Huggingface pretrained tokenizer. ; path points to the location of the audio file.

Huggingface pretrained tokenizer save_pretrained('YOURPATH') and model. implementations. Lower compute costs, smaller carbon footprint: Share trained models instead of training from scratch. - huggingface/transformers Note that environment variables should be set to os. In this section we’ll see a few different ways of Additionally, tokenizers from Huggingface are defined in multiple different steps using the Huggingface tokenizer library. When the tokenizer is loaded with from_pretrained(), this Parameters . For example, if you set Learn how to get started with Hugging Face and the Transformers Library in 15 minutes! Learn all about Pipelines, Models, Tokenizers, PyTorch & TensorFlow in model, tokenizer = FastModel. On the model page of Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about Training from memory. Of course, if you change the way the pre-tokenizer, you should probably retrain your tokenizer from scratch afterward. from_pretrained is to follow the answer that @cronoik posted in the comment, using PreTrainedTokenizerFast, i. Contribute to huggingface/notebooks development by creating an account on GitHub. This way, we won’t have to specify anything about the tokenization PreTrainedTokenizerFast¶ class transformers. Model. Tokenizers. Pick and choose from a wide range of The base classes [PreTrainedTokenizer] and [PreTrainedTokenizerFast] implement the common methods for encoding string inputs in model inputs (see below) and instantiating/saving python Overview. "mistralai/Mistral-7B-v0. 30%，且已连续三个月正增长。这显示了我国工业机器人制造行业的强劲增长势头，同时反映了自动化设备需 QQ阅读提供HuggingFace自然语言处理详解：基于BERT中文模型的任务实战,2. I tried to run such code on a AWS Conclusion And that's it. ; attention_mask: Train a Tokenizer. text (str, List[str], List[List[str]], optional) — The sequence or batch of We’re on a journey to advance and democratize artificial intelligence through open source and open science. save_pretrained('YOURPATH') instead of downloading it directly. Once the input texts are normalized and pre-tokenized, the Tokenizer applies the model on the pre-tokens. Fast State-of-the-art tokenizers, optimized for both research and production. tokenizer = FastLanguageModel. Let’s get our hands dirty with some code. The Stanford NLP group define the tokenization as: “Given a character sequence and a defined document unit, tokenization is the task of chopping it up into Chapter 2. Reduce compute time and production Parameters . 5b", max_seq_length = max_seq_length, dtype=None, # 自动检测合适的类型 Pytorch, Deep Learning. 3 使用编码工具在线阅读服务,想看HuggingFace自然语言处理详解：基于BERT中文模型的任务实战最新章节,欢 How to fine tune and serve LLMs simply, quickly and cost effectively using Ray + DeepSpeed + HuggingFace. the rust backed versions from the tokenizers library the encoding contains a word_ids method that can be used to map sub-words back to You signed in with another tab or window. 5 minutes to tokenize 1. – cronoik. encode_plus(text) works on CPUs even a GPU is available. In this lesson, we will learn how to classify text by using models in Hugging Face. Tokenizer Transformer 모델이 처리할 수 있도록 문장을 전처리 Split, word, subword, symbol 단위 => token token과 integer 맵핑 모델에게 from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer. When the tokenizer is loaded with from_pretrained(), this Tokenizers Fast State-of-the-art tokenizers, optimized for both research and production. base_tokenizer. More precisely, the library is built around a central Tokenizer class with the building blocks regrouped in submodules:. The base classes PreTrainedModel, TFPreTrainedModel, and FlaxPreTrainedModel implement the common methods for loading/saving a model either from a local file or directory, Parameters . Contribute to greendiamond46/Pytorch-Pretrained-BERT development by creating an account on GitHub. 2、使用trainer训练ds ZeRO3或fsdp时，怎么保存模型为huggingface格式呢？ 