Adding datasets
Browse files- .gitignore +2 -1
- hupd.py +10 -9
- json-files-Jan2016.tar +0 -3
- tests/tests.py +179 -0
.gitignore
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@@ -1 +1,2 @@
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tmp
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tmp
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*.pyc
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hupd.py
CHANGED
@@ -119,7 +119,7 @@ class PatentsConfig(datasets.BuilderConfig):
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class Patents(datasets.GeneratorBasedBuilder):
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_DESCRIPTION
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VERSION = datasets.Version("1.0.
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# This is an example of a dataset with multiple configurations.
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# If you don't want/need to define several sub-sets in your dataset,
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@@ -129,16 +129,16 @@ class Patents(datasets.GeneratorBasedBuilder):
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PatentsConfig(
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name="sample",
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description="Patent data from January 2016, for debugging",
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metadata_url="https://huggingface.co/datasets/HUPD/hupd
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data_url="https://huggingface.co/datasets/HUPD/hupd
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data_dir="
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),
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PatentsConfig(
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name="all",
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description="Patent data from
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metadata_url="https://
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data_url="https://
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data_dir="
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),
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]
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@@ -274,8 +274,9 @@ class Patents(datasets.GeneratorBasedBuilder):
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for id_, x in enumerate(df.itertuples()):
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# JSON files are named by application number (unique)
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application_number = x.application_number
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filepath = os.path.join(json_dir, application_number + '.json')
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try:
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with open(filepath, 'r') as f:
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patent = json.load(f)
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class Patents(datasets.GeneratorBasedBuilder):
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_DESCRIPTION
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VERSION = datasets.Version("1.0.2")
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# This is an example of a dataset with multiple configurations.
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# If you don't want/need to define several sub-sets in your dataset,
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PatentsConfig(
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name="sample",
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description="Patent data from January 2016, for debugging",
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metadata_url="https://huggingface.co/datasets/HUPD/hupd/resolve/main/hupd_metadata_jan16_2022-02-22.feather",
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data_url="https://huggingface.co/datasets/HUPD/hupd/resolve/main/data/sample-jan-2016.tar.gz",
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data_dir="data", # this will unpack to data/sample/2016
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),
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PatentsConfig(
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name="all",
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description="Patent data from all years (2004-2018)",
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metadata_url="https://huggingface.co/datasets/HUPD/hupd/resolve/main/hupd_metadata_2022-02-22.feather",
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data_url="https://huggingface.co/datasets/HUPD/hupd/resolve/main/data/all-years.tar.gz",
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data_dir="data", # this will unpack to data/{year}
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),
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]
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for id_, x in enumerate(df.itertuples()):
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# JSON files are named by application number (unique)
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application_year = str(x.filing_date.year)
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application_number = x.application_number
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filepath = os.path.join(json_dir, application_year, application_number + '.json')
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try:
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with open(filepath, 'r') as f:
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patent = json.load(f)
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json-files-Jan2016.tar
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version https://git-lfs.github.com/spec/v1
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oid sha256:4a7d7923941e39255112d2b40a40e8dae8579d9150459c1f0599ffe8a4cfb5a5
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size 2024540160
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tests/tests.py
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"""
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Dataset loading tests. Run with:
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PYTHONPATH=. pytest tests/tests.py -vvrP
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Additional notes about pytest:
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- Skip a test with @pytest.mark.skip(reason='skipping')
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- Use `-vvrP` to print stdout
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"""
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import pdb
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import os
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from pathlib import Path
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from pprint import pprint
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import pytest
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import torch
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import torch.nn.functional as F
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import torch.utils.data
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from datasets import load_dataset
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def test_dataset_sample():
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"""Load the sample dataset"""
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root = os.getcwd()
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dataset_dict = load_dataset(
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'hupd.py',
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name='sample',
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data_files=os.path.join(root, "hupd_metadata_jan16_2022-02-22.feather"),
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data_dir=os.path.join(root, "data/sample"),
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uniform_split=True
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)
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for name, dataset in dataset_dict.items():
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print(f'Dataset {name}: {len(dataset)}')
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import pdb; pdb.set_trace()
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if __name__ == '__main__':
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test_dataset_sample()
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# # # ----- Data loading example 1 ------
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# # # To load a dataset from files directly, pass in the
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# # # data_files and data_dir parameters. For example:
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# # # ----- Data loading example 2 ------
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# # # It is simple to specify an IPCR or CPC label and
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# # # a date range for training/validation. For example:
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# # dataset_dict = load_dataset(
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# # 'patents.py',
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# # data_files="/blob/uspto/data/codebooks/data_link_new.pkl",
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# # data_dir="/blob/uspto/data/distilled",
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# # ipcr_label='G01T', #'G06F',
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# # cpc_label=None,
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# # train_filing_start_date=None,
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# # train_filing_end_date=None,
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# # val_filing_start_date=None,
