Saravanan666/pypi-org huggingface dataset:
Package: bert-qa
BERT-QABuild question-answering systems using state-of-the-art pre-trained contextualized language models, e.g. BERT. We are working to accelerate the development of question-answering systems based on BERT and TF 2.0!BackgroundThis project is based on our study:Question Generation by Transformers.CitationTo cite this work, use the following BibTeX citation.@article{question-generation-transformers@2019,
  title={Question Generation by Transformers},
  author={Kriangchaivech, Kettip and Wangperawong, Artit},
  journal={arXiv preprint arXiv:1909.05017},
  year={2019}
}RequirementsTensorFlow 2.0 will be installed if not already on your systemInstallationpip install bert_qaExample usageRun Colab demo notebookhere.download pre-trained models and SQuAD datawget -q https://storage.googleapis.com/cloud-tpu-checkpoints/bert/keras_bert/uncased_L-12_H-768_A-12.tar.gz
tar -xvzf uncased_L-12_H-768_A-12.tar.gz
mv -f home/hongkuny/public/pretrained_models/keras_bert/uncased_L-12_H-768_A-12 .download SQuAD datawget -q https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json
wget -q https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.jsonimport, initialize, pre-process data, finetune, and predict!from bert_qa import squad
qa = squad.SQuAD()
qa.preprocess_training_data()
qa.fit()
predictions = qa.predict()evaluateimport json
pred_data = json.load(open('model/predictions.json'))
dev_data = json.load(open('dev-v1.1.json'))['data']
qa.evaluate(dev_data, pred_data)Advanced usageModel typeThe default model is an uncased Bidirectional Encoder Representations from Transformers (BERT) consisting of 12 transformer layers, 12 self-attention heads per layer, and a hidden size of 768. Below are all models currently supported that you can specify withhub_module_handle. We expect that more will be added in the future. For more information, seeTensorFlow's BERT GitHub.BERT-Large, Uncased (Whole Word Masking):
24-layer, 1024-hidden, 16-heads, 340M parametersBERT-Large, Cased (Whole Word Masking):
24-layer, 1024-hidden, 16-heads, 340M parametersBERT-Base, Uncased:
12-layer, 768-hidden, 12-heads, 110M parametersBERT-Large, Uncased:
24-layer, 1024-hidden, 16-heads, 340M parametersBERT-Base, Cased:
12-layer, 768-hidden, 12-heads , 110M parametersBERT-Large, Cased:
24-layer, 1024-hidden, 16-heads, 340M parametersContributingBERT-QA is an open-source project founded and maintained to better serve the machine learning and data science community. Please feel free to submit pull requests to contribute to the project. By participating, you are expected to adhere to BERT-QA'scode of conduct.Questions?For questions or help using BERT-QA, please submit a GitHub issue.
Package: bert-sent-encoding
This is a bert sentence encoding tool.Installpip install --index-url https://pypi.python.org/simple/ bert-sent-encoding==0.2.0orgit clone ssh://git@gitlab.leihuo.netease.com:32200/shaojianzhi/bert-sent-encoding.git
cd bert-sent-encoding
python setup.py installUsefrom bert_sent_encoding import bert_sent_encoding # 1st line
bse = bert_sent_encoding(model_path='bert_sent_encoding/model/chinese_L-12_H-768_A-12', seq_length=64, batch_size=8) # 2nd line
vector = bse.get_vector('你吃饭了吗', word_vector=False, layer=-1)   # 3rd line 1. get vector of string
vectors = bse.get_vector(['你吃饭了吗', '已经吃了呀'], word_vector=False, layer=-1)  # 4th line 2. get vector list of strings
bse.write_txt2vector(input_file, output_file, word_vector=False, layer=-1)   # 5th line 3. get and write vectors of stringsfor 2nd line:bse = bert_sent_encoding(model_path='bert_sent_encoding/model/chinese_L-12_H-768_A-12', seq_length=64, batch_size=8)
*model_path is required, seq_length and batch_size are optionalfor 3rd, 4th and 5th linesvector = bse.get_vector('你吃饭了吗', word_vector=False, layer=-1)   # 3rd line 1. get vector of string
vectors = bse.get_vector(['你吃饭了吗', '已经吃了呀'], word_vector=False, layer=-1)  # 4th line 2. get vector list of strings
bse.write_txt2vector(input_file, output_file, word_vector=False, layer=-1)   # 5th line 3. get and write vectors of strings
*word_vector and layer are optional*for 5th line:bse.write_txt2vector(input_file, output_file)   # 3. get and write vectors of stringspath ofinput_fileandoutput_fileare defined by user and below is content ofinput_file:the first line text
the second line text
...
