089, -0.0002, -0.0284]]),
 9509: tensor([[ 0.0085, -0.0081,  0.0381,  ..., -0.0066, -0.0098, -0.0098]]),
 386: tensor([[-0.0145,  0.0105,  0.0063,  ..., -0.0062, -0.0136, -0.0159]]),
 3398: tensor([[ 0.0060, -0.0011,  0.0488,  ...,  0.0052,  0.0019, -0.0115]]),
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 3061: tensor([[-0.0264,  0.0036,  0.0162,  ..., -0.0111, -0.0261, -0.0013]]),
 1814: tensor([[-0.0181,  0.0040, -0.0027,  ..., -0.0169, -0.0102,  0.0005]]),
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 9792: tensor([[ 0.0018, -0.0157,  0.0227,  ..., -0.0203, -0.0193, -0.0265]]),
 1439: tensor([[-0.0118,  0.0071,  0.0210,  ..., -0.0113, -0.0237, -0.0146]]),
 10848: tensor([[-0.0178,  0.0272,  0.0116,  ..., -0.0038, -0.0007, -0.0049]]),
 9846: tensor([[-0.0110,  0.0083,  0.0205,  ..., -0.0262, -0.0006, -0.0233]]),
 7713: tensor([[ 0.0061, -0.0101,  0.0383,  ..., -0.0267, -0.0263, -0.0260]]),
 2516: tensor([[-0.0072,  0.0157,  0.0116,  ..., -0.0068, -0.0078, -0.0374]]),
 11112: tensor([[ 0.0155,  0.0035,  0.0346,  ..., -0.0214,  0.0035, -0.0066]]),
 11087: tensor([[-0.0232, -0.0009,  0.0090,  ..., -0.0240, -0.0273, -0.0178]]),
 2483: tensor([[-0.0133,  0.0064,  0.0120,  ..., -0.0186,  0.0039,  0.0126]]),
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 371: tensor([[-0.0300,  0.0084,  0.0013,  ..., -0.0249,  0.0042, -0.0245]]),
 1981: tensor([[-0.0087,  0.0042,  0.0205,  ..., -0.0305,  0.0048, -0.0182]]),
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 10609: tensor([[-0.0090,  0.0123,  0.0150,  ..., -0.0135, -0.0005, -0.0436]]),
 213: tensor([[-0.0033,  0.0082,  0.0244,  ..., -0.0257, -0.0319, -0.0300]]),
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 6293: tensor([[-0.0054, -0.0034,  0.0160,  ..., -0.0057, -0.0052, -0.0214]]),
 3096: tensor([[-0.0196,  0.0092,  0.0132,  ..., -0.0179,  0.0013, -0.0435]]),
 10492: tensor([[-0.0103,  0.0053,  0.0301,  ..., -0.0322, -0.0206, -0.0401]]),
 2618: tensor([[-0.0062, -0.0084, -0.0031,  ..., -0.0268, -0.0069, -0.0094]]),
 1737: tensor([[-1.5890e-03, -7.3855e-03,  3.6437e-02,  ..., -1.6940e-02,
          -2.0187e-05, -2.2518e-02]]),
 4532: tensor([[-0.0181, -0.0008,  0.0407,  ...,  0.0033, -0.0233, -0.0173]]),
 8212: tensor([[-0.0180,  0.0131,  0.0164,  ..., -0.0172, -0.0096, -0.0129]]),
 10656: tensor([[-0.0143, -0.0307,  0.0160,  ..., -0.0309, -0.0220, -0.0120]]),
 3252: tensor([[-0.0132, -0.0079,  0.0305,  ..., -0.0218, -0.0212, -0.0172]]),
 6057: tensor([[-0.0183, -0.0171,  0.0198,  ..., -0.0421, -0.0104, -0.0430]]),
 4822: tensor([[ 0.0008, -0.0085,  0.0033,  ..., -0.0062, -0.0048, -0.0170]]),
 5281: tensor([[-0.0206, -0.0297, -0.0015,  ..., -0.0116,  0.0012, -0.0049]]),
 9658: tensor([[-0.0090, -0.0103,  0.0191,  ..., -0.0103, -0.0063, -0.0172]]),
 5178: tensor([[ 0.0035, -0.0052, -0.0176,  ..., -0.0102, -0.0087, -0.0231]]),
 10982: tensor([[-0.0098,  0.0054,  0.0329,  ..., -0.0167,  0.0031, -0.0093]]),
 2725: tensor([[-1.9180e-02, -2.0919e-05,  1.2971e-02,  ..., -9.5235e-03,
          -1.3788e-03, -1.9074e-02]]),
 7520: tensor([[-1.7921e-02,  2.1717e-02,  8.6034e-03,  ...,  3.6107e-03,
           6.3443e-05, -4.0736e-02]]),
 1491: tensor([[-0.0247,  0.0127,  0.0004,  ..., -0.0111, -0.0295, -0.0122]]),
 4694: tensor([[-0.0307, -0.0005,  0.0181,  ..., -0.0090, -0.0123, -0.0232]]),
 1768: tensor([[-0.0287, -0.0050,  0.0257,  ..., -0.0284, -0.0158, -0.0178]]),
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 2647: tensor([[-0.0098,  0.0212,  0.0384,  ..., -0.0012, -0.0146, -0.0309]]),
