File size: 1,494 Bytes
54ba1b4 6adeed3 54ba1b4 6adeed3 e3a795c 6adeed3 d1ecb0f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 |
---
license: apache-2.0
task_categories:
- question-answering
- conversational
language:
- en
tags:
- evaluation
pretty_name: MT Bench
size_categories:
- n<1K
---
# MT Bench by LMSYS
This set of evaluation prompts is created by the [LMSYS org](https://huggingface.co/lmsys) for better evaluation of chat models.
For more information, see the [paper](https://arxiv.org/abs/2306.05685).
### Dataset loading
To load this dataset, use 🤗 datasets:
```python
from datasets import load_dataset
data = load_dataset(HuggingFaceH4/mt_bench_prompts, split="train")
```
### Dataset creation
To create the dataset, we do the following for our internal tooling.
* rename `turns` to `prompts`,
* add empty `reference` to remaining prompts (for HF Datasets),
* Use the following code to load and save as a dataset
```python
from datasets import load_dataset
import hashlib
data = load_dataset("json", data_files="https://huggingface.co/datasets/HuggingFaceH4/mt_bench_prompts/raw/main/raw/question.jsonl", split="train")
# %% create_dataset.ipynb 11
def format_example(example):
return {
"prompt": example["prompt"],
"prompt_id": int(hashlib.sha256(''.join(example["prompt"]).encode("utf-8")).hexdigest(), 16) % (10 ** 8),
"category": example["category"],
"reference": example["reference"],
}
formatted_ds = data.map(format_example, num_proc=6, remove_columns=data.column_names)
#
formatted_ds.push_to_hub("HuggingFaceH4/mt_bench_prompts", split="train")
``` |