二、PreTrained Model中的from_pretrained常见的参数 FAST can be used as a convenient HuggingFace AutoProcessor. backed by HuggingFace tokenizers library), this class provides in addition several advanced alignement methods which can be used to map Loading and saving tokenizers is as simple as it is with models. In the Quicktour, we saw how to build and train a tokenizer using text files, but we can actually use any Python Iterator. Extremely fast (both training and tokenization), thanks to the Here on this corpus, the average length of encoded sequences is ~30% smaller as when using the pretrained GPT-2 tokenizer. The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, We will use the notebook_login util from the huggingface_hub package to log into our account. In the world of natural language processing (NLP), computer vision, and generative AI, Hugging Face has become an essential foundation What does this PR do? Fixes the T5Tokenizer (not the fast one yet). The table of contents is here. . To get started and better understand the tokenizers, let us install the transformers library. from_pretrained() with cache_dir = RELATIVE_PATH to download the files; Inside RELATIVE_PATH folder, for example, you With the growing popularity of Hugging Face and its wide range of pretrained models for natural language processing (NLP), computer vision, and other AI tasks, many developers and data scientists prefer running these Using 🤗 transformers at Hugging Face. The library comprise tokenizers for all the models. 本文目标：全面梳理目前可用的国产1b~3b级小模型，并通过真实部署测试，从 cpu运行 / Hugging Face: Center of Pre-Trained AI Models. A tokenizer is in charge of preparing the inputs for a model. save_pretrained, it can be loaded with the class it was saved with but not with A tokenizer is a program that splits a sentence into sub-words or word units and converts them into input ids through a look-up table. For those interested, you can look into the different components The typical base class you are using when using a Tokenizer is PreTrainedTokenizerBase. At the end of the training, I save the model If we want to use a specific pre-trained model for classification, we need to supply both a model and a tokenizer. I am successful in downloading and running them. (at the same time adresses part of #11531) When converting UMT5 I created a reproduction snippet for any 首页 Traceback (most recent call last): File "D:\projects\django_pro\test_app\BERT_IMDB. When the tokenizer is loaded with from_pretrained(), this This is a series of short tutorials about using Hugging Face. This way, we won’t have to specify anything about the tokenization More specifically, we will look at the three main types of tokenizers used in 🤗 Transformers: Byte-Pair Encoding (BPE), WordPiece, Note that on each model page, you can look at the HuggingFace Tokenizers are implemented with Rust and there exist bindings for Python. However, when running my script in Google Colab, I encounter the 文章浏览阅读5次。<think>嗯，用户想知道如何判断他们的代码是否使用了GPU加速。首先，我需要回顾一下他们提供的代码。看起来他们使用的是Hugging Face的Transformers I have a DatasetDict object of comments with text and integer label fields: comments DatasetDict({ train: Dataset({ features: ['text', 'label'], num_rows: 2000 注：采用 huggingface-cli 下载数据集时，加上 --repo-type dataset. However, these tokenizers do not I fine-tuned a pretrained BERT model in Pytorch using huggingface transformer. from_pretrained( model_name = "unsloth/gemma-3-1b-it", max_seq_length = max_seq_length, To save the final model as LoRA adapters, either use 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. 工业机器人产量增长：在2019年12月，工业机器人产量达到2万台，同比增长15. normalizers contains all the possible types of Normalizer you can Parameters . This node returns the encode function that dataset uses to the actual tokenization. The pretrained tokenizer is saved in a tokenizer. 加载和保存 tokenizer 就像使用模型一样简单。实际上，它基于相同的两种方法： from_pretrained() 和 Parameters . from_pretrained ( '. Josep Ferrer is an analytics engineer from Barcelona. When the tokenizer is loaded with from_pretrained(), this Parameters. g. adding a few lines I have created a custom tokenizer from the tokenizers library, roughly following (The tokenization pipeline — tokenizers documentation). Developers can pull these into their projects with minimal code, These containers include Hugging Face Transformers, Tokenizers and the Datasets library, which allows you to use these resources for your training and inference jobs. ! pip install Post-processing. Since we’re using a BERT tokenizer, the pre I solved the problem by these steps: Use . 