Remove tag mb
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GitHub Topics Scraper | Web-Scraping by Python

Becoming Human

title_class = 'f3 lh-condensed mb-0 mt-1 Link--primary' : This line defines the CSS class name ( title_class ) for the HTML element that contains the topic titles on the web page. It retrieves a list of BeautifulSoup Tag objects representing the topic title tags.

Python 59
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Efficiently fine-tune the ESM-2 protein language model with Amazon SageMaker

AWS Machine Learning Blog

e-]*) MB", }, {"Name": "train_loss", "Regex": "'loss': ([0-9.e-]*)"}, e-]*)"}, { "Name": "train_samples_per_second", "Regex": "'train_samples_per_second': ([0-9.e-]*)", e-]*)", }, {"Name": "eval_loss", "Regex": "'eval_loss': ([0-9.e-]*)"}, e-]*)"}, {"Name": "eval_accuracy", "Regex": "'eval_accuracy': ([0-9.e-]*)"},

AWS 95
professionals

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Introducing spaCy v2.3

Explosion

Japanese The updated Japanese language class switches to SudachiPy for word segmentation and part-of-speech tagging. Language Model Size TAG UAS LAS ENTS F Chinese zh_core_web_sm 45 MB 89.63 zh_core_web_md 75 MB 90.23 zh_core_web_lg 575 MB 90.55 Danish da_core_news_sm 16 MB 92.79 da_core_news_md 46 MB 94.13

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Get insights on your user’s search behavior from Amazon Kendra using an ML-powered serverless stack

AWS Machine Learning Blog

docker tag :latest.dkr.ecr.us-east-1.amazonaws.com/ The memory sizes you can choose are 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB. amazonaws.com docker build -t. amazonaws.com/ :latest docker push.dkr.ecr.us-east-1.amazonaws.com/

ML 71
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Building Your Own ChatGPT with OpenAI API: A Step-by-Step Guide

Mlearning.ai

Replace the code the <form> tag with this code: <form className="relative" onSubmit=""> <input type="text" placeholder= {generatePlaceholder(currentPrompt)} value={userInput} required onChange={(e)=>setUserInput(e.target.value)} rows="1" className="block p-2.5 826.95l1.414 4.925A1.5

AI 52
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Overcoming LLMs’ Analytic Limitations Through Suitable Integrations

Towards AI

This 66 MB corpus contains 50K documents or ~13.9M It has functions for the analysis of explicit text elements such as words, n-grams, POS tags, and multi-word expressions, as well as implicit elements such as clusters, anomalies, and biases. tokens, so it’s not particularly large.

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Containerization of Machine Learning Applications

Heartbeat

placeholder = 'Not on time' </script> {% elif prediction == 0%} <script> document.getElementById('disabledTextInput').placeholder placeholder = 'Not on time' </script> {% elif prediction == 0%} <script> document.getElementById('disabledTextInput').placeholder