- the options ```--train_only``` and ```--eval_only``` allow to save only the train and the eval segments respectively; - we remove the parts of the audio where the participant wearing the microphone is talking (according to the manual transcription); - manual transcription is also ...
fix eval_dataset in example Browse files main (#5109) codemayq committed Aug 7, 2024 1 parent b7ca6c8 commit 823e7c1 Showing 1 changed file with 1 addition and 1 deletion. Whitespace Ignore whitespace Split Unified 2 changes: 1 addition & 1 deletion 2 examples/train_full/llama3_full...
This is an evaluation harness for the HumanEval problem solving dataset described in the paper "Evaluating Large Language Models Trained on Code". It used to measure functional correctness for synthesizing programs from docstrings. It consists of 164 original programming problems, assessing language comp...
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ceval-exam.zip (1.48M) 下载 File Name Size Update Time dev/accountant_dev.csv 3348 2023-05-14 19:38:06 dev/advanced_mathematics_dev.csv 6954 2023-05-14 19:38:06 dev/art_studies_dev.csv 1369 2023-05-14 19:38:06 dev/basic_medicine_dev.csv 1759 2023-05-14 19:38:06 dev/business...
IndicMT Eval: A Dataset to Meta-Evaluate Machine Translation Metrics for Indian Languages Ananya Sai B, Tanay Dixit, Vignesh Nagarajan, Anoop Kunchukuttan, Pratyush Kumar, Mitesh M. Khapra, Raj Dabre ACL 2023|July 2023 Download BibTex
SemiEval 2010 Task8 Dataset 喜爱 1 本赛题使用的数据为公共数据集SemiEval 2010 Task8 Dataset,但我们移除了数据集中的实体标注。数据集中一共有10717条英文文本,分为8000条训练数据以及2717条测试数据,包含9种实体间的关系,数据集文件也有对9种实体关系的详细描述及距离,帮助参赛者更深入的理解各种实体关系的...
JFT/FastEval14k数据集的简介 JFT是用于大规模图像分类数据集的内部Google数据集。它包含超过3.5亿个高分辨率图像,并用17,000个类别中的标签注释。为了评估在JFT上训练的模型的性能,Xception算法的作者使用辅助数据集FastEval14k。 FastEval14k是一个包含14,000个图像的数据集,具有约6,000个类别的密集标注(平均每个图...
Tomoyuki, KajiwaraDanushka, BollegalaYuichi, YoshidaKenichi, Kawarabayashi
if use_cached_eval_dataset: epoch_iterator = self._eval_epoch_iterator If you think we could apply the same approach to tf-keras, you are welcome to open up a PR there. Otherwise we will probably stick to this being fixed on Keras 3. I will close this for now, but if you can...