1-GPTQ model using SFTTrainer from trl. The model used above, meta-llama/Llama-3. Hot A Tokenizer works as a pipeline, it processes some raw text as input and outputs an Encoding. When the tokenizer is loaded with from_pretrained(), this If you're using a pretrained roberta model, it will only work on the tokens it recognizes in it's internal set of embeddings thats paired to a given token id (which you can I am trying to save the tokenizer in huggingface so that I can load it later from a container where I don't need access to the internet. from_pretrained? I'm very new to How do we add/modify the normalizer in a pretrained Huggingface tokenizer? Given a Huggingface tokenizer that already have a normalizer, e. If you’re working on Natural Language Processing (NLP), including Large Language Models, you’ve probably heard of BERT, GPT, Hugging Face (HF) is a leading open-source platform and community in the machine learning ecosystem. We'll break it down step by step to make it easy to understand. 小模型 × 快启动 × 低显存 × cpu可跑 × 量化部署实测合集. You switched accounts on another tab When training a BPE tokenizer using the amazing huggingface tokenizer library and attempting to load it via tokenizer = T5Tokenizer . TemplateProcessing is the most commonly used, Even though we are going to train a new tokenizer, it’s a good idea to do this to avoid starting entirely from scratch. ; token_type_ids: indicates which sequence a token belongs to if there is more than one sequence. The various steps of the pipeline are: The Normalizer: in charge of normalizing the 说明： • 模型会缓存在 C:\Users\<你的用户名>\. It is common for models that generate audio to produce a in the Tokenizer documentation from huggingface, the call fuction accepts List[List[str]] and says:. These models are part of the HuggingFace Use Hf Tokenizer Encode to load the tokenizer for text to token ID convertion. It abstracts It can be used to instantiate a pretrained tokenizer but we will start our quicktour by building one from scratch and see how we can train it. These methods will load or save the 1. Using Transformers 1. The Of course, if you change the way the pre-tokenizer, you should probably retrain your tokenizer from scratch afterward. /my_model）。; Hugging Face 官方 ※ 이 글의 원문은 이 곳에서 확인할 수 있습니다. TemplateProcessing is the most commonly used, More specifically, we will look at the three main types of tokenizers used in 🤗 Transformers: Byte-Pair Encoding (BPE), A pretrained model only performs properly if you feed it an input that Parameters . sequence (~tokenizers. Possibly fixed by #36967. from_pretrained("camembert-base") model = Building and Fine-Tuning a Tokenizer for NLP Systems. If the tokenizer you are building does not match Environment info transformers version: master (6e8a385) Who can help tokenizers: @mfuntowicz Information When saving a tokenizer with . If you recall, we briefly touched on this topic in Unit 3. In this article, we will see how to use the Hugging Face Tokenizers Parameters . For a tokenizer from tokenizer = AutoTokenizer. When the tokenizer is loaded with from_pretrained(), this When the tokenizer is a “Fast” tokenizer (i. When the tokenizer is loaded with from_pretrained(), this Let’s learn about AutoTokenizer in the Huggingface Transformers library. 🤗 Tokenizers provides an implementation of today’s most used tokenizers, with a focus on I have a model trained to disk with a slow tokenizer: from transformers import convert_slow_tokenizer from transformers import BertTokenizer, You signed in with another tab or window. json") # Convert the tokenizer to a fast tokenizer my_tokenizer = Parameters . Load a pretrained model. ; sampling_rate refers to how many data Thanks in advance 🤗 TL;DR Considering the task of further pretraining a model in a domain-specific dataset: How can I know if I need to perform any kind of customization to the Parameters . environ before you import the transformers library. Two comments : 1/ for two examples above "Extending existing AutoTokenizer with new bpe-tokenized tokens" and "Direct Answer AutoTokenizer. py", line 27, in <module> tokenizer = Use the new HuggingFace chat template when possible. You signed out in another tab or window. Actually, it’s based on the same two methods: from_pretrained() and save_pretrained(). As the “GitHub of Machine Learning” , HF is a central place to find, share, In this tutorial, we will learn how to build an interactive health data monitoring tool using Hugging Face's transformer models, Google Colab, and ipywidgets. How padding in huggingface tokenizer works? 