 4940: tensor([[-0.0264,  0.0040,  0.0151,  ..., -0.0174, -0.0324, -0.0110]]),
 6135: tensor([[-0.0173,  0.0025,  0.0264,  ..., -0.0361, -0.0241, -0.0143]]),
 3330: tensor([[-0.0210,  0.0155,  0.0196,  ..., -0.0025, -0.0278, -0.0004]]),
 10734: tensor([[-0.0275,  0.0088,  0.0061,  ..., -0.0157, -0.0294, -0.0283]]),
 2993: tensor([[-0.0125, -0.0139,  0.0130,  ..., -0.0329, -0.0329, -0.0058]]),
 7217: tensor([[-0.0195,  0.0064,  0.0049,  ..., -0.0248, -0.0082, -0.0237]]),
 834: tensor([[-0.0206,  0.0012, -0.0159,  ..., -0.0440, -0.0094,  0.0159]]),
 2012: tensor([[-0.0061, -0.0081,  0.0087,  ..., -0.0300, -0.0150, -0.0059]]),
 4338: tensor([[-0.0031,  0.0003,  0.0284,  ...,  0.0103, -0.0106, -0.0519]]),
 8418: tensor([[ 0.0093,  0.0027,  0.0406,  ..., -0.0002,  0.0188, -0.0318]]),
 3948: tensor([[-0.0334, -0.0012,  0.0088,  ...,  0.0071, -0.0312, -0.0164]]),
 419: tensor([[ 0.0031, -0.0161,  0.0309,  ...,  0.0018, -0.0135, -0.0283]]),
 9296: tensor([[-0.0044, -0.0032,  0.0249,  ..., -0.0220, -0.0145, -0.0367]]),
 6760: tensor([[-0.0151,  0.0020,  0.0200,  ..., -0.0264, -0.0285, -0.0226]]),
 8835: tensor([[-0.0046, -0.0155,  0.0277,  ..., -0.0102, -0.0104, -0.0338]]),
 10298: tensor([[-0.0088,  0.0284,  0.0356,  ..., -0.0152, -0.0128, -0.0150]]),
 10161: tensor([[-0.0040,  0.0072,  0.0214,  ..., -0.0060,  0.0073, -0.0237]]),
 3565: tensor([[-0.0021,  0.0052,  0.0244,  ..., -0.0234, -0.0190, -0.0365]]),
 6499: tensor([[-0.0150, -0.0101,  0.0396,  ..., -0.0084, -0.0387, -0.0307]]),
 682: tensor([[ 0.0002, -0.0125,  0.0602,  ..., -0.0172, -0.0135, -0.0119]]),
 3407: tensor([[ 0.0036, -0.0068,  0.0202,  ..., -0.0125, -0.0216, -0.0178]]),
 10003: tensor([[ 0.0006, -0.0038,  0.0239,  ..., -0.0272, -0.0142, -0.0074]]),
 8957: tensor([[-0.0084, -0.0109,  0.0050,  ..., -0.0084, -0.0098, -0.0119]]),
 6602: tensor([[-0.0342,  0.0043,  0.0224,  ...,  0.0050,  0.0019, -0.0257]]),
 2170: tensor([[ 0.0026,  0.0108,  0.0218,  ..., -0.0254,  0.0073, -0.0312]]),
 956: tensor([[-0.0007,  0.0096,  0.0400,  ..., -0.0154, -0.0051, -0.0284]]),
 2289: tensor([[-0.0406,  0.0026,  0.0101,  ..., -0.0200, -0.0009, -0.0021]]),
 7375: tensor([[-0.0311, -0.0148,  0.0063,  ..., -0.0175, -0.0038, -0.0379]]),
 8683: tensor([[-0.0135,  0.0045,  0.0242,  ..., -0.0253, -0.0360, -0.0121]]),
 446: tensor([[-0.0353, -0.0076,  0.0028,  ..., -0.0172, -0.0231, -0.0101]]),
 9130: tensor([[-0.0217, -0.0051,  0.0196,  ..., -0.0132, -0.0075, -0.0131]]),
 5610: tensor([[-0.0212,  0.0164,  0.0059,  ..., -0.0088, -0.0092, -0.0246]]),
 1162: tensor([[-0.0236, -0.0025, -0.0085,  ..., -0.0206, -0.0002, -0.0235]]),
 3917: tensor([[-0.0479, -0.0060, -0.0083,  ..., -0.0090, -0.0081, -0.0250]]),
 8447: tensor([[-0.0057, -0.0099,  0.0156,  ..., -0.0497, -0.0080, -0.0170]]),
 4367: tensor([[-0.0178, -0.0264,  0.0218,  ...,  0.0028, -0.0128, -0.0255]]),
 7248: tensor([[ 6.8541e-05,  1.6040e-02,  2.1287e-02,  ..., -1.6897e-02,
          -2.7794e-02, -2.2118e-02]]),
 8525: tensor([[-0.0341, -0.0132, -0.0105,  ..., -0.0139, -0.0316, -0.0103]]),
 4205: tensor([[-0.0237,  0.0013,  0.0177,  ..., -0.0169, -0.0238, -0.0139]]),
 6989: tensor([[-0.0096, -0.0202,  0.0148,  ..., -0.0068, -0.0085, -0.0273]]),
 3875: tensor([[-0.0303, -0.0040,  0.0055,  ..., -0.0015, -0.0413, -0.0052]]),
 1000: tensor([[ 0.0131,  0.0058,  0.0344,  ..., -0.0121, -0.0080, -0.0207]]),
 909: tensor([[-0.0510, -0.0059, -0.0062,  ..., -0.0098,  0.0020, -0.0195]]),
 8908: tensor([[-0.0144,  0.0086,  0.0311,  ..., -0.0130, -0.0227, -0.0249]]),
 3458: tensor([[-0.0331, -0.0058, -0.0074,  ..., -0.0267, -0.0166, -0.0131]]),
 ...}

