{"id":1899,"date":"2024-05-31T07:36:34","date_gmt":"2024-05-31T07:36:34","guid":{"rendered":"https:\/\/www.nicekj.com\/?p=1899"},"modified":"2024-05-31T07:36:54","modified_gmt":"2024-05-31T07:36:54","slug":"tongguoyuanmalejiechatgptyuanlihehexinliucheng-2","status":"publish","type":"post","link":"https:\/\/www.nicekj.com\/tongguoyuanmalejiechatgptyuanlihehexinliucheng-2.html","title":{"rendered":"\u901a\u8fc7\u6e90\u7801\u4e86\u89e3ChatGPT\u539f\u7406\u548c\u6838\u5fc3\u6d41\u7a0b"},"content":{"rendered":"

\n

\"\u901a\u8fc7\u6e90\u7801\u4e86\u89e3ChatGPT\u539f\u7406\u548c\u6838\u5fc3\u6d41\u7a0b\"<\/figure>\n<\/p>\n

ChatGPT<\/h1>\n

ChatGPT\uff0c\u5168\u79f0\u4e3aChat Generative Pre-trained Transformer\uff0c\nChatGPT\u662f\u4e00\u79cd\u57fa\u4e8eTransformer<\/code>\u7684\u9884\u8bad\u7ec3<\/code>\u8bed\u8a00\u6a21\u578b<\/p>\n

\u60f3\u8c61\u4e00\u4e0b\uff0c\u4f60\u6709\u4e00\u4e2a\u8d85\u7ea7\u806a\u660e\u7684\u670b\u53cb\uff0c\u540d\u53ebChatGPT<\/code>\u3002ChatGPT\u80fd\u591f\u9605\u8bfb\u548c\u7406\u89e3\u5927\u91cf\u7684\u4e66\u7c4d\u3001\u6587\u7ae0\u548c\u7f51\u9875\u7b49\u5404\u79cd\u6750\u6599\u3002\u5b83\u8bfb\u5f97\u8d8a\u591a\uff0c\u5c31\u8d8a\u806a\u660e\u3002<\/p>\n

ChatGPT\u8bb0\u5fc6\u529b\u975e\u5e38\u60ca\u4eba,\u4e0d\u4ec5\u80fd\u8bb0\u4f4f\u8bfb\u8fc7\u7684\u6bcf\u4e2a\u8bcd\u5185\u5bb9, \u8fd8\u80fd\u7406\u89e3\u8fd9\u4e9b\u8bcd\u4e4b\u95f4\u7684\u5173\u8054\u548c\u6a21\u5f0f\u3002\u6bd4\u5982\uff0c\u5982\u679c\u4f60\u95ee\u5b83\u4e00\u672c\u5c0f\u8bf4\u7684\u4e3b\u8981\u60c5\u8282\uff0c\u5b83\u4e0d\u4ec5\u80fd\u544a\u8bc9\u4f60\u60c5\u8282\uff0c\u8fd8\u80fd\u5206\u6790\u4eba\u7269\u6027\u683c\uff0c\u751a\u81f3\u9884\u6d4b\u6545\u4e8b\u53ef\u80fd\u7684\u7ed3\u5c40\u3002<\/p>\n

ChatGPT\u8be6\u7ec6\u539f\u7406\u548c\u8bad\u7ec3\u8fc7\u7a0b\u975e\u5e38\u590d\u6742\uff0c\u5e76\u6d89\u53ca\u5230\u5927\u91cf\u7684\u5de5\u7a0b\u6280\u672f\u3001\u5206\u5e03\u5f0f\u8ba1\u7b97\u4ee5\u53ca\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\u3002OpenAI\u4e5f\u6ca1\u6709\u516c\u5f00\u5b8c\u6574\u8bad\u7ec3\u7684\u6e90\u4ee3\u7801\u3002<\/p>\n

\n

\u6709\u4e2a\u5f00\u6e90\u7248\u672c\u7684ChatGPT\nOpenChatKit(github.com\/togethercom\u2026<\/a>)<\/p>\n<\/blockquote>\n