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# # val_filing_end_date=None,
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# # )
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# # # ----- Data loading example 3 ------
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# # If you do not specify the data_files and data_dir parameters, the
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# # dataset will be downloaded automatically for you. For example:
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# dataset_dict = load_dataset(
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# 'patents.py',
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# data_dir="/blob/uspto/data/distilled",
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# cache_dir='/blob/data/patents/distilled/distilled/huggingface-dataset/cache',
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# ipcr_label=None, # 'G01T', #'G06F', # cpc_label='G01T',
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# train_filing_start_date='2016-01-01',
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# train_filing_end_date='2016-01-05',
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# val_filing_start_date='2017-01-01',
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# val_filing_end_date='2017-01-05',
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# )
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# def combine_two_sections(tokenizer, dataset, s1, s2, new_tokens):
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# # Add the seperation token
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# if tokenizer.sep_token != '[SEP]':
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# tokenizer.add_tokens(['[SEP]'], special_tokens=True)
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# tokenizer.sep_token = '[SEP]'
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# print(f'[OLD] len(tokenizer.vocab) = {len(tokenizer)}')
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# tokenizer.add_tokens(new_tokens + [s1.upper(), 'TITLE', 'YEAR', s2.upper()])
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# print(f'[NEW] len(tokenizer.vocab) = {len(tokenizer)}')
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# dataset = dataset.map(
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# # lambda e: {f'{s1}_{s2}': f'[SEP] {s1.upper()} ' + e[s1 + '_label'][:4] + ' [SEP] ' + e[s2]})
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# lambda e: {f'{s1}_{s2}': f'[SEP] TITLE ' + e['title'] + '. YEAR ' + e['filing_date'][:4] + f'. {s1.upper()} ' + e[s1 + '_label'][:4] + f' [SEP] {s2.upper()} ' + e[s2]})
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# return tokenizer, dataset
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# def convert_ids_to_string(tokenizer, input):
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# return ' '.join(tokenizer.convert_ids_to_tokens(input))
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# conditional = 'ipc'
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# section = 'abstract'
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# # Print some metadata
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# print('Dataset dictionary contents:')
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# pprint(dataset_dict)
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# print('Dataset dictionary cached to:')
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# pprint(dataset_dict.cache_files)
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# print(f'Train dataset size: {dataset_dict["train"].shape}')
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# print(f'Validation dataset size: {dataset_dict["validation"].shape}')
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# # Example: preprocess dataset "decision" feature for classification
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# decision_to_str = {
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# 'REJECTED': 0,
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# 'ACCEPTED': 1,
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# 'PENDING': 2,
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# 'CONT-REJECTED': 3,
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# 'CONT-ACCEPTED': 4,
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# 'CONT-PENDING': 5
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# }
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# def map_decision_to_string(example):
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# # NOTE: returned dict updates the example
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# return {'decision': decision_to_str[example['decision']]}
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# # Performing the remapping means iterating over the dataset
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# # NOTE: This stores the updated table in a cache file indexed
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# # by the current state and the mapping function
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# train_dataset = dataset_dict['train'].map(map_decision_to_string)
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# print('Processed train dataset cached to: ')
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# pprint(train_dataset.cache_files)
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# # Example: preprocess dataset "abstract" field using huggingface
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# # tokenizers for classification. We truncate at the max token length.
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# from transformers import AutoTokenizer
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# tokenizer = AutoTokenizer.from_pretrained('roberta-base')
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# # def map_cpc_label(example):
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# # # NOTE: returned dict updates the example
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# # # print(tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids(example['cpc_label'][:4])))
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# # return {'cpc_label': tokenizer.convert_tokens_to_ids(example['cpc_label'][:4])}
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# # train_dataset = train_dataset.map(map_cpc_label)
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# if conditional:
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# f = open(f'{conditional}_labels.txt', 'r')
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# new_tokens = f.read().split('\n')
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# tokenizer, train_dataset = combine_two_sections(tokenizer, train_dataset, conditional, section, new_tokens)
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# section = f'{conditional}_{section}'
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# # We tokenize in batches, so it is actually quite fast
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# print('Tokenizing')
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# train_dataset = train_dataset.map(
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# lambda e: tokenizer((e[section]), truncation=True, padding='max_length'),
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# batched=True)
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# print('Processed train dataset cached to: ')
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# pprint(train_dataset.cache_files)
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# print('Processed train dataset columns: ')
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# pprint(train_dataset.column_names)
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# # Convert to PyTorch Dataset
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# # NOTE: If you also want to return string columns (as a list), just
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# # pass `output_all_columns=True` to the dataset
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# train_dataset.set_format(type='torch',
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# columns=['input_ids', 'attention_mask', 'decision'])
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# # Standard PyTorch DataLoader
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# from torch.utils.data import DataLoader
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# train_dataloader = DataLoader(train_dataset, batch_size=16)
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# print('Shapes of items in batch from standard PyTorch DataLoader:')
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# pprint({k: v.shape for k, v in next(iter(train_dataloader)).items()})
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# print('Batch from standard PyTorch DataLoader:')
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# batch = next(iter(train_dataloader))
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# pprint(batch['input_ids'])
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# pprint(batch['decision'])
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# # Print examples
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# print(convert_ids_to_string(tokenizer, batch['input_ids'][0]))
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# pprint(batch['input_ids'][0][:20])
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# # vocab = batch['input_ids'][0][:20]
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# # for elt in vocab:
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# # print(f'{elt}: {convert_ids_to_string(tokenizer, [elt])}')
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# print(tokenizer.decode(batch['input_ids'][0]))
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# print('All done')
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