---
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)
```