\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5316\u7248\u7684\u6d41\u7a0b\u5e76\u7ed9\u51fa\u90e8\u5206Python\u4f2a\u4ee3\u7801\u6765\u8bf4\u660e\u5173\u952e\u6b65\u9aa4\u3002<\/p>\n

\n

\"ChatGPT\u539f\u7406.png\"<\/figure>\n<\/p>\n

1.\u6570\u636e\u6536\u96c6<\/h1>\n
    \n
  • ChatGPT\u6536\u96c6\u5927\u91cf\u7684\u516c\u5f00\u7684\u6587\u672c\u6570\u636e\uff0c\u5305\u62ec\u7f51\u9875\u3001\u65b0\u95fb\u3001\u4e66\u7c4d\u3001\u5bf9\u8bdd\u548c\u4ee3\u7801\u7b49\u3002<\/li>\n<\/ul>\n\n
      \n
    • \u540c\u65f6, \u5206\u6790\u7f51\u7edc\u4e0a\u7684\u70ed\u70b9\u8bdd\u9898\u548c\u6d41\u884c\u6587\u5316\uff0c\u4ee5\u4e86\u89e3\u6700\u65b0\u7684\u8bed\u8a00\u6a21\u5f0f\u548c\u8868\u8fbe\u65b9\u5f0f\u3002<\/li>\n
    • \u6570\u636e\u8d8a\u5927\u3001\u8d8a\u591a\u6837\u5316\uff0c\u6a21\u578b\u751f\u6210\u7684\u81ea\u7136\u8bed\u8a00\u6587\u672c\u5c31\u8d8a\u597d\u3002<\/li>\n<\/ul>\n\n
      <\/div>
      arduino<\/span>
      \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>def collect_data<\/span>()<\/span>:<\/span><\/span>\n    raw_text_data = []<\/span>\n    <\/span>\n    # \u4f7f\u7528\u722c\u866b\u6216\u8005\u5176\u4ed6\u624b\u6bb5\u6293\u53d6\u6570\u636e<\/span>\n    for<\/span> webpage in web_crawler<\/span>()<\/span>:<\/span><\/span>\n        raw_text_data.append(webpage.text)<\/span>\n<\/span>\n    cleaned_data = [clean_text<\/span>(text) for<\/span> text in raw_text_data]<\/span>\n    <\/span>\n    return<\/span> cleaned_data_<\/span>\n<\/code><\/pre>\n
      2.\u6570\u636e\u6e05\u6d17<\/h1>\n
      \u53bb\u9664HTML\u6807\u7b7e\u3001\u7279\u6b8a\u5b57\u7b26\u7b49,\n\u5e2e\u52a9\u6a21\u578b\u66f4\u597d\u5730\u7406\u89e3\u8f93\u5165\u7684\u6587\u672c\uff0c\u5e76\u63d0\u9ad8\u751f\u6210\u7684\u6587\u672c\u7684\u8d28\u91cf\u3002<\/p>\n
      <\/div>
      ini<\/span>
      \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>def clean_text(text):<\/span>\n    # \u5b9e\u9645\u6e05\u7406\u53ef\u80fd\u5305\u62ec\u5f88\u591a\u64cd\u4f5c<\/span><\/span>\n    cleaned<\/span> = re.sub('<.*?>'<\/span>, ''<\/span>, text)  # \u79fb\u9664HTML\u6807\u7b7e<\/span><\/span>\n    cleaned<\/span> = ''<\/span>.join(c for c in cleaned if c.isprintable())  # \u53ea\u4fdd\u7559\u53ef\u6253\u5370\u5b57\u7b26<\/span><\/span>\n    return cleaned<\/span>\n<\/code><\/pre>\n
      3. \u6570\u636e\u9884\u5904\u7406<\/h1>\n
      \u5b9e\u9645\u9884\u5904\u7406\u8fd8\u4f1a\u5305\u62ec\u53bb\u9664\u566a\u58f0\u3001\u6807\u51c6\u5316\u8bcd\u6c47\u3001\u6784\u5efa\u8bcd\u6c47\u8868\u7b49\u6b65\u9aa4,\u6700\u7ec8\u751f\u6210\u6570\u636e\u96c6(\u8bcd\u6c47\u8868).<\/p>\n
      <\/div>
      ini<\/span>