Package: bertserini-on-telegram
Bertserini-on-TelegramBertserini-on-Telegram is a pytorch Bertserini implementation with a small utility to plug it in a Telegram bot.
The models and dataloaders are written in PyTorchLightning, and are usable on their own, or via PyTorchLightning CLI.Project StructureA typical top-level directory layout.
├── bertserini_on_telegram      # Actual project code
|    ├── data                   # contains the DataModules to handle SQuAD V1.1
|    ├── models                 # holds the Modules to run validation and inference with BERT
|    └── utils                  # general helper functions
├── examples                    # Example scripts of how to run the code
├── LICENSE
└── README.mdInstallationInstall viapippipinstallbertserini-on-telegramUsageThe fastest way to learn how to use this package is by heading on tothe examples folder, and have a look at the python scripts saved in there. In each example folder, there is a config.yaml file holding the run parameters, a script.py file where the actual example code is, and a runnable script.sh file that shows how to run a PytorchLightningCli script with a config file.To run an example, simplycdexamples/<example_dir>
./example_shell_script.shRunning it on TelegramThe telegram example supposes that the used knows how to create a Telegram bot in the first place. Telegram has an awesome tutorial on how to do it, you can find ithere.Once the bot is online, the next step is to insert the bot's private token into the config.yaml file, as shown below (the file needs to be created)./example/telegram_bot/telegram_token_id.yamltoken_id:<token_id>Be careful when adding a bot to a group, it must have the necessary permission to be able to read the messages.Now just run the example as shown in the snippet below, and you are good to go :)cdexamples/telegram_bot
./run_bertbot.sh
Package: bert-slot-tokenizer
bert_slot_tokenizerVersion 0.3bert_slot_tokenizer是一个将slot filling 任务中slot解析为其他格式的工具环境：Python 3Python 2安装：pipinstallbert-slot-tokenizer支持的格式：IOB格式IOBS格式BMES格式SPAN格式使用方法：frombert_slot_tokenizerimportSlotConvertervocab_path='tests/test_data/example_vocab.txt'# you can find a example here --> https://github.com/DevRoss/bert-slot-tokenizer/blob/master/tests/test_data/example_vocab.txtsc=SlotConverter(vocab_path,do_lower_case=True)text='Too YOUNG, too simple, sometimes naive! 蛤蛤+1s蛤蛤蛤嗝'slot={'蛤蛤':'name','+1s':'time','嗝':'语气'}output_text,iob_slot=sc.convert(text,slot,fmt='IOB')output_text,iobs_slot=sc.convert(text,slot,fmt='IOBS')output_text,bmes_slot=sc.convert(text,slot,fmt='BMES')output_text,span_slot=sc.convert(text,slot,fmt='SPAN')print(output_text)# ['too', 'young', ',', 'too', 'simple', ',', 'some', '##times', 'na', '##ive', '!', '蛤', '蛤', '+', '1', '##s', '蛤', '蛤', '蛤', '嗝']print(iob_slot)# ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-name', 'I-name', 'B-time', 'I-time', 'I-time', 'B-name', 'I-name', 'O', 'B-语气']print(iobs_slot)# ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-name', 'I-name', 'B-time', 'I-time', 'I-time', 'B-name', 'I-name', 'O', 'S-语气']print(bmes_slot)# ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-name', 'E-name', 'B-time', 'M-time', 'E-time', 'B-name', 'E-name', 'O', 'S-语气']print(span_slot)# [[11, 12, 'name'], [13, 15, 'time'], [16, 17, 'name'], [19, 19, '语气']]写在最后：感谢BERT对NLP领域的推动感谢开源欢迎PR和issue联系方式：devross1997@gmail.com
Package: bertstem
