plaguss/argilla_sdk_docs_raw_unstructured

Dataset info

This dataset contains documentation chunks from repositories (ADD REPOS).

Postprocessing

After some inspection, some chunks contain text too short to be meaningful, so we decided to remove those by removing chunks whose number of tokens (computed with the same tokenizer of the model to be used for the embeddings) is lower or equal to the 5%:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-base-en-v1.5")

df = ds.to_pandas()
df["token_length"] = df["chunks"].apply(lambda x: len(tokenizer.encode(x)))

df_short = df[df["token_length"] >= df["token_length"].quantile(0.05)]
ds = Dataset.from_pandas(df_short[["filename", "chunks", "repo_name"]], preserve_index=False)