In [14]:

# Check length and dimension of query embeddings
len(query_embeddings), query_embeddings[0].shape

# Check length and dimension of query embeddings len(query_embeddings), query_embeddings[0].shape

Out[14]:

(11204, torch.Size([1, 1536]))

In [10]:

# Check node embeddings
node_embeddings

# Check node embeddings node_embeddings

Out[10]:

{0: tensor([[-0.0497, -0.0080, -0.0108,  ..., -0.0098, -0.0167, -0.0184]]),
 1: tensor([[-0.0310,  0.0156,  0.0047,  ..., -0.0299, -0.0185, -0.0211]]),
 2: tensor([[-0.0295,  0.0115, -0.0203,  ..., -0.0397, -0.0181, -0.0204]]),
 3: tensor([[-0.0320, -0.0132, -0.0218,  ..., -0.0329, -0.0123, -0.0272]]),
 4: tensor([[-0.0325, -0.0123, -0.0177,  ..., -0.0282, -0.0045, -0.0315]]),
 5: tensor([[-0.0136,  0.0236,  0.0044,  ..., -0.0408, -0.0187, -0.0272]]),
 6: tensor([[-0.0257, -0.0240, -0.0326,  ..., -0.0291, -0.0035, -0.0589]]),
 7: tensor([[-0.0396,  0.0123, -0.0340,  ..., -0.0194, -0.0149, -0.0235]]),
 8: tensor([[-0.0345, -0.0122, -0.0214,  ..., -0.0268, -0.0206, -0.0376]]),
 9: tensor([[-0.0239,  0.0046, -0.0159,  ..., -0.0240, -0.0084, -0.0385]]),
 10: tensor([[-0.0468,  0.0205, -0.0175,  ..., -0.0329, -0.0131, -0.0329]]),
 11: tensor([[-0.0305, -0.0085, -0.0157,  ..., -0.0219,  0.0019, -0.0292]]),
 12: tensor([[-0.0392,  0.0038, -0.0234,  ..., -0.0421, -0.0152, -0.0192]]),
 13: tensor([[-0.0346, -0.0029, -0.0270,  ..., -0.0328, -0.0126, -0.0392]]),
 14: tensor([[-0.0262,  0.0027, -0.0106,  ..., -0.0319, -0.0069, -0.0253]]),
 15: tensor([[-0.0490, -0.0068, -0.0229,  ..., -0.0331, -0.0051, -0.0285]]),
 16: tensor([[-0.0375,  0.0165, -0.0160,  ..., -0.0340, -0.0232, -0.0056]]),
 17: tensor([[-0.0311,  0.0126, -0.0076,  ..., -0.0138, -0.0285, -0.0084]]),
 18: tensor([[-0.0543, -0.0118, -0.0111,  ..., -0.0184, -0.0166, -0.0349]]),
 19: tensor([[-0.0484, -0.0021, -0.0089,  ..., -0.0289,  0.0047, -0.0100]]),
 20: tensor([[-0.0246,  0.0050,  0.0015,  ..., -0.0201, -0.0234, -0.0226]]),
 21: tensor([[-0.0323,  0.0020, -0.0073,  ..., -0.0208, -0.0021, -0.0086]]),
 22: tensor([[-0.0352, -0.0054, -0.0212,  ..., -0.0306, -0.0104, -0.0211]]),
 23: tensor([[-0.0442,  0.0124, -0.0185,  ..., -0.0354, -0.0137,  0.0015]]),
 24: tensor([[-0.0395,  0.0162,  0.0140,  ..., -0.0434,  0.0019, -0.0188]]),
 25: tensor([[-0.0507, -0.0074, -0.0236,  ..., -0.0168, -0.0180, -0.0305]]),
 26: tensor([[-0.0464, -0.0071, -0.0220,  ..., -0.0388, -0.0096, -0.0215]]),
 27: tensor([[-0.0241,  0.0031,  0.0005,  ..., -0.0172, -0.0211, -0.0140]]),
 28: tensor([[-0.0398, -0.0016, -0.0021,  ..., -0.0274, -0.0072, -0.0229]]),
 29: tensor([[-0.0483, -0.0168, -0.0316,  ..., -0.0265, -0.0036, -0.0249]]),
 30: tensor([[-0.0516, -0.0005, -0.0175,  ..., -0.0351, -0.0135, -0.0192]]),
 31: tensor([[-0.0338, -0.0124, -0.0109,  ..., -0.0212, -0.0079, -0.0219]]),
 32: tensor([[-0.0310, -0.0007, -0.0193,  ..., -0.0381, -0.0324, -0.0135]]),
 33: tensor([[-0.0378, -0.0063, -0.0011,  ..., -0.0309, -0.0144, -0.0240]]),
 34: tensor([[-0.0246, -0.0071,  0.0018,  ..., -0.0319, -0.0094, -0.0082]]),
 35: tensor([[-0.0119,  0.0198,  0.0092,  ..., -0.0352, -0.0150, -0.0320]]),
 36: tensor([[-0.0344, -0.0056, -0.0079,  ..., -0.0477, -0.0266, -0.0092]]),
 37: tensor([[-0.0206,  0.0138, -0.0174,  ..., -0.0389,  0.0007, -0.0240]]),
 38: tensor([[-0.0453, -0.0030, -0.0072,  ..., -0.0313, -0.0186, -0.0271]]),
 39: tensor([[-0.0560, -0.0014, -0.0270,  ..., -0.0159, -0.0054, -0.0269]]),
 40: tensor([[-0.0377, -0.0008, -0.0217,  ..., -0.0237, -0.0286, -0.0138]]),
 41: tensor([[-0.0371, -0.0117, -0.0135,  ..., -0.0497, -0.0031, -0.0318]]),
 42: tensor([[-0.0335,  0.0034, -0.0234,  ..., -0.0025, -0.0142, -0.0506]]),
 43: tensor([[-0.0301, -0.0157, -0.0153,  ..., -0.0235, -0.0070, -0.0193]]),
 44: tensor([[-0.0201,  0.0050, -0.0203,  ..., -0.0426, -0.0196, -0.0168]]),
 45: tensor([[-0.0470,  0.0032, -0.0271,  ..., -0.0089, -0.0067, -0.0090]]),
