| --- |
| dataset_info: |
| features: |
| - name: filename |
| dtype: string |
| - name: chunks |
| dtype: string |
| - name: repo_name |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 111941 |
| num_examples: 251 |
| download_size: 37308 |
| dataset_size: 111941 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| --- |
| |
|
|
| ## 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%: |
|
|
| ```python |
| 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) |
| ``` |
