Duplicate from google/IFEval

Co-authored-by: Lewis Tunstall <lewtun@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+task_categories:
+- text-generation
+language:
+- en
+pretty_name: IFEval
+---
+
+# Dataset Card for IFEval
+
+<!-- Provide a quick summary of the dataset. -->
+
+## Dataset Description
+
+- **Repository:** https://github.com/google-research/google-research/tree/master/instruction_following_eval
+- **Paper:** https://huggingface.co/papers/2311.07911
+- **Leaderboard:** https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
+- **Point of Contact:** [Le Hou](lehou@google.com)
+
+### Dataset Summary
+
+This dataset contains the prompts used in the [Instruction-Following Eval (IFEval) benchmark](https://arxiv.org/abs/2311.07911) for large language models. It contains around 500 "verifiable instructions" such as "write in more than 400 words" and "mention the keyword of AI at least 3 times" which can be verified by heuristics. To load the dataset, run:
+
+```python
+from datasets import load_dataset
+
+ifeval = load_dataset("google/IFEval")
+```
+
+### Supported Tasks and Leaderboards
+
+The IFEval dataset is designed for evaluating chat or instruction fine-tuned language models and is one of the core benchmarks used in the [Open LLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard).
+
+### Languages
+
+The data in IFEval are in English (BCP-47 en).
+
+## Dataset Structure
+
+### Data Instances
+
+An example of the `train` split looks as follows:
+
+```
+{
+    "key": 1000,
+    "prompt": 'Write a 300+ word summary of the wikipedia page "https://en.wikipedia.org/wiki/Raymond_III,_Count_of_Tripoli". Do not use any commas and highlight at least 3 sections that has titles in markdown format, for example *highlighted section part 1*, *highlighted section part 2*, *highlighted section part 3*.',
+    "instruction_id_list": [
+        "punctuation:no_comma",
+        "detectable_format:number_highlighted_sections",
+        "length_constraints:number_words",
+    ],
+    "kwargs": [
+        {
+            "num_highlights": None,
+            "relation": None,
+            "num_words": None,
+            "num_placeholders": None,
+            "prompt_to_repeat": None,
+            "num_bullets": None,
+            "section_spliter": None,
+            "num_sections": None,
+            "capital_relation": None,
+            "capital_frequency": None,
+            "keywords": None,
+            "num_paragraphs": None,
+            "language": None,
+            "let_relation": None,
+            "letter": None,
+            "let_frequency": None,
+            "end_phrase": None,
+            "forbidden_words": None,
+            "keyword": None,
+            "frequency": None,
+            "num_sentences": None,
+            "postscript_marker": None,
+            "first_word": None,
+            "nth_paragraph": None,
+        },
+        {
+            "num_highlights": 3,
+            "relation": None,
+            "num_words": None,
+            "num_placeholders": None,
+            "prompt_to_repeat": None,
+            "num_bullets": None,
+            "section_spliter": None,
+            "num_sections": None,
+            "capital_relation": None,
+            "capital_frequency": None,
+            "keywords": None,
+            "num_paragraphs": None,
+            "language": None,
+            "let_relation": None,
+            "letter": None,
+            "let_frequency": None,
+            "end_phrase": None,
+            "forbidden_words": None,
+            "keyword": None,
+            "frequency": None,
+            "num_sentences": None,
+            "postscript_marker": None,
+            "first_word": None,
+            "nth_paragraph": None,
+        },
+        {
+            "num_highlights": None,
+            "relation": "at least",
+            "num_words": 300,
+            "num_placeholders": None,
+            "prompt_to_repeat": None,
+            "num_bullets": None,
+            "section_spliter": None,
+            "num_sections": None,
+            "capital_relation": None,
+            "capital_frequency": None,
+            "keywords": None,
+            "num_paragraphs": None,
+            "language": None,
+            "let_relation": None,
+            "letter": None,
+            "let_frequency": None,
+            "end_phrase": None,
+            "forbidden_words": None,
+            "keyword": None,
+            "frequency": None,
+            "num_sentences": None,
+            "postscript_marker": None,
+            "first_word": None,
+            "nth_paragraph": None,
+        },
+    ],
+}
+```
+
+### Data Fields
+
+The data fields are as follows:
+
+* `key`: A unique ID for the prompt.
+* `prompt`: Describes the task the model should perform.
+* `instruction_id_list`: An array of verifiable instructions. See Table 1 of the paper for the full set with their descriptions.
+* `kwargs`: An array of arguments used to specify each verifiable instruction in `instruction_id_list`.
+
+### Data Splits
+
+|               | train |
+|---------------|------:|
+| IFEval        | 541   |
+
+### Licensing Information
+
+The dataset is available under the [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).
+
+### Citation Information
+
+```
+@misc{zhou2023instructionfollowingevaluationlargelanguage,
+      title={Instruction-Following Evaluation for Large Language Models}, 
+      author={Jeffrey Zhou and Tianjian Lu and Swaroop Mishra and Siddhartha Brahma and Sujoy Basu and Yi Luan and Denny Zhou and Le Hou},
+      year={2023},
+      eprint={2311.07911},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2311.07911}, 
+}
+```