|
|
--- |
|
|
configs: |
|
|
- config_name: gift_ctx |
|
|
data_files: |
|
|
- split: train |
|
|
path: gift_ctx/train.parquet |
|
|
--- |
|
|
|
|
|
## Gift-EvalCTX Parquet |
|
|
This repository hosts the GIFT-EvalCTX dataset in parquet format, highly compatible with LLMs. Each row is a sample of the dataset and contain the following fields: |
|
|
- **idx**: unique id of the sample |
|
|
- **source**: source of the same |
|
|
- **skill**: the types of context |
|
|
- **frequency**: frequency of the sample |
|
|
- **history_values**: a string containing values of the history |
|
|
- **history_start**: starting timestamp of the history |
|
|
- **history_end**: ending timestamp of the history |
|
|
- **future_values**: a string containing values of the forecasting future |
|
|
- **future_start**: starting timestamp of the future |
|
|
- **future_end**: ending timestamp of the future |
|
|
- **entry_sep**: separator used to split the string in history_values, future_values, and roi into array of floats |
|
|
- **roi**: region of interest, indicating the indices of the timestamp that are affected by the context and used in evaluation |
|
|
- **pred_length**: number of timestamps to forecast |
|
|
- **system_prompt**: default system prompt |
|
|
- **user_instruct**: query to forecast |
|
|
- **context_info**: context for the current sample |
|
|
- **prompt**: a complete prompt to query LLM |
|
|
|
|
|
Note that all fields contain string only, you need to convert them into the appropriate format (array of floats, or datetime). |
|
|
|
|
|
## Example usage |
|
|
```python |
|
|
from datasets import load_dataset |
|
|
|
|
|
ds = load_dataset( |
|
|
"Salesforce/GiftEvalCTX", |
|
|
"gift_ctx", |
|
|
split="train" |
|
|
) |
|
|
print(len(ds)) |
|
|
print(ds[0].keys()) |
|
|
``` |
|
|
|
|
|
This repository is made public for research purposes only. |