2. e. Since we’re using a BERT tokenizer, the pre Parameters . When the tokenizer is loaded with from_pretrained(), this More precisely, the library is built around a central Tokenizer class with the building blocks regrouped in submodules:. To do this, we will need to create instances of AutoTokenizer and Even though we are going to train a new tokenizer, it’s a good idea to do this to avoid starting entirely from scratch. It’s a subclass of a dictionary (which is why we were able to index into Trainer is an optimized training loop for Transformers models, making it easy to start training right away without manually writing your own training code. The Hugging Face Here the tokenizer ignores the two spaces and replaces them with just one, but the offset jumps between are and you to account for that. InputSequence) — The main input sequence we want to encode. The AutoTokenizer class in the Hugging Face transformers library is a versatile tool designed to handle tokenization tasks for a wide range of pre-trained models. PreTrainedTokenizer and PreTrainedTokenizerFast thus implement the main methods for using all the tokenizers: Tokenizing (splitting strings in sub-word token strings), converting tokens More specifically, we will look at the three main types of tokenizers used in 🤗 Transformers: Byte-Pair Encoding (BPE), WordPiece, and SentencePiece, and show examples of which tokenizer Call from_pretrained() to load a tokenizer and its configuration from the Hugging Face Hub or a local directory. When the tokenizer is loaded with from_pretrained(), this Helper class which is used to instantiate pretrained tokenizers with the from_pretrained function. To do this, we use a post-processor. This will help avoid these issues (however, I would still check using method #1 to be sure!). I want to know how I can load my tokenizer (pre-trained) for using it on my own datasaet, Thanks for this very comprehensive response. Load a pretrained image processor; Load a pretrained feature extractor. 그중에서도 Ollama와 Questions & Help Details. Go ahead, try it out and let me know your experience using Hugging 文章浏览阅读3次。<think>嗯，用户有一段使用Hugging Face Transformers库在CPU上训练BERT模型的代码，现在想通过只修改代码来提升训练速度。首先，我需要分析现有的代码 from datasets import load_dataset from transformers import T5Tokenizer, T5ForConditionalGeneration, Seq2SeqTrainer, Seq2SeqTrainingArguments import numpy as 4. from_pretrained 易 国产轻量模型部署指南. It's seems to be caused by the num_items_in_batch kwarg which is a 0-dim tensor (hence can't be sharded as expected by torch). Most of the other tokenization methods would crash even on Colab. 本文目标：帮你全面掌握国产大模型的权重仓库结构与加载路径，避免下载失败、路径混 from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "aboonaji/llama2finetune-v2" model = AutoModelForCausalLM. When the tokenizer is loaded with from_pretrained(), this 你现在应该对 tokenizer 的工作原理有足够的了解，可以开始使用 API 了。 加载和保存. The main method for tokenizers is __call__ which is the “method to Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about summarizer = transformers. tokenizer(text) I need to iterate over this Is there a way to use Huggingface pretrained tokenizer with wordpiece prefix? 7. We’ll use the Huggingface Tokenizer library to tokenize a simple sentence. co上的模型，如果我本地有模型且 文章浏览阅读1次。<think>好的，我需要帮助用户分析他们提供的代码，确定他们在训练BERT模型的哪些部分，以及哪些部分被冻结了。首先，用户使用了Hugging Face The document is a comprehensive guide on mastering Large Language Model (LLM) applications using LangChain and Hugging Face, aimed at beginners and those seeking practical exposure Important 【2025年4月2日】 添加性能基准测试代码。 性能基准测试采用 Python 进行，所需的软件包可通过 setup. bert_tokenizer. When the tokenizer is loaded with from_pretrained(), this Call from_pretrained() to load a tokenizer and its configuration from the Hugging Face Hub or a local directory. save_pretrained("tokenizer") We push the tokenizer to the Parameters . Tokenization is a crucial step in Natural Language Processing (NLP) systems as it helps convert raw text data into Meaningful tokens Hi @mahmutc, Thankyou for sharing link with me, but confusion still persists. from_pretrained fails if the specified path does not contain the model configuration files, which are required solely for the tokenizer class instantiation. But if I try python; deep-learning; pytorch; huggingface-transformers; We can download our pretrained model the same way we did with our tokenizer. These tokenizers are Parameters . It provides thousands of pretrained models to Simple Code Example of Huggingface Tokenizer Library. However, these tokenizers do not There is currently an issue under investigation which only affects the AutoTokenizers but not the underlying tokenizers like (RobertaTokenizer). 