 46: tensor([[-0.0334,  0.0033, -0.0152,  ..., -0.0250, -0.0189, -0.0190]]),
 47: tensor([[-0.0354, -0.0274, -0.0133,  ..., -0.0273, -0.0133, -0.0255]]),
 48: tensor([[-0.0353, -0.0092, -0.0234,  ..., -0.0227, -0.0118, -0.0284]]),
 49: tensor([[-0.0344, -0.0043,  0.0104,  ..., -0.0149, -0.0034, -0.0386]]),
 50: tensor([[-0.0475, -0.0132, -0.0267,  ..., -0.0219, -0.0075, -0.0262]]),
 51: tensor([[-0.0374, -0.0038, -0.0065,  ..., -0.0216, -0.0189, -0.0263]]),
 52: tensor([[-0.0389, -0.0061, -0.0157,  ..., -0.0278, -0.0094, -0.0173]]),
 53: tensor([[-0.0520, -0.0026, -0.0457,  ..., -0.0336, -0.0097, -0.0281]]),
 54: tensor([[-0.0280, -0.0075, -0.0208,  ..., -0.0251, -0.0181, -0.0239]]),
 55: tensor([[-0.0433, -0.0098, -0.0194,  ..., -0.0352, -0.0144, -0.0173]]),
 56: tensor([[-0.0516,  0.0055,  0.0027,  ..., -0.0162, -0.0002, -0.0205]]),
 57: tensor([[-0.0297,  0.0080, -0.0182,  ..., -0.0204, -0.0075, -0.0241]]),
 58: tensor([[-0.0365, -0.0027, -0.0091,  ..., -0.0243,  0.0030, -0.0126]]),
 59: tensor([[-0.0384,  0.0015, -0.0135,  ..., -0.0195,  0.0002, -0.0232]]),
 60: tensor([[-0.0345, -0.0034,  0.0086,  ..., -0.0230, -0.0242, -0.0116]]),
 61: tensor([[-0.0311, -0.0028, -0.0196,  ..., -0.0178, -0.0059, -0.0364]]),
 62: tensor([[-4.1921e-02,  9.5770e-03,  5.0823e-05,  ..., -1.6245e-02,
          -1.2847e-04, -2.8425e-02]]),
 63: tensor([[-0.0354,  0.0020,  0.0061,  ..., -0.0081, -0.0007, -0.0184]]),
 64: tensor([[-0.0227,  0.0038, -0.0093,  ..., -0.0220, -0.0103, -0.0282]]),
 65: tensor([[-0.0181,  0.0007, -0.0121,  ..., -0.0367, -0.0097, -0.0267]]),
 66: tensor([[-0.0318,  0.0013, -0.0208,  ..., -0.0420, -0.0230, -0.0324]]),
 67: tensor([[-0.0400, -0.0078, -0.0288,  ..., -0.0354, -0.0237, -0.0222]]),
 68: tensor([[-0.0470, -0.0098, -0.0249,  ..., -0.0264, -0.0142, -0.0331]]),
 69: tensor([[-0.0451, -0.0103, -0.0260,  ..., -0.0177, -0.0167, -0.0214]]),
 70: tensor([[-0.0234,  0.0182, -0.0098,  ..., -0.0185, -0.0062, -0.0193]]),
 71: tensor([[-0.0487, -0.0023, -0.0110,  ..., -0.0126, -0.0298, -0.0327]]),
 72: tensor([[-0.0288, -0.0364, -0.0058,  ..., -0.0352,  0.0062, -0.0364]]),
 73: tensor([[-0.0296, -0.0003, -0.0019,  ..., -0.0213, -0.0060, -0.0206]]),
 74: tensor([[-0.0415,  0.0012, -0.0381,  ..., -0.0563, -0.0191, -0.0263]]),
 75: tensor([[-0.0411,  0.0064, -0.0148,  ..., -0.0293, -0.0058, -0.0361]]),
 76: tensor([[-0.0251, -0.0117, -0.0037,  ..., -0.0350, -0.0090, -0.0282]]),
 77: tensor([[-0.0329, -0.0043, -0.0210,  ..., -0.0161,  0.0074, -0.0093]]),
 78: tensor([[-0.0407,  0.0118, -0.0073,  ..., -0.0230, -0.0112, -0.0054]]),
 79: tensor([[-0.0413, -0.0081, -0.0305,  ..., -0.0345, -0.0116, -0.0198]]),
 80: tensor([[-0.0422,  0.0095, -0.0249,  ..., -0.0164, -0.0124, -0.0228]]),
 81: tensor([[-0.0405, -0.0134, -0.0289,  ..., -0.0141,  0.0033, -0.0429]]),
 82: tensor([[-0.0206,  0.0025, -0.0112,  ..., -0.0256, -0.0119, -0.0162]]),
 83: tensor([[-0.0301,  0.0133, -0.0233,  ..., -0.0368, -0.0239, -0.0205]]),
 84: tensor([[-0.0296, -0.0033, -0.0072,  ..., -0.0188, -0.0056, -0.0209]]),
 85: tensor([[-0.0456, -0.0043, -0.0070,  ..., -0.0367, -0.0173, -0.0275]]),
 86: tensor([[-0.0375,  0.0012, -0.0171,  ..., -0.0304, -0.0177, -0.0207]]),
 87: tensor([[-0.0411,  0.0059, -0.0109,  ..., -0.0409, -0.0054, -0.0209]]),
 88: tensor([[-0.0151,  0.0048, -0.0104,  ..., -0.0258, -0.0110, -0.0275]]),
 89: tensor([[-2.8237e-02,  5.4525e-03, -8.6096e-03,  ..., -4.0161e-02,
          -7.9270e-05, -3.0100e-02]]),
 90: tensor([[-0.0369,  0.0002, -0.0167,  ..., -0.0555, -0.0223, -0.0213]]),
 91: tensor([[-0.0425, -0.0039, -0.0130,  ..., -0.0251, -0.0104, -0.0252]]),
 92: tensor([[-0.0307,  0.0134, -0.0030,  ..., -0.0342, -0.0055, -0.0333]]),
 93: tensor([[-0.0530,  0.0143, -0.0053,  ..., -0.0317, -0.0006,  0.0060]]),
 94: tensor([[-0.0431, -0.0255, -0.0246,  ..., -0.0265, -0.0033, -0.0318]]),
