
# 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")
```
task_categories:question-answering task_categories:conversational size_categories:n<1K language:en license:apache-2.0 evaluation arxiv:2306.05685 region:us
{"language": ["en"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["question-answering", "conversational"], "pretty_name": "MT Bench", "tags": ["evaluation"]}
TAGS
#task_categories-question-answering #task_categories-conversational #size_categories-n<1K #language-English #license-apache-2.0 #evaluation #arxiv-2306.05685 #region-us 


# MT Bench by LMSYS
This set of evaluation prompts is created by the LMSYS org for better evaluation of chat models.
For more information, see the paper.


### Dataset loading
To load this dataset, use  datasets:

### 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

# MT Bench by LMSYS
This set of evaluation prompts is created by the LMSYS org for better evaluation of chat models.
For more information, see the paper. ### Dataset loading
To load this dataset, use  datasets: ### 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
TAGS
#task_categories-question-answering #task_categories-conversational #size_categories-n<1K #language-English #license-apache-2.0 #evaluation #arxiv-2306.05685 #region-us 
 # MT Bench by LMSYS
This set of evaluation prompts is created by the LMSYS org for better evaluation of chat models.
For more information, see the paper. ### Dataset loading
To load this dataset, use  datasets: ### 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
passage: TAGS
#task_categories-question-answering #task_categories-conversational #size_categories-n<1K #language-English #license-apache-2.0 #evaluation #arxiv-2306.05685 #region-us 
# MT Bench by LMSYS
This set of evaluation prompts is created by the LMSYS org for better evaluation of chat models.
For more information, see the paper.### Dataset loading
To load this dataset, use  datasets:### 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