Effective communication, specifically through documentation, is the beating
heart of collaboration among contributors in software development. Recent
advancements in language models (LMs) have enabled the introduction of a new
type of actor in that ecosystem: LM-powered assistants capable of code
generation, optimization, and maintenance. Our study investigates the efficacy
of small language models (SLMs) for generating high-quality docstrings by
assessing accuracy, conciseness, and clarity, benchmarking performance
quantitatively through mathematical formulas and qualitatively through human
evaluation using Likert scale. Further, we introduce DocuMint, as a large-scale
supervised fine-tuning dataset with 100,000 samples. In quantitative
experiments, Llama 3 8B achieved the best performance across all metrics, with
conciseness and clarity scores of 0.605 and 64.88, respectively. However, under
human evaluation, CodeGemma 7B achieved the highest overall score with an
average of 8.3 out of 10 across all metrics. Fine-tuning the CodeGemma 2B model
using the DocuMint dataset led to significant improvements in performance
across all metrics, with gains of up to 22.5% in conciseness. The fine-tuned
model and the dataset can be found in HuggingFace and the code can be found in
the repository.

通过数学公式和人工评估使用 Likert 量表对性能进行定量评估和质量评估，研究了小型语言模型在生成高质量文档字符串方面的有效性，并引入了包含 10 万个样本的大规模监督微调数据集（DocuMint）。

DocuMint: 使用小型语言模型为 Python 生成文档字符串

DocuMint: Docstring Generation for Python using Small Language Models

Performance Benchmarking of HPC systems is an ongoing effort that seeks to
provide information that will allow for increased performance and improve the
job schedulers that manage these systems. We develop a benchmarking tool that
utilizes machine learning models and gathers performance data on
GPU-accelerated nodes while they perform material segmentation analysis. The
benchmark uses a ML model that has been converted from Caffe to PyTorch using
the MMdnn toolkit and the MINC-2500 dataset. Performance data is gathered on
two ERDC DSRC systems, Onyx and Vulcanite. The data reveals that while
Vulcanite has faster model times in a large number of benchmarks, and it is
also more subject to some environmental factors that can cause performances
slower than Onyx. In contrast the model times from Onyx are consistent across
benchmarks.

HPC 系统性能基准测试中发现，使用机器学习模型对使用 GPU 加速节点进行材料分割分析时，Onyx 和 Vulcanite 两个 ERDC DSRC 系统的表现存在差异，Onyx 在各项测试中的模型时间保持一致，而 Vulcanite 在大多数基准测试中速度较快，但也受环境因素影响导致部分性能较慢。

深度学习模型在两个高性能计算系统上的材料分割性能基准测试

Benchmarking Performance of Deep Learning Model for Material  Segmentation on Two HPC Systems

A user-generated text on social media enables health workers to keep track of
information, identify possible outbreaks, forecast disease trends, monitor
emergency cases, and ascertain disease awareness and response to official
health correspondence. This exchange of health information on social media has
been regarded as an attempt to enhance public health surveillance (PHS).
Despite its potential, the technology is still in its early stages and is not
ready for widespread application. Advancements in pretrained language models
(PLMs) have facilitated the development of several domain-specific PLMs and a
variety of downstream applications. However, there are no PLMs for social media
tasks involving PHS. We present and release PHS-BERT, a transformer-based PLM,
to identify tasks related to public health surveillance on social media. We
compared and benchmarked the performance of PHS-BERT on 25 datasets from
different social medial platforms related to 7 different PHS tasks. Compared
with existing PLMs that are mainly evaluated on limited tasks, PHS-BERT
achieved state-of-the-art performance on all 25 tested datasets, showing that
our PLM is robust and generalizable in the common PHS tasks. By making PHS-BERT
available, we aim to facilitate the community to reduce the computational cost
and introduce new baselines for future works across various PHS-related tasks.

使用社交媒体上用户生成的文本数据进行公共卫生监测的研究表明，使用预训练语言模型（PLM）的进展促进了针对社交媒体上公共卫生监测任务的新模型的开发。本文介绍了一种名为 PHS-BERT 的 PLM，该模型在公共卫生监测任务上的表现优于现有的 PLMs，并在广泛的数据集上表现出鲁棒性和泛化性。