BERT-STEMBERT model fine-tuned on Science Technology Engineering and Mathematics (STEM) lessons.Install:To install from pip:pip install bertstemQuickstartTo encode sentences :fromBERT_STEM.BertSTEMimport*bert=BertSTEM()# Example dataframe with text in spanishdata={'col_1':[3,2,1],'col_2':['hola como estan','alumnos queridos','vamos a hablar de matematicas']}df=pd.DataFrame.from_dict(data)# Encode sentences using BertSTEM:bert._encode_df(df,column='col_2',encoding='sum')To classify sentences with COPUS models:fromBERT_STEM.BertSTEMimport*# Download BERT for classification (guiding/presenting/administration)bert_classification=BertSTEMForTextClassification(2,model_name='pablouribe/bertstem-copus-guiding')# Example dataframe with text in spanishdata={'col_1':[3,2,1],'col_2':['hola como estan','alumnos queridos','vamos a hablar de matematicas']}df=pd.DataFrame.from_dict(data)# Classify sentences using BertSTEM for COPUS (Guiding):bert_classification.predict(df,'col_2')To use it from HuggingFace:fromBERT_STEM.Encodeimport*importpandasaspdimporttransformers# Download spanish BERTSTEM:model=transformers.BertModel.from_pretrained("pablouribe/bertstem")# Download spanish tokenizer:tokenizer=transformers.BertTokenizerFast.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased",do_lower_case=True,add_special_tokens=False)# Example dataframe with text in spanishdata={'col_1':[3,2,1],'col_2':['hola como estan','alumnos queridos','vamos a hablar de matematicas']}df=pd.DataFrame.from_dict(data)# Encode sentences using BertSTEM:sentence_encoder(df,model,tokenizer,column='col_2',encoding='sum')
Package: bert-summarizer
BERT-based text summarizersTable of ContentsTable of ContentsAbout the ProjectGetting startedUsageRoadmapContributingLicenseContactAcknowledgementsAbout the ProjectThis repository will provide various summarization models using BERT.Getting startedTBWUsageTBWRoadmapBertSumExtContributingTBWLicenseDistributed under the MIT License. SeeLICENSEfor more information.ContactKeisuke Hirota -@rad0717- tahiro.k.ad[at]gmail.comProject Link:https://github.com/k-tahiro/bert-summarizerAcknowledgementsEMNLP 2019 paperText Summarization with Pretrained Encodersnlpyang/PreSumm
Package: bert-text
BERT+ TF Keras For NLP Tasks说明：本项目处于开发阶段，暂时不可用简介：以Tensorflow的Keras和Tensorflow hub的Bert预训练模型开发NLP的各种任务。项目特点下载稳定，较为典型的测试数据，附带详细介绍数据接口封装，减少数据的处理工作模型采用tf.keras完成，方便快捷不失灵活方便的保存模型和部署环境Tensorflow:1.13.1Tensorflow-hubTODO List句子向量化但是存在一个重复加载计算图导致速度变慢的问题，体验不佳，后续将改进。已解决,解决方法是直接使用BERT的分词器,不从hub重新加载,撒花!性能测试Batch输入文本分类二分类任务已完成。数据量不大的情况下可以不使用微调，否则参数量增大可能会过拟合。多分类任务
BERT的多分类任务和二分类类似，只需要修改标签，然后sigmod换softmax,损失函数也换成多分类交叉熵即可。目前没有稳定的外链数据，所以没写example。多标签任务序列标注NER开发中阅读理解斯坦福SQUAD类似的中文检索式阅读理解未完成模型保存和部署h5转saved_model。最好的方式是可以使用tf.keras.experimental.export_saved_model导出模型，然后直接使用TF serving部署。TF Data输入多输入的TF Data写法GPU利用率的比较其他计划Tensorflow hub加载本地路径Windows测试其他说明为什么用tf.keras而不是keras
tf.keras成为TF2.0的主要模式，由TF团队开发，支持更多TF的特性包括tf.data以及tf serving，TF2.0出了之后项目会迁移到TF2.0，而keras已经较长时间没有重大更新了，所以tf.keras是更好的选择。由于网络原因，无法下载Tensorflow hub的BERT模型
这里有一份百度云的，链接， 提取码:4pcq，大小为364.1M，linux下载解压后拷贝到/tmp/，完成后的路径为/tmp/tfhub_modules参考项目keras-bert
Package: berttextclassification
About一个用于训练和调用bert模型完成数据分类的python包。Install$ pip3 install -U berttextclassificationDirectorbertbert_lawlaw.pyDataLawlaw.pytrain.pyevaluate.pyacc_and_f1.pyBertModel.pypredict.pybert_lawlaw.pymain程序完成训练到评估，默认是只评估不进行训练。DataLawlaw.py定义了一个LawlawProcessor类，封装了一个，完成分类任务中数据相关的操作。LawlawProcessor类get_labels函数
五个设定好的分类标签load_train_examples函数
加载json格式的训练数据load_dev_examples函数
加载json格式的评估数据train.py对模型进行训练，并保存训练数据。train函数
根据设置的batch_size和epoch进行训练。将lr和loss写到tensorboard中，根据save_steps保存不同checkpoint的模型。evaluate.py对验证集进行评估，得到训练结果。evaluate函数
对选择的model进行评估，并给出acc_and_f1函数返回的三项分值参数。acc_and_f1.py计算bert模型预测后的分值。acc_and_f1函数
