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raise error if token count exceeds 1024 instead of attempting to re-chunk #29

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Merged
alimaredia merged 2 commits into from
Jun 18, 2025
Merged

raise error if token count exceeds 1024 instead of attempting to re-chunk #29

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961dbd2
raise error if token count exceeds 1024 instead of attempting to re-c…
khaledsulayman Jun 10, 2025
85999ee
when chunk exceeds max token count, print truncated chunk
khaledsulayman Jun 18, 2025
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30 changes: 14 additions & 16 deletions notebooks/instructlab-knowledge/utils/create_seed_dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -119,7 +119,7 @@ def safe_concatenate_datasets(datasets: list[Dataset]) -> Dataset:
def get_token_count(text, tokenizer):
return len(tokenizer.tokenize(text))

def add_icls(qna_yaml: Dict[str, str], chunked_document: Dataset) -> Dataset:
def add_icls(qna_yaml: Dict[str, str], chunked_document: Dataset, max_token_count: int = 1024) -> Dataset:
"""
Add the ICLS label to the dataset.
Args:
Expand Down Expand Up @@ -147,24 +147,22 @@ def add_icls(qna_yaml: Dict[str, str], chunked_document: Dataset) -> Dataset:
)
)
chunked_document_all_icl = safe_concatenate_datasets(chunked_document_all_icl)
chunked_document_all_icl = chunked_document_all_icl.map(
lambda x: {
"chunks": chunk_document(
[x["document"]], server_ctx_size=4096, chunk_word_count=1024
)
if get_token_count(x["document"], tokenizer) > 1024
else [x["document"]]
}
)
def truncate_chunk(chunk: str):
words = chunk.split()
if len(words) > 7:
return " ".join(words[:3]) + " ... " + " ".join(words[-3:])
return chunk

for c in chunked_document_all_icl:
if get_token_count(c["document"], tokenizer) > max_token_count:
raise ValueError(f"Chunk \"{truncate_chunk(c["document"])}\" exceeds token count of {max_token_count}")


df = chunked_document_all_icl.to_pandas()
df_exploded = df.explode("chunks").reset_index(drop=True)
new_ds = Dataset.from_pandas(df_exploded)
new_ds = new_ds.remove_columns("document").rename_columns(
{"chunks": "document"}
)
new_ds = Dataset.from_pandas(df)

# Only keep document greater than 100 tokens
new_ds = new_ds.filter(
lambda x: get_token_count(x["document"], tokenizer) > 100
lambda c: get_token_count(c["document"], tokenizer) > 100
)
return new_ds