Datasetpublic

minpeter/bfcl-v1-non-live-ast-parsed

[PARSED] BFCL V1 AST (non-live python) The data in this dataset is a subset of the original gorilla-llm/Berkeley-Function-Calling-Leaderboard Subset name multi-turn parallel multiple definition Last turn type number of dataset simple no no no tool_calls 400 multiple no no yes tool_calls 200 parallel no yes no tool_calls 200 parallel_multiple no yes yes tool_calls 200 This is a re-parsing formatting dataset for Python AST parts from V1 of the official… See the full description on the dataset page: https://huggingface.co/datasets/minpeter/bfcl-v1-non-live-ast-parsed.

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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Dataset Card

[PARSED] BFCL V1 AST (non-live python)

The data in this dataset is a **subset** of the original gorilla-llm/Berkeley-Function-Calling-Leaderboard

Subset name	multi-turn	parallel	multiple definition	Last turn type	number of dataset
simple	no	no	no	tool_calls	400
multiple	no	no	yes	tool_calls	200
parallel	no	yes	no	tool_calls	200
parallel_multiple	no	yes	yes	tool_calls	200

This is a re-parsing formatting dataset for Python AST parts from V1 of the official dataset of BFCL.

Simple (400 AST): Single function evaluation contains the simplest but most commonly seen format, where the user supplies a single JSON function document, with one and only one function call being invoked.

Multiple Function (200 AST): Multiple function category contains a user question that only invokes one function call out of 2 to 4 JSON function documentations. The model needs to be capable of selecting the best function to invoke according to user-provided context.

Parallel Function (200 AST): Parallel function is defined as invoking multiple function calls in parallel with one user query. The model needs to digest how many function calls need to be made and the question to model can be a single sentence or multiple sentence.

Parallel Multiple Function (200 AST): Parallel Multiple function is the combination of parallel function and multiple function. In other words, the model is provided with multiple function documentation, and each of the corresponding function calls will be invoked zero or more times.

Load the dataset

python

from datasets import load_dataset

ds = load_dataset("minpeter/bfcl-v1-non-live-ast-parsed", data_files="*.parquet")
print(ds)

# DatasetDict({
#     train: Dataset({
#         features: ['messages', 'tools', 'extra'],
#         num_rows: 1000
#     })
# })