计算模型预测标签的三项分值参数acc，f1_score和acc_and_f1二者的平均值。BertModel.py对Bert模型的相关操作，包括设置种子、加载模型和保存模型。set_seed函数
设置种子数值。load_model函数
从output_dir加载model和tokenizersave_model函数
将model和tokenizer保存到output_dirpredict.py对输入的文本进行分类预测。predict函数
使用加载的模型，对输入文本进行分类。Usage对模型进行训练，并保存模型fromBertModelimportset_seed,init_model,load_model,save_modelfromDataLawlawimportLawlawProcessorfromtrainimporttrainset_seed()task='lawlaw'processor=LawlawProcessor(task)label_list=processor.get_labels()model,tokenizer=init_model(task,len(label_list))train_dataset=processor.load_train_examples(tokenizer,max_seq_length=128)global_step,tr_loss=train(model,train_dataset,train_batch_size=8,num_train_epochs=5.0,save_steps=1000,output_dir='./outs')save_model(model,tokenizer,output_dir='./outs')对模型进行测试fromBertModelimportset_seed,init_model,load_model,save_modelfromDataLawlawimportLawlawProcessorfromevaluateimportevaluateset_seed()task='lawlaw'processor=LawlawProcessor(task)model,tokenizer=load_model(output_dir='./outs')eval_dataset=processor.load_dev_examples(tokenizer,max_seq_length=128)result=evaluate(model,eval_dataset,eval_batch_size=16,output_dir='./outs')print(result)Contact usmay.xiaoya.zhang@gmail.com
Package: bert-text-pretty
bert_text_pretty 是一个bert 文本特征化和文本后处理解码的包，用于bert工程推理# -*- coding:utf-8 -*-importnumpyasnpfrombert_text_prettyimportcls,ner,relation,tokenization'''简化文本特征化，解码https://github.com/ssbuild/bert_text_pretty.git'''text_list=["你是谁123456","你是谁123456222222222222"]tokenizer=tokenization.FullTokenizer(vocab_file=r'F:\pretrain\chinese_L-12_H-768_A-12\vocab.txt',do_lower_case=True)feat=cls.cls_text_feature(tokenizer,text_list,max_seq_len=128,with_padding=False)print(feat)feat=ner.ner_text_feature(tokenizer,text_list,max_seq_len=128,with_padding=False)print(feat)feat=relation.re_text_feature(tokenizer,text_list,max_seq_len=128,with_padding=False)print(feat)labels=['标签1','标签2']print(cls.load_labels(labels))print(ner.load_label_bio(labels))# def ner_decoding(example_all, id2label, logits_all,trans=None) # crf 解码'''example_all 文本list ,id2label 标签 list or dictlogits_all 为bert 预测结果 (batch,seq_len,num_tags) or (batch,seq_len)trans 是否启用trans预测 , 2D解析crf序列  or 解析 已经解析过的crf序列'''#ner_pointer_decoding(example_all, id2label, logits_all,threshold=0.) # 指针 解码'''example_all 文本list ,id2label 标签 list or dictlogits_all (batch,num_labels,seq_len,seq_len)threshold 阈值'''#  cls.cls_decoding(example_all,labels,logits) #分类'''example_all 文本list ,id2label 标签 list or dictlogits_all (batch,hidden)threshold 阈值'''# relation.re_decoding(example_all, id2spo, logits_all)  #关旭
Package: bert-tokens
Bert tokens ToolsA useful tools to handle problems when you use Bert.InstallationWith PipThis repository is tested on Python 3.6+, and can be installed using pip as follows:pipinstallbert-tokensUsageTokenization and token-span-convertWordPiece tokenization for BERT, which can be universally applicable for different language versions for BERT. The supported BERT checkpoints including but not limited to:BERT-Base, UncasedBERT-Base, ChineseBERT-Base, Multilingual CasedToken-span-convertConvert token span from char-level to wordpiece-level. This usually happens in multi-lingual scenarios.For example, query="播放mylove"，the char-level index of sequence "mylove" is [2,8], while the token index after bert tokenization should be [2,4]And convert token span from wordpiece-level to char-level, just as the reverse procedure of above.Examplefrombert_tokens.bert_tokenizerimportTokenizerfrombert_tokens.convert_word_spanimportconvert_word_span,convert_char_spandict_path="vocab/vocab.txt"tokenizer=Tokenizer(dict_path,do_lower_case=True)tokens=tokenizer.tokenize("播放MYLOVE")print(tokens)## ['[CLS]', '播', '放', 'my', '##love', '[SEP]']convert_word_span("播放MYLOVE",[2,8],tokenizer)## [2, 4]convert_char_span("播放MYLOVE",[2,4],tokenizer)## [2, 8]
