전체 글 (172) 썸네일형 리스트형 Machine Learning Specialization - Supervised Machine Learning : Regression and Classification What you will learn- Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression)- Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods- Apply best practices for ML development & use unsupervised learning techniques for uns.. Marian : an efficient, free neural achine translation framework https://huggingface.co/Helsinki-NLP/opus-mt-en-es Helsinki-NLP/opus-mt-en-es · Hugging Face⚡ nouman66/Multilingual_Translator ⚡ gradio-tests/english_to_spanish 👁 gradio-tests/english_to_spanishv4-sse 🚀 Samin-Rob/FOREIGN-WHISPERS 🗣️ jesseplusplus/easy-translate 🤷 ➡️ 🤗 Hellisotherpeople/HF-SHAP 😻 ysharma/TranslateQuoteshuggingface.co This model is based on https://marian-nmt.github.io/ Mari.. Fine-tuned version of BERT : a Transformer Bidirectional Encoder based Architecture trained on MLM (Mask Language Modeling) Hugging Face codehttps://huggingface.co/nickwong64/bert-base-uncased-poems-sentiment nickwong64/bert-base-uncased-poems-sentiment · Hugging Facenickwong64/bert-base-uncased-poems-sentiment Bert is a Transformer Bidirectional Encoder based Architecture trained on MLM(Mask Language Modeling) objective. bert-base-uncased finetuned on the poem_sentiment dataset using HuggingFace Trainer with below t.. poem_sentiment : a sentiment dataset of poem verses from Project Gutenberg https://huggingface.co/datasets/poem_sentiment poem_sentiment · Datasets at Hugging Facewhen i peruse the conquered fame of heroes, and the victories of mighty generals, i do not envy the generals,huggingface.co t5-small : an encoder-decoder model to use summarization, translation, Q&A, text classification. t5-small https://huggingface.co/google-t5/t5-small google-t5/t5-small · Hugging FaceModel Card for T5 Small Table of Contents Model Details Uses Bias, Risks, and Limitations Training Details Evaluation Environmental Impact Citation Model Card Authors How To Get Started With the Model Model Details Model Description The developers of the Thuggingface.co1. model details2. uses3. biases, risks and .. xsum - provides a set of BBC articles and summaries https://huggingface.co/datasets/EdinburghNLP/xsum EdinburghNLP/xsum · Datasets at Hugging FaceSimone Favaro got the crucial try with the last move of the game, following earlier touchdowns by Chris Fusaro, Zander Fagerson and Junior Bulumakau. Rynard Landman and Ashton Hewitt got a try in either half for the Dragons. Glasgow showed far superior strhuggingface.co Extreme Summarization (XSum) data.. NLP - Task08 - Text Generation https://huggingface.co/tasks/text-generation What is Text Generation? - Hugging Face🏆 HuggingFaceH4/open_llm_leaderboardhuggingface.co NLP - Task07 - Text Classification https://huggingface.co/tasks/text-classification 이전 1 2 3 4 5 6 ··· 22 다음 목록 더보기