 95: tensor([[-0.0226, -0.0106, -0.0131,  ..., -0.0502,  0.0056, -0.0303]]),
 96: tensor([[-0.0274, -0.0168,  0.0043,  ..., -0.0344, -0.0127, -0.0207]]),
 97: tensor([[-0.0317,  0.0062, -0.0047,  ..., -0.0330, -0.0030, -0.0188]]),
 98: tensor([[-0.0274,  0.0116, -0.0038,  ..., -0.0246, -0.0041, -0.0244]]),
 99: tensor([[-0.0239, -0.0111, -0.0183,  ..., -0.0484,  0.0027, -0.0192]]),
 100: tensor([[-0.0245,  0.0184, -0.0053,  ..., -0.0245, -0.0022, -0.0226]]),
 101: tensor([[-0.0299, -0.0202, -0.0104,  ..., -0.0205, -0.0163, -0.0373]]),
 102: tensor([[-0.0498, -0.0018, -0.0117,  ..., -0.0277, -0.0127, -0.0313]]),
 103: tensor([[-0.0366,  0.0001, -0.0224,  ..., -0.0290, -0.0062, -0.0176]]),
 104: tensor([[-0.0192, -0.0029, -0.0123,  ..., -0.0387, -0.0109, -0.0266]]),
 105: tensor([[-0.0374, -0.0104, -0.0039,  ..., -0.0125, -0.0114, -0.0287]]),
 106: tensor([[-0.0376, -0.0001, -0.0204,  ..., -0.0255, -0.0039, -0.0355]]),
 107: tensor([[-0.0209,  0.0090, -0.0321,  ..., -0.0253, -0.0107, -0.0296]]),
 108: tensor([[-0.0338, -0.0023, -0.0233,  ..., -0.0204, -0.0026, -0.0293]]),
 109: tensor([[-0.0525, -0.0051, -0.0316,  ..., -0.0283, -0.0244, -0.0108]]),
 110: tensor([[-0.0289,  0.0050, -0.0027,  ..., -0.0214,  0.0006, -0.0238]]),
 111: tensor([[-0.0490, -0.0038, -0.0013,  ..., -0.0080, -0.0197, -0.0370]]),
 112: tensor([[-0.0183, -0.0045, -0.0177,  ..., -0.0198, -0.0150, -0.0136]]),
 113: tensor([[-0.0491,  0.0082, -0.0178,  ..., -0.0344, -0.0128, -0.0137]]),
 114: tensor([[-0.0242, -0.0039, -0.0085,  ..., -0.0305,  0.0001, -0.0308]]),
 115: tensor([[-0.0473,  0.0098, -0.0209,  ..., -0.0385, -0.0060,  0.0005]]),
 116: tensor([[-0.0503,  0.0008, -0.0157,  ..., -0.0241, -0.0052, -0.0231]]),
 117: tensor([[-0.0347,  0.0126, -0.0129,  ..., -0.0230, -0.0046, -0.0059]]),
 118: tensor([[-0.0188,  0.0216, -0.0105,  ..., -0.0439, -0.0268, -0.0163]]),
 119: tensor([[-0.0478, -0.0132, -0.0228,  ..., -0.0187, -0.0102, -0.0321]]),
 120: tensor([[-0.0529, -0.0066, -0.0264,  ..., -0.0242, -0.0209, -0.0216]]),
 121: tensor([[-0.0246,  0.0045, -0.0094,  ..., -0.0243,  0.0052, -0.0250]]),
 122: tensor([[-0.0274, -0.0037, -0.0143,  ..., -0.0245, -0.0243, -0.0157]]),
 123: tensor([[-0.0335, -0.0108, -0.0298,  ..., -0.0279, -0.0118, -0.0259]]),
 124: tensor([[-0.0196,  0.0027, -0.0198,  ..., -0.0335,  0.0015, -0.0435]]),
 125: tensor([[-0.0393,  0.0012, -0.0273,  ..., -0.0385,  0.0002, -0.0242]]),
 126: tensor([[-0.0312,  0.0124, -0.0205,  ..., -0.0191, -0.0026, -0.0168]]),
 127: tensor([[-0.0430,  0.0038, -0.0316,  ..., -0.0452, -0.0011, -0.0220]]),
 128: tensor([[-0.0327, -0.0120, -0.0240,  ..., -0.0379, -0.0080, -0.0292]]),
 129: tensor([[-0.0405,  0.0157, -0.0155,  ..., -0.0177, -0.0103, -0.0098]]),
 130: tensor([[-0.0476,  0.0034, -0.0139,  ..., -0.0320, -0.0095, -0.0329]]),
 131: tensor([[-0.0472, -0.0043, -0.0129,  ..., -0.0290, -0.0104, -0.0375]]),
 132: tensor([[-0.0137,  0.0057, -0.0113,  ..., -0.0282, -0.0202, -0.0321]]),
 133: tensor([[-0.0238, -0.0075, -0.0035,  ..., -0.0341, -0.0190, -0.0189]]),
 134: tensor([[-0.0357,  0.0039, -0.0110,  ..., -0.0259, -0.0066, -0.0266]]),
 135: tensor([[-0.0224, -0.0073,  0.0037,  ..., -0.0113, -0.0081, -0.0307]]),
 136: tensor([[-0.0258,  0.0034, -0.0109,  ..., -0.0342,  0.0011, -0.0084]]),
 137: tensor([[-0.0300,  0.0064, -0.0174,  ..., -0.0408, -0.0082, -0.0053]]),
 138: tensor([[-0.0295,  0.0151, -0.0228,  ..., -0.0358, -0.0034, -0.0165]]),
 139: tensor([[-0.0382, -0.0018, -0.0107,  ..., -0.0239, -0.0095, -0.0149]]),
 140: tensor([[-0.0267,  0.0130, -0.0235,  ..., -0.0288, -0.0187, -0.0247]]),
 141: tensor([[-0.0381,  0.0173, -0.0138,  ..., -0.0413, -0.0053, -0.0329]]),
 142: tensor([[-0.0426, -0.0167, -0.0220,  ..., -0.0205, -0.0123, -0.0297]]),
 143: tensor([[-0.0089, -0.0094, -0.0130,  ..., -0.0268, -0.0099, -0.0166]]),