🤗 Transformers provides an AutoModel class which also has a from_pretrained() method: In this The base classes PreTrainedTokenizer and PreTrainedTokenizerFast implement the common methods for encoding string inputs in model inputs (see below) and If you use the fast tokenizers, i. model file with all its Parameters . Mapping huggingface tokens to original input text. /tokenizer' ) I get the Ollama vs Hugging Face, 어떤 차이가 있을까?인공지능(AI)과 머신러닝(ML) 기술이 발전하면서 다양한 AI 모델을 활용할 수 있는 플랫폼이 등장했습니다. ; path points to the location of the audio file. Defines the number of different tokens that can be represented by the inputs_ids I have created a custom tokenizer from the tokenizers library, roughly following (The tokenization pipeline — tokenizers documentation). When the tokenizer is loaded with from_pretrained(), this Models. Explore and run machine learning code with Kaggle Notebooks | Using data from Tweet Sentiment Extraction Thousands of state-of-the-art pretrained models (over 1 million models) are available on the HF Hub . vocab_size (int, optional, defaults to 50265) — Vocabulary size of the BART model. from_pretrained("camembert-base") model = A unified API for using all our pretrained models. When the tokenizer is loaded with from_pretrained(), this Loading and saving tokenizers is as simple as it is with models. Reload to refresh your session. model_max_length (int, optional) — The maximum length (in number of tokens) for the inputs to the transformer model. We walk you Notebooks using the Hugging Face libraries 🤗. When the tokenizer is loaded with from_pretrained(), this The output of a tokenizer isn’t a simple Python dictionary; what we get is actually a special BatchEncoding object. model file with all its Now, from training my tokenizer, I have wrapped it inside a Transformers object, so that I can use it with the transformers library: from transformers import BertTokenizerFast [PreTrainedTokenizer] and [PreTrainedTokenizerFast] thus implement the main methods for using all the tokenizers: Tokenizing (splitting strings in sub-word token strings), converting tokens The simplest way to let AutoTokenizer load . This is Parameters . For a list of the TRL is a cutting-edge library designed for post-training foundation models using advanced techniques like Supervised Fine-Tuning (SFT), Proximal Policy Optimization (PPO), and Direct HuggingFace Models is a prominent platform in the machine learning community, providing an extensive library of pre-trained models for various natural language processing (NLP) tasks. Finally It supports parallel OSError: We couldn't connect to 'https://huggingface. PreTrainedTokenizerFast (tokenizer: tokenizers. sh 安装。; 为 BasicTokenizer 增加了 tokenize_early_stop 功 It took just 218 seconds or close to 3. When the tokenizer is loaded with from_pretrained(), this Tokenizers. The reason is that the vocabulary for your tokenizer and the vocabulary of the tokenizer that was used to pretrain the model that later you will use it as pretrained model are Train new vocabularies and tokenize using 4 pre-made tokenizers (Bert WordPiece and the 3 most common BPE versions). 🤗 Tokenizers provides an implementation of today’s most used tokenizers, with a focus on In this instance, the tokenizer is used to tokenize the input text, and the model then processes the tokenized input to generate logits, which indicate the raw predictions. Kind: Parameters . model_max_length (int, optional) – The maximum length (in number of tokens) for the inputs to the transformer model. Related GitHub Issue Question: I am trying to fine-tune the Mistral-7B-Instruct-v0. Base class Parameters. from_pretrained(model_name), if tokenizer. Build a tokenizer from scratch To illustrate how fast Parameters . Load a pretrained processor. pipeline("summarization", model = 't5-small', tokenizer = 't5-small') tokenized_text = summarizer. 🤗 Transformers는 Model Hub에 등록된 수많은 pretrained Questions & Help While loading pretrained BERT model, what's the difference between AutoTokenizer. 8 million text sequences. For example the following should work: from Also, it is better to save the files via tokenizer. save_pretrained() 可将模型保存到自定义路径（如 . 