Package: berycker
beryckerDocker-like tool to set up a headless RaspberryPi
ヘッドレスでのRaspberryPiやlinuxの初期設定をdockerライクにする、コマンドラインツールです。
現時点ではラズパイのみ対応installpip install beryckerコマンド一覧initberycker initwifiやsshの設定を行います。
イメージを作ったあと、イメージを焼いたドライブを挿入したままの状態で実行してください。buildberycker buildラズパイが起動した後に実行してください。
beryckerfileを読み込んでマシンの初期設定を行います。
この際、sshの接続で必要なホスト名、ユーザー名、パスワードをインタラクティブに答える必要があります。beryckerfileDockerFileにインスパイアされたものなので、Dockerを使ったことがある方は親しみやすいでしょう。
基本的には関数を実行していく順に並べるだけです。
関数を先頭に書き、スペースに続けて引数を書きます。関数名は大文字で書くことに注意してください。関数は5つあります。FUNCTION_NAME argberyckerfileの例はexampleディレクトリにあります。RUNRUN commandこの関数は任意のコマンドを実行します。ADDADD "string" pathこの関数は任意の文字列を既存のファイルに書き込みます。
絶対パスとホームディレクトリに対しての相対パスをpathに入力できます。任意の文字列はダブルクォーテーションで囲む必要があることに注意してください。IPIP ip_adressこの関数はIPアドレスを任意のIPアドレスに固定します。SSHSSH pub_keyこの関数はSSHの公開鍵をマシンに登録します。公開鍵はテキストファイルに保存するべきではないので、以下の変数を使うことをおすすめします。HOSTNAMEHOSTNAME any_nameこの関数は任意のホスト名をマシンに割り当てます。変数SSH {pub_key}パスワードなどのファイルに記入することがはばかられる値や、マシンごとに値が異なる所（ホスト名など）を変数として定義できます。
beryckerfile内で定義した変数は、ビルドするときにインタラクティブに入力できます。
Package: beryl-events-utils
Beryl-Events-UtilsA set of async utils for Beryl's Events systemInfoBeryl, which is a discord bot offers a events system, where users can set an event and then later check how much more days is there left. The events system is powered off of PostgreSQL and thus requires an PostgreSQL server in order to use. These are the base corountines used by Beryl.InstallationPippipinstallberyl-events-utilsPoetrypoetryaddberyl-events-utilsLicenseMIT
Package: beryllium
Beryllium PackageThis is beryllium package. You can useberylliumto write your lean.InstallationpipinstallberylliumBeryllium Demo# -*- coding: utf-8 -*-fromberylliumimportBerylliumimporttimefromberylliumimportFieldList,Field,FieldName,Page,ListCssSelector,Mongodb,NextPageCssSelectorSetup,PageFuncbery=Beryllium()bery.driver=bery.get_driver()bery.fast_get_page("https://www.baidu.com")time.sleep(1)bery.until_send_text_by_css_selector(css_selector="#kw",text="杭州")bery.until_send_enter_by_css_selector(css_selector="#kw")time.sleep(2)fieldlist_shop=FieldList(Field(field_name=FieldName.SHOP_NAME,css_selector="h3"),)page_shop=Page(name="shop_page",field_list=fieldlist_shop,list_css_selector=ListCssSelector(list_css_selector="#content_left > div.result.c-container"))bery.until_click_no_next_page_by_css_selector(next_page_setup=NextPageCssSelectorSetup(page=page_shop,css_selector="#page > a.n",main_page_func=PageFunc(func=bery.from_page_get_data_list,page=page_shop)))DemonstrationOutlookCommitted to creating an interactive crawler library. I hope programmers with the same desire to join my development. You can contact me by email:wujunleiwang@gmail.com
Package: besalu-notifier
Besalu NotifierA tool to watch the inventory of Cafe Besalu in Seattle, WA.Installationpip install besalu-notifierRunningTo see the names of items as well as what's currently available as a one-off, run:besalu listand then to watch certain items (including push notifications when they're all there):besalu watch "Lemon Blackberry Muffin, Almond Croissant, Bread Pudding"To see what options are available for watch, run:besalu watch --help
Package: besecure-developer-toolkit