 144: tensor([[-0.0409,  0.0018, -0.0288,  ..., -0.0156, -0.0080, -0.0203]]),
 145: tensor([[-0.0411, -0.0061, -0.0046,  ..., -0.0249, -0.0034, -0.0405]]),
 146: tensor([[-0.0370,  0.0163, -0.0327,  ..., -0.0237, -0.0093, -0.0406]]),
 147: tensor([[-0.0448,  0.0056, -0.0336,  ..., -0.0350, -0.0028, -0.0370]]),
 148: tensor([[-0.0260,  0.0217, -0.0152,  ..., -0.0260, -0.0124, -0.0323]]),
 149: tensor([[-0.0300, -0.0059, -0.0325,  ..., -0.0393, -0.0074, -0.0125]]),
 150: tensor([[-0.0329,  0.0052, -0.0277,  ..., -0.0308, -0.0140, -0.0195]]),
 151: tensor([[-0.0582,  0.0107, -0.0032,  ..., -0.0350, -0.0104, -0.0242]]),
 152: tensor([[-0.0395, -0.0047, -0.0253,  ..., -0.0418, -0.0147, -0.0186]]),
 153: tensor([[-0.0496, -0.0098, -0.0172,  ..., -0.0190, -0.0151, -0.0340]]),
 154: tensor([[-0.0336,  0.0064, -0.0434,  ..., -0.0241, -0.0145, -0.0406]]),
 155: tensor([[-0.0430,  0.0100, -0.0247,  ..., -0.0126, -0.0219, -0.0305]]),
 156: tensor([[-0.0299, -0.0091, -0.0241,  ..., -0.0382, -0.0306, -0.0007]]),
 157: tensor([[-0.0337,  0.0032, -0.0129,  ..., -0.0313,  0.0037, -0.0298]]),
 158: tensor([[-0.0368, -0.0129, -0.0151,  ..., -0.0413, -0.0179, -0.0297]]),
 159: tensor([[-0.0389,  0.0041, -0.0204,  ..., -0.0288,  0.0102, -0.0092]]),
 160: tensor([[-0.0384, -0.0044, -0.0048,  ..., -0.0264, -0.0224, -0.0304]]),
 161: tensor([[-0.0233, -0.0057, -0.0151,  ..., -0.0197, -0.0084, -0.0319]]),
 162: tensor([[-0.0203,  0.0294,  0.0080,  ..., -0.0460, -0.0255, -0.0181]]),
 163: tensor([[-0.0270,  0.0183, -0.0161,  ..., -0.0312, -0.0123, -0.0278]]),
 164: tensor([[-0.0351, -0.0145, -0.0119,  ..., -0.0344, -0.0047, -0.0290]]),
 165: tensor([[-3.1572e-02, -6.5590e-05, -5.0487e-03,  ..., -2.2928e-02,
          -2.6104e-02, -1.4848e-02]]),
 166: tensor([[-0.0409, -0.0092, -0.0102,  ..., -0.0263, -0.0203, -0.0279]]),
 167: tensor([[-0.0214,  0.0034, -0.0074,  ..., -0.0443, -0.0298, -0.0020]]),
 168: tensor([[-0.0483, -0.0150, -0.0249,  ..., -0.0187, -0.0126, -0.0305]]),
 169: tensor([[-0.0281,  0.0120, -0.0098,  ..., -0.0245, -0.0041, -0.0273]]),
 170: tensor([[-0.0299,  0.0024, -0.0131,  ..., -0.0301, -0.0066, -0.0324]]),
 171: tensor([[-0.0389, -0.0124, -0.0204,  ..., -0.0056, -0.0124, -0.0359]]),
 172: tensor([[-0.0611,  0.0054, -0.0058,  ..., -0.0429, -0.0042, -0.0329]]),
 173: tensor([[-0.0364, -0.0112, -0.0049,  ..., -0.0436, -0.0055, -0.0292]]),
 174: tensor([[-0.0496, -0.0123, -0.0195,  ..., -0.0332, -0.0077, -0.0323]]),
 175: tensor([[-0.0400,  0.0079, -0.0143,  ..., -0.0348, -0.0147, -0.0276]]),
 176: tensor([[-0.0377, -0.0062, -0.0146,  ..., -0.0142, -0.0214, -0.0274]]),
 177: tensor([[-0.0493, -0.0021,  0.0041,  ..., -0.0103, -0.0085, -0.0276]]),
 178: tensor([[-0.0502, -0.0050, -0.0061,  ..., -0.0180, -0.0152, -0.0300]]),
 179: tensor([[-0.0357, -0.0035, -0.0459,  ..., -0.0410, -0.0138, -0.0213]]),
 180: tensor([[-0.0454, -0.0130, -0.0219,  ..., -0.0289, -0.0213, -0.0262]]),
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 903: tensor([[-0.0414, -0.0137, -0.0226,  ..., -0.0336, -0.0011, -0.0319]]),
 904: tensor([[-0.0489, -0.0129, -0.0053,  ..., -0.0164, -0.0215, -0.0261]]),
 905: tensor([[-0.0282, -0.0013, -0.0115,  ..., -0.0295, -0.0053, -0.0207]]),
 906: tensor([[-0.0346,  0.0035, -0.0014,  ..., -0.0369, -0.0111, -0.0310]]),
 907: tensor([[-0.0310, -0.0007, -0.0147,  ..., -0.0326, -0.0058, -0.0244]]),
 908: tensor([[-0.0301,  0.0026, -0.0238,  ..., -0.0292, -0.0064, -0.0118]]),
 909: tensor([[-0.0244,  0.0154, -0.0161,  ..., -0.0388, -0.0054, -0.0180]]),
 910: tensor([[-0.0282,  0.0108, -0.0267,  ..., -0.0408, -0.0140, -0.0161]]),
 911: tensor([[-0.0383, -0.0048, -0.0206,  ..., -0.0246,  0.0028, -0.0309]]),
 912: tensor([[-0.0475, -0.0155, -0.0174,  ..., -0.0203, -0.0096, -0.0250]]),
 913: tensor([[-0.0380,  0.0009, -0.0332,  ..., -0.0214, -0.0063, -0.0319]]),
 914: tensor([[-0.0307,  0.0200, -0.0205,  ..., -0.0064, -0.0189, -0.0288]]),