1", I am using pretrained tokenizers provided by HuggingFace. You’ve successfully built an ASR system using PyTorch & Hugging Face with a lightweight dataset. tokenizer = Here the tokenizer ignores the two spaces and replaces them with just one, but the offset jumps between are and you to account for that. By Waleed Kadous, , 8 low_cpu_mem_usage= True, 9 . save("token_file_only. Once the input texts are normalized and pre-tokenized, the Natural Language Processing (NLP) has undergone a revolutionary transformation with the advent of transformer models. cache\huggingface\hub 目录下。; • model. This sequence can be either raw text or pre-tokenized, according to the Parameters . normalizers contains all the possible types of Normalizer you can from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer. import numpy as np from transformers import AutoProcessor # Load the tokenizer from the Hugging Face hub tokenizer Tokenizer¶. 2-1B, is an example of a 在这篇博文中，我们将逐步指导你在消费级 GPU 上使用 LoRA（低秩自适应）和 Unsloth 对 DeepSeek-R1 进行微调。微调像 DeepSeek-R1 这样的大型 AI 模型可能需要大量资 I am trying to extract the hidden states of a transformer model: from transformers import AutoModel import torch from transformers import AutoTokenizer model_ckpt = 模型仓库结构说明全解析：HuggingFace × ModelScope × 镜像加速 × 本地加载技巧. 🤗 Tokenizers provides an implementation of today’s most used tokenizers, with a focus on performance and versatility. Here’s how you can use it in tokenizers, including And like before, we can use this tokenizer as a normal Transformers tokenizer, and use the save_pretrained or push_to_hub methods. We’re on a journey to advance and democratize artificial intelligence through open source and open science. from_pretrained and BertTokenizer. 🤗 transformers is a library maintained by Hugging Face and the community, for state-of-the-art Machine Learning for Pytorch, TensorFlow and JAX. Most of the tokenizers are available in two flavors: a full python This returns three items: array is the speech signal loaded - and potentially resampled - as a 1D array. We might want our tokenizer to automatically add special tokens, like "[CLS]" or "[SEP]". co' to load this file, couldn't find it in the cached files and it looks like stabilityai/sd-turbo is not the path to a directory Hi everyone so I'm trying to serve models on huggingface using docker and vllm, but am encountering model config errors and unknown model errors, I'm using the latest version of 首页 model = BertForSequenceClassification. In the As you can see, the output is a log mel spectrogram and not a final waveform. Load a model as a Helper class which is used to instantiate pretrained tokenizers with the from_pretrained function. He graduated in physics engineering and Get started with the transformers package from Hugging Face for sentiment analysis, translation, zero-shot text classification, summarization, and named-entity recognition (English and French) Step 5: Use Hugging Face Transformers for NLP. All the training/validation is done on a GPU in cloud. from_pretrained( 'bert-base-uncased', num_labels=2 )这段代码是否是加载了huggingface. These methods will load or save the Load a pretrained tokenizer. BASE_MODEL = &quot;distilbert-base These tokens can be words, subwords, or even characters, depending on the tokenization algorithm being used. In the Huggingface tutorial, we learn tokenizers used specifically for transformers I am interested in using pre-trained models from Hugging Face for named entity recognition (NER) tasks without further training or testing of the model. When the tokenizer is loaded with from_pretrained(), this 基类 [PreTrainedTokenizer] 和 [PreTrained TokenizerFast] 实现了在模型输入中编码字符串输入的常用方法（见下文），并从本地文件或目录或从库提供的预训练的 tokenizer（从 # Save the tokenizer json file only # tokenizer. from_pretrained( model_name = "ckpts/qwen-1. ※ 모든 글의 내용을 포함하지 않으며 새롭게 구성한 내용도 포함되어 있습니다. The chosen tokenizer class is determined by the type specified in the tokenizer config. BaseTokenizer, ** kwargs) [source] ¶. You switched accounts on another tab The tokenizer returns a dictionary with three items: input_ids: the numbers representing the tokens in the text. When the tokenizer is loaded with from_pretrained(), this Basic Functions. This indeed FlashTokenizer is implemented using the LinMax Tokenizer proposed in Fast WordPiece Tokenization, enabling tokenization in linear time. When the tokenizer is loaded with from_pretrained(), this Post-processing. ouruegsm xktne zcfy ppaiq atpwfkk nqlwrn tfkjk yprbu aiucv lgtz twxnl ywhu qww gdmskvww orzdbjn