BeS-dev-kitBeS-dev-kit is a cli tool for generating metadata and assessment report forBeSLighthouse.Pre-requisitesPython 3.10pipGithub personal access tokenInstallation$ python3 -m pip install besecure-developer-toolkitUsageIf you are running the command for the first time, you will be prompted to provide the complete path to yourbesecure-osspoi-datastoreandbesecure-assessment-datastoreand yourpersonal access tokenGenerate MetadataCommand helps to generate metadata such as OSSP-master file data and version details file.$ bes-dev-kit generate-metadataFor more options use--helpat end.Note:For the first time use this command will ask for three extra inputs -ASSETS_DIR: path ofbesecure-osspoi-datastorein local system,ASSESSMENT_DIR: path ofbesecure-assessment-datastore,GITHUB_AUTH_TOKENGenerate Reports$ bes-dev-kit generate-report-  generate all reports (scorecard, sciticality_score, codeql, sbom)$ bes-dev-kit generate-report < report name > ... < report name >- generate specific report<report name> - scorecard, codeql, criticality_scoreFor more options use--helpat end.Note:If an older version ofcriticality_scoreis already installed in the system (<v2.0.0), Please uninstall it usingpip uninstall criticality-score.Download Risk SummaryCommand helps to download risk-summary of Be-Secure listed open source projects in PDF format.$ bes-dev-kit risk-summaryFor more options use--helpat end.Validate Version Details file$ bes-dev-kit validate-version-fileCommand helps to check version details file naming convention.For more details use--helpat end.Validate Report filesCommand helps to check report files naming convention / if report file available.$ bes-dev-kit validate-report-file <report name> ... <report name>Note: Provide listed parameters only. scorecard, codeql, criticality_score, sonarqube, sbom, fossologyUse bellow command to check all reports at a time.$ bes-dev-kit validate-report-fileFor more details use--helpat end.Setting up locallyInstallpoetry. Use thelinkto install Poetry.Clone the repo.Move into the cloned directory.Run the command -$ poetry add "typer[all]"Create a new virtual env using Poetry -$ poetry shellRun the command to install the tool-$ poetry installCheck installation -$ bes-dev-kit --help
Package: beSMArt
beSMArtPython connector for SMA solar panelUsagefrombeSMArtimportget_data,get_attributedata=get_data("192.168.1.100")# IP of your SMA devicetotal_yield=get_attribute(data,"total_yield")# You can check available fields in table underneathFieldsI got this fromhttps://github.com/kellerza/pysmathanks to @kellerzaAPI_IDFieldUnit6100_40263F00grid_powerW6100_00465700frequencyHz6100_00464800voltage_l1V6100_00464900voltage_l2V6100_00464A00voltage_l3V6100_40465300current_l1A6100_40465400current_l2A6100_40465500current_l3A6100_0046C200pv_powerW6380_40451F00pv_voltageV6380_40452100pv_currentA6400_0046C300pv_gen_meterkWh6400_00260100total_yieldkWh6400_00262200daily_yieldWh6100_40463600grid_power_suppliedW6100_40463700grid_power_absorbedW6400_00462400grid_total_yieldkWh6400_00462500grid_total_absorbedkWh6100_00543100current_consumptionW6400_00543A00total_consumptionkWh
Package: besnappy
BeSnappy HTTP API client=======================An unofficial client library for the `BeSnappy`_ HTTP API... _BeSnappy: https://github.com/BeSnappy/api-docs|gha-ci|_ |gha-cover|_.. |gha-ci| image:: https://travis-ci.org/westerncapelabs/besnappy.png?branch=develop.. _gha-ci: https://travis-ci.org/westerncapelabs/besnappy.. |gha-cover| image:: https://coveralls.io/repos/westerncapelabs/besnappy/badge.png?branch=develop.. _gha-cover: https://coveralls.io/r/westerncapelabs/besnappyIssues can be filed in the GitHub issue tracker. Please don't use the issuetracker for general support queries.Note: API coverage is currently extremely low.