 915: tensor([[-0.0278, -0.0054, -0.0072,  ..., -0.0295, -0.0208, -0.0120]]),
 916: tensor([[-0.0303, -0.0123, -0.0169,  ..., -0.0366, -0.0038, -0.0145]]),
 917: tensor([[-0.0253, -0.0011, -0.0157,  ..., -0.0315, -0.0048, -0.0260]]),
 918: tensor([[-0.0394,  0.0126, -0.0188,  ..., -0.0447, -0.0060, -0.0121]]),
 919: tensor([[-0.0315,  0.0069, -0.0164,  ..., -0.0330, -0.0119, -0.0153]]),
 920: tensor([[-0.0398, -0.0191, -0.0175,  ..., -0.0085, -0.0188, -0.0152]]),
 921: tensor([[-0.0337,  0.0047, -0.0064,  ..., -0.0231, -0.0139, -0.0391]]),
 922: tensor([[-0.0276, -0.0041, -0.0164,  ..., -0.0259,  0.0068, -0.0324]]),
 923: tensor([[-0.0213, -0.0070, -0.0203,  ..., -0.0390, -0.0018, -0.0321]]),
 924: tensor([[-0.0287,  0.0009, -0.0122,  ..., -0.0149, -0.0078, -0.0390]]),
 925: tensor([[-0.0346,  0.0030, -0.0123,  ..., -0.0122, -0.0106, -0.0325]]),
 926: tensor([[-0.0359,  0.0032, -0.0040,  ..., -0.0185, -0.0122, -0.0376]]),
 927: tensor([[-0.0144, -0.0122, -0.0068,  ..., -0.0392, -0.0121, -0.0184]]),
 928: tensor([[-0.0280,  0.0026, -0.0184,  ..., -0.0332, -0.0162, -0.0470]]),
 929: tensor([[-0.0352,  0.0160, -0.0228,  ..., -0.0243, -0.0006, -0.0364]]),
 930: tensor([[-0.0429, -0.0085, -0.0129,  ..., -0.0069, -0.0026, -0.0162]]),
 931: tensor([[-0.0213, -0.0031, -0.0136,  ..., -0.0356, -0.0195, -0.0326]]),
 932: tensor([[-0.0315, -0.0026, -0.0128,  ..., -0.0334,  0.0087, -0.0383]]),
 933: tensor([[-0.0215,  0.0006,  0.0045,  ..., -0.0271, -0.0219, -0.0329]]),
 934: tensor([[-0.0405, -0.0061, -0.0303,  ..., -0.0334, -0.0094, -0.0200]]),
 935: tensor([[-0.0274,  0.0086, -0.0195,  ..., -0.0249, -0.0047, -0.0305]]),
 936: tensor([[-0.0541, -0.0052, -0.0207,  ..., -0.0286, -0.0179, -0.0119]]),
 937: tensor([[-0.0366, -0.0108,  0.0139,  ..., -0.0180, -0.0163, -0.0204]]),
 938: tensor([[-0.0467, -0.0023, -0.0245,  ..., -0.0367, -0.0138, -0.0075]]),
 939: tensor([[-0.0272, -0.0013, -0.0188,  ..., -0.0371, -0.0281, -0.0090]]),
 940: tensor([[-0.0278,  0.0015, -0.0060,  ..., -0.0139, -0.0101, -0.0278]]),
 941: tensor([[-0.0339, -0.0017, -0.0160,  ..., -0.0212, -0.0085, -0.0334]]),
 942: tensor([[-0.0419, -0.0082, -0.0138,  ..., -0.0364, -0.0016, -0.0250]]),
 943: tensor([[-0.0456, -0.0089, -0.0196,  ..., -0.0378,  0.0123, -0.0184]]),
 944: tensor([[-0.0334,  0.0061, -0.0170,  ..., -0.0286,  0.0001, -0.0167]]),
 945: tensor([[-0.0345, -0.0007, -0.0193,  ..., -0.0285, -0.0119, -0.0412]]),
 946: tensor([[-0.0295, -0.0079, -0.0212,  ..., -0.0243, -0.0033, -0.0374]]),
 947: tensor([[-0.0380,  0.0007, -0.0124,  ..., -0.0238,  0.0079, -0.0174]]),
 948: tensor([[-0.0275, -0.0012, -0.0209,  ..., -0.0281, -0.0130, -0.0214]]),
 949: tensor([[-0.0264,  0.0016, -0.0086,  ..., -0.0178, -0.0099, -0.0290]]),
 950: tensor([[-0.0297, -0.0066,  0.0037,  ..., -0.0170, -0.0180, -0.0377]]),
 951: tensor([[-0.0411, -0.0086,  0.0158,  ..., -0.0076, -0.0100, -0.0226]]),
 952: tensor([[-0.0374,  0.0097, -0.0183,  ..., -0.0457,  0.0111, -0.0127]]),
 953: tensor([[-0.0463,  0.0007, -0.0259,  ..., -0.0412, -0.0312, -0.0229]]),
 954: tensor([[-0.0405, -0.0117, -0.0246,  ..., -0.0215, -0.0005, -0.0363]]),
 955: tensor([[-0.0295,  0.0020, -0.0146,  ..., -0.0480,  0.0018, -0.0142]]),
 956: tensor([[-0.0352,  0.0037, -0.0183,  ..., -0.0186, -0.0004, -0.0138]]),
 957: tensor([[-0.0321, -0.0005, -0.0290,  ..., -0.0420, -0.0106, -0.0319]]),
 958: tensor([[-0.0415, -0.0152, -0.0193,  ...,  0.0069, -0.0005, -0.0281]]),
 959: tensor([[-0.0356, -0.0070,  0.0061,  ..., -0.0260, -0.0072, -0.0231]]),
 960: tensor([[-0.0304,  0.0050, -0.0333,  ..., -0.0416,  0.0010, -0.0238]]),
 961: tensor([[-0.0464,  0.0050, -0.0358,  ..., -0.0452, -0.0066, -0.0262]]),
 962: tensor([[-0.0369,  0.0026, -0.0095,  ..., -0.0248,  0.0044, -0.0207]]),
 963: tensor([[-0.0384, -0.0014, -0.0281,  ..., -0.0168, -0.0144, -0.0241]]),
