README.md

metadata

title: TheESPnetLeaderBoard
emoji: 🐢
colorFrom: gray
colorTo: pink
sdk: streamlit
sdk_version: 1.44.1
app_file: app.py
pinned: false
license: apache-2.0
short_description: Official ESPnet Leaderboard

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Leaderboard

Model Name	Publisher	Open?	Chatbot Arena Elo	HellaSwag (few-shot)	HellaSwag (zero-shot)	HellaSwag (one-shot)	HumanEval-Python (pass@1)	LAMBADA (zero-shot)	LAMBADA (one-shot)	MMLU (zero-shot)	MMLU (few-shot)	TriviaQA (zero-shot)	TriviaQA (one-shot)	WinoGrande (zero-shot)	WinoGrande (one-shot)	WinoGrande (few-shot)
alpaca-7b	Stanford	no			0.739									0.661
alpaca-13b	Stanford	no	1008

Benchmarks

Benchmark Name	Author	Link	Description
Chatbot Arena Elo	LMSYS	https://lmsys.org/blog/2023-05-03-arena/	"In this blog post, we introduce Chatbot Arena, an LLM benchmark platform featuring anonymous randomized battles in a crowdsourced manner. Chatbot Arena adopts the Elo rating system, which is a widely-used rating system in chess and other competitive games." (Source: https://lmsys.org/blog/2023-05-03-arena/)
HellaSwag	Zellers et al.	https://arxiv.org/abs/1905.07830v1	"HellaSwag is a challenge dataset for evaluating commonsense NLI that is specially hard for state-of-the-art models, though its questions are trivial for humans (>95% accuracy)." (Source: https://paperswithcode.com/dataset/hellaswag)
HumanEval	Chen et al.	https://arxiv.org/abs/2107.03374v2	"It used to measure functional correctness for synthesizing programs from docstrings. It consists of 164 original programming problems, assessing language comprehension, algorithms, and simple mathematics, with some comparable to simple software interview questions." (Source: https://paperswithcode.com/dataset/humaneval)
LAMBADA	Paperno et al.	https://arxiv.org/abs/1606.06031	"The LAMBADA evaluates the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative passages sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole passage, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse." (Source: https://huggingface.co/datasets/lambada)
MMLU	Hendrycks et al.	https://github.com/hendrycks/test	"The benchmark covers 57 subjects across STEM, the humanities, the social sciences, and more. It ranges in difficulty from an elementary level to an advanced professional level, and it tests both world knowledge and problem solving ability. Subjects range from traditional areas, such as mathematics and history, to more specialized areas like law and ethics. The granularity and breadth of the subjects makes the benchmark ideal for identifying a model’s blind spots." (Source: "https://paperswithcode.com/dataset/mmlu")
TriviaQA	Joshi et al.	https://arxiv.org/abs/1705.03551v2	"We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions." (Source: https://arxiv.org/abs/1705.03551v2)
WinoGrande	Sakaguchi et al.	https://arxiv.org/abs/1907.10641v2	"A large-scale dataset of 44k [expert-crafted pronoun resolution] problems, inspired by the original WSC design, but adjusted to improve both the scale and the hardness of the dataset." (Source: https://arxiv.org/abs/1907.10641v2)