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33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
import os
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from mmengine.config import read_base
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with read_base():
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from .ruler_cwe_gen import cwe_datasets as cwe # CWE
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from .ruler_fwe_gen import fwe_datasets as fwe # FWE
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from .ruler_niah_gen import niah_datasets as niah # Niah
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from .ruler_qa_gen import qa_datasets as qa # QA
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from .ruler_vt_gen import vt_datasets as vt # VT
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import_ds = sum((cwe, fwe, niah, qa, vt), [])
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# Evaluation config
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NUM_SAMPLES = 100 # Change to the number of samples you need
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tokenizer_model = os.environ.get('TOKENIZER_MODEL', 'gpt-4')
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# Change the context lengths to be tested
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max_seq_lens = [1024 * 16]
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abbr_suffixs = ['16k']
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ruler_datasets = []
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# Different seq length
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for max_seq_len, abbr_suffix in zip(max_seq_lens, abbr_suffixs):
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for dataset in import_ds:
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tmp_dataset = dataset.deepcopy()
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tmp_dataset['abbr'] = tmp_dataset['abbr'] + '_' + abbr_suffix
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tmp_dataset['num_samples'] = NUM_SAMPLES
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tmp_dataset['max_seq_length'] = max_seq_len
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tmp_dataset['tokenizer_model'] = tokenizer_model
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ruler_datasets.append(tmp_dataset)
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