 964: tensor([[-0.0457,  0.0039, -0.0028,  ..., -0.0308, -0.0366, -0.0233]]),
 965: tensor([[-0.0356,  0.0113, -0.0088,  ..., -0.0266,  0.0098, -0.0275]]),
 966: tensor([[-0.0194,  0.0058, -0.0113,  ..., -0.0194, -0.0236, -0.0157]]),
 967: tensor([[-0.0387,  0.0007, -0.0103,  ..., -0.0303, -0.0103, -0.0172]]),
 968: tensor([[-0.0378,  0.0141, -0.0098,  ..., -0.0178, -0.0109, -0.0202]]),
 969: tensor([[-0.0405, -0.0141, -0.0112,  ..., -0.0249, -0.0049, -0.0247]]),
 970: tensor([[-0.0213, -0.0064,  0.0053,  ..., -0.0264, -0.0028, -0.0264]]),
 971: tensor([[-0.0248,  0.0124,  0.0011,  ..., -0.0334, -0.0034, -0.0349]]),
 972: tensor([[-0.0134,  0.0054, -0.0033,  ..., -0.0178, -0.0104, -0.0260]]),
 973: tensor([[-0.0386, -0.0154, -0.0156,  ..., -0.0442, -0.0046, -0.0242]]),
 974: tensor([[-0.0428, -0.0096, -0.0221,  ..., -0.0358,  0.0175, -0.0170]]),
 975: tensor([[-0.0312, -0.0053, -0.0058,  ..., -0.0319, -0.0058, -0.0278]]),
 976: tensor([[-0.0338, -0.0059, -0.0204,  ..., -0.0249, -0.0030, -0.0400]]),
 977: tensor([[-0.0443, -0.0124, -0.0074,  ..., -0.0172,  0.0136, -0.0344]]),
 978: tensor([[-0.0458, -0.0057, -0.0071,  ..., -0.0102, -0.0099, -0.0343]]),
 979: tensor([[-0.0301, -0.0097, -0.0249,  ..., -0.0211, -0.0132, -0.0238]]),
 980: tensor([[-0.0403,  0.0210, -0.0069,  ..., -0.0212, -0.0124, -0.0369]]),
 981: tensor([[-0.0305,  0.0132, -0.0163,  ..., -0.0136, -0.0109, -0.0305]]),
 982: tensor([[-0.0402, -0.0097, -0.0111,  ..., -0.0374, -0.0121, -0.0387]]),
 983: tensor([[-0.0241, -0.0080, -0.0023,  ..., -0.0164, -0.0231, -0.0282]]),
 984: tensor([[-0.0275, -0.0067, -0.0300,  ..., -0.0556, -0.0159, -0.0301]]),
 985: tensor([[-0.0433,  0.0055, -0.0240,  ..., -0.0274, -0.0166, -0.0194]]),
 986: tensor([[-0.0446, -0.0045, -0.0211,  ..., -0.0337,  0.0001, -0.0227]]),
 987: tensor([[-0.0523, -0.0033, -0.0202,  ..., -0.0259, -0.0118, -0.0336]]),
 988: tensor([[-0.0433, -0.0136, -0.0437,  ..., -0.0216, -0.0142, -0.0136]]),
 989: tensor([[-0.0385, -0.0129, -0.0194,  ..., -0.0313, -0.0125, -0.0328]]),
 990: tensor([[-0.0309, -0.0018, -0.0063,  ..., -0.0240,  0.0023, -0.0262]]),
 991: tensor([[-0.0299,  0.0072, -0.0134,  ..., -0.0450,  0.0005, -0.0149]]),
 992: tensor([[-0.0477,  0.0002, -0.0149,  ..., -0.0422, -0.0194, -0.0253]]),
 993: tensor([[-0.0403, -0.0138, -0.0082,  ..., -0.0457, -0.0131, -0.0213]]),
 994: tensor([[-0.0391,  0.0010, -0.0166,  ..., -0.0278, -0.0307, -0.0208]]),
 995: tensor([[-0.0420,  0.0053, -0.0148,  ..., -0.0359,  0.0001, -0.0196]]),
 996: tensor([[-0.0391, -0.0082, -0.0006,  ..., -0.0450, -0.0105, -0.0190]]),
 997: tensor([[-0.0217, -0.0087, -0.0246,  ..., -0.0317, -0.0149, -0.0266]]),
 998: tensor([[-0.0625, -0.0010, -0.0158,  ..., -0.0208, -0.0058, -0.0298]]),
 999: tensor([[-0.0303,  0.0024,  0.0018,  ..., -0.0095, -0.0071, -0.0227]]),
 ...}

In [15]:

# Check length and dimension of node embeddings
len(node_embeddings), node_embeddings[0].shape

# Check length and dimension of node embeddings len(node_embeddings), node_embeddings[0].shape

Out[15]:

(129375, torch.Size([1, 1536]))

Check StarQA-PrimeKG splits¶

StarkQA-PrimeKG splits contain train, validation, and test indices for benchmarking the QA-driven AI models.

In [12]:

# Check the split indices of the starkqa primekg dataframe
starkqa_split_indices.keys()

# Check the split indices of the starkqa primekg dataframe starkqa_split_indices.keys()

Out[12]:

dict_keys(['train', 'val', 'test', 'test-0.1'])

Finally, we can check the number of each split as follows.

In [13]:

# Check the number of samples in each split of the starkqa primekg dataframe
for split, idx in starkqa_split_indices.items():
    print(f"{split}: {len(idx)}")

# Check the number of samples in each split of the starkqa primekg dataframe for split, idx in starkqa_split_indices.items(): print(f"{split}: {len(idx)}")

train: 6162
val: 2241
test: 2801
test-0.1: 280