      \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>def preprocess_data(cleaned_data, tokenizer):<\/span>\n    input_ids, attention_masks<\/span> = [], []<\/span>\n<\/span>\n    for text in cleaned_data:<\/span>\n        '''<\/span>\n        1. \u5206\u8bcd\uff1a\u5c06\u8f93\u5165\u7684\u6587\u672c\u5206\u5272\u6210\u5355\u8bcd\u6216\u5b50\u8bcd\u3002<\/span>\n        2. \u6dfb\u52a0\u7279\u6b8a\u6807\u8bb0\uff1a\u53ef\u80fd\u4f1a\u6dfb\u52a0\u4e00\u4e9b\u7279\u6b8a\u7684\u6807\u8bb0\u6216\u6807\u8bc6\u7b26\uff0c\u7528\u4e8e\u8868\u793a\u6587\u672c\u7684\u5f00\u5934\u548c\u7ed3\u5c3e\uff0c\u6216\u8005\u7528\u4e8e\u533a\u5206\u4e0d\u540c\u7684\u90e8\u5206\u3002<\/span>\n        3. \u7f16\u7801\uff1a\u5c06\u5355\u8bcd\u6216\u5b50\u8bcd\u6620\u5c04\u5230\u7279\u5b9a\u7684\u7f16\u7801\u6216\u7d22\u5f15\u3002<\/span>\n        4. \u6dfb\u52a0\u5176\u4ed6\u4fe1\u606f\uff1a\u53ef\u80fd\u4f1a\u6dfb\u52a0\u4e00\u4e9b\u989d\u5916\u7684\u4fe1\u606f\uff0c\u4f8b\u5982\u8bcd\u7684\u4f4d\u7f6e\u3001\u8bcd\u6027\u6807\u8bb0\u7b49\u3002<\/span>\n        '''<\/span>\n        encoded<\/span> = tokenizer.encode_plus(<\/span>\n            text,<\/span>\n            add_special_tokens<\/span>=True<\/span>,<\/span>\n            max_length<\/span>=tokenizer.model_max_length,<\/span>\n            padding<\/span>='max_length'<\/span>,<\/span>\n            truncation<\/span>=True<\/span>,<\/span>\n            return_tensors<\/span>='pt'<\/span><\/span>\n        )<\/span>\n        input_ids.append(encoded['input_ids']<\/span>)<\/span>\n        attention_masks.append(encoded['attention_mask']<\/span>)<\/span>\n<\/span>\n    dataset<\/span> = torch.utils.data.TensorDataset(torch.cat(input_ids), torch.cat(attention_masks))<\/span>\n<\/span>\n    # \u8fd4\u56de\u6570\u636e\u96c6<\/span><\/span>\n    return dataset<\/span>\n<\/code><\/pre>\n
      4.\u8bad\u7ec3<\/h1>\n
      \u751f\u6210\u6700\u7ec8\u7684\u6a21\u578b<\/p>\n
      <\/div>
      ini<\/span>
      \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>def train(dataset, optimizer):<\/span>\n    # \u521d\u59cb\u5316\u8bad\u7ec3\u6a21\u578b\u548c\u914d\u7f6e<\/span><\/span>\n    config<\/span> = transformers.GPT2Config.from_pretrained('gpt2'<\/span>)<\/span>\n    # \u521b\u5efa\u6a21\u578b\u5b9e\u4f8b<\/span><\/span>\n    model<\/span> = GPT2LMHeadModel(config)<\/span>\n<\/span>\n    # \u5c06\u6a21\u578b\u79fb\u52a8\u5230\u6307\u5b9a\u7684\u8bbe\u5907\u4e0a\u8fdb\u884c\u8bad\u7ec3\uff0c\u5982 GPU\u3002<\/span><\/span>\n    device<\/span> = \"cuda\"<\/span> # \u6216\u8005 \"cuda:0\" \u7b49\u5177\u4f53\u7684 GPU \u7d22\u5f15<\/span><\/span>\n    model.to(device)<\/span>\n<\/span>\n    # \u5728\u6bcf\u4e2aepoch\u4e2d\uff0c\u5bf9\u6570\u636e\u96c6\u7684\u591a\u4e2a\u6279\u6b21\u8fdb\u884c\u5904\u7406\uff0c\u8ba1\u7b97\u635f\u5931\uff0c\u5e76\u6839\u636e\u635f\u5931\u7684\u68af\u5ea6\u6765\u66f4\u65b0\u6a21\u578b\u7684\u53c2\u6570\uff0c\u4ee5\u9010\u6b65\u6539\u8fdb\u6a21\u578b\u7684\u6027\u80fd<\/span><\/span>\n    for epoch in range(num_epochs):<\/span>\n        for batch in dataloader:<\/span>\n            inputs, masks<\/span> = batch<\/span>\n            inputs, masks<\/span> = inputs.to(device), masks.to(device)<\/span>\n            <\/span>\n            outputs<\/span> = model(inputs, masks)<\/span>\n            loss<\/span> = compute_loss(outputs, targets)  # \u6839\u636e\u5b9e\u9645\u60c5\u51b5\u5b9a\u4e49\u635f\u5931\u51fd\u6570<\/span><\/span>\n            <\/span>\n            optimizer.zero_grad()<\/span>\n            loss.backward()<\/span>\n            optimizer.step()<\/span>\n        <\/span>\n    trainer<\/span> = Trainer(<\/span>\n        model<\/span>=model,<\/span>\n        args<\/span>=training_args,<\/span>\n        train_dataset<\/span>=dataset,<\/span>\n    )<\/span>\n    trainer.train()<\/span>\n<\/code><\/pre>\n
      5. \u5fae\u8c03<\/h1>\n
      \u5bf9\u6a21\u578b\u8fdb\u884c\u5fae\u8c03\u4ee5\u9002\u5e94\u7279\u5b9a\u4efb\u52a1, \u8fdb\u4e00\u6b65\u63d0\u9ad8\u6a21\u578b\u5728\u7279\u5b9a\u9886\u57df\u7684\u6027\u80fd\u548c\u8868\u73b0\u3002<\/p>\n
      <\/div>
      ruby<\/span>
      \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>def<\/span> fine_tune_on_task<\/span>(model, task_data<\/span>):<\/span>\n    # \u66f4\u65b0\u8bad\u7ec3\u53c2\u6570\u4ee5\u9002\u5e94\u5fae\u8c03\u4efb\u52a1<\/span><\/span>\n    training_args.num_train_epochs = 3<\/span>  # \u793a\u4f8b\uff1a\u51cf\u5c11\u9884\u8bad\u7ec3\u65f6\u957f<\/span><\/span>\n    trainer = Trainer<\/span>(model, train_dataset=task_data)<\/span>\n<\/span>\n    # \u6267\u884c\u5fae\u8c03<\/span><\/span>\n    trainer.train()<\/span>\n    # \u8fd4\u56de\u65b0\u7684\u6a21\u578b<\/span><\/span>\n    return<\/span> model<\/span>\n<\/span>\nclass<\/span> ChatGPTTransformerEncoder<\/span>:<\/span>\n    def<\/span> __init__<\/span>(self<\/span>, config<\/span>):<\/span>\n        self<\/span>.encoder = GPT2Model<\/span>(config)<\/span>\n<\/span>\n    def<\/span> encode<\/span>(self<\/span>, input_ids, attention_mask<\/span>):<\/span>\n        # \u8f93\u5165ids\u7ecf\u8fc7\u5d4c\u5165\u5c42\u3001\u4f4d\u7f6e\u7f16\u7801\u540e\u9001\u5165\u591a\u5c42Transformer\u7f16\u7801\u5668<\/span><\/span>\n        encoded_output = self<\/span>.encoder(input_ids=input_ids, attention_mask=attention_mask)<\/span>\n        <\/span>\n        # \u6700\u540e\u4e00\u5c42Transformer\u7f16\u7801\u5668\u7684\u8f93\u51fa\u4f5c\u4e3a\u4e0a\u4e0b\u6587\u5411\u91cf<\/span><\/span>\n        context_vector = encoded_output.last_hidden_state[:<\/span>, 0<\/span>, :<\/span>]  # \u53d6\u7b2c\u4e00\u4e2aToken\uff08\u901a\u5e38\u662f[CLS]\uff09\u7684\u5411\u91cf<\/span><\/span>\n        <\/span>\n        return<\/span> context_vector, encoded_output.attentions  # \u8fd4\u56de\u4e0a\u4e0b\u6587\u5411\u91cf\u53ca\u5404\u5c42\u6ce8\u610f\u529b\u6743\u91cd<\/span><\/span>\n<\/code><\/pre>\n
      \n
      \"\u901a\u8fc7\u6e90\u7801\u4e86\u89e3ChatGPT\u539f\u7406\u548c\u6838\u5fc3\u6d41\u7a0b\"<\/figure>\n<\/p>\n
      6. \u63a5\u53d7\u5e76\u5904\u7406prompt(\u63d0\u793a\u8bcd)<\/h1>\n
      \u5c06\u6587\u672cprompt\u8f6c\u6362\u4e3a\u6a21\u578b\u6240\u9700\u7684input_ids\u548cattention_mask\u3002\n\u53c2\u6570:<\/p>\n
        \n
      • prompt: \u8f93\u5165\u7684\u6587\u672c\u63d0\u793a<\/li>\n
      • max_length: \u6a21\u578b\u7684\u6700\u5927prompt\u5e8f\u5217\u957f\u5ea6<\/li>\n<\/ul>\n\n
        <\/div>
        ini<\/span>
        \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>def process_prompt(prompt, max_length<\/span>=512<\/span>):<\/span>\n    # \u5bf9prompt\u8fdb\u884c\u7f16\u7801<\/span><\/span>\n    encoding<\/span> = tokenizer.encode_plus(<\/span>\n        prompt,<\/span>\n        add_special_tokens<\/span>=True<\/span>,  # \u6dfb\u52a0\u7279\u6b8atoken\uff08\u5982[CLS]\u548c[SEP]\uff09<\/span><\/span>\n        max_length<\/span>=max_length,<\/span>\n        padding<\/span>='max_length'<\/span>,  # \u82e5\u957f\u5ea6\u4e0d\u8db3\uff0c\u5219\u586b\u5145\u5230\u6700\u5927\u957f\u5ea6<\/span><\/span>\n        truncation<\/span>=True<\/span>,  # \u82e5\u957f\u5ea6\u8d85\u8fc7\uff0c\u5219\u622a\u65ad\u81f3\u6700\u5927\u957f\u5ea6<\/span><\/span>\n        return_tensors<\/span>='pt'<\/span>  # \u8fd4\u56dePyTorch\u5f20\u91cf\u683c\u5f0f<\/span><\/span>\n    )<\/span>\n<\/span>\n    # \u63d0\u53d6\u5fc5\u8981\u7684\u90e8\u5206<\/span><\/span>\n    input_ids<\/span> = encoding['input_ids'<\/span>]<\/span>\n    attention_mask<\/span> = encoding['attention_mask'<\/span>]<\/span>\n<\/span>\n    return input_ids, attention_mask<\/span>\n<\/code><\/pre>\n
        7. Transformer\u7f16\u7801\u9636\u6bb5<\/h1>\n
        <\/div>
        ini<\/span>
        \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>def transformer_encode(input_ids, attention_mask):<\/span>\n    # \u521d\u59cb\u5316\u7f16\u7801\u5668<\/span><\/span>\n    config<\/span> = transformers.GPT2Config.from_pretrained('gpt2'<\/span>)<\/span>\n    encoder<\/span> = ChatGPTTransformerEncoder(config)<\/span>\n<\/span>\n    # \u8fd4\u56de\u4e0a\u4e0b\u6587\u5411\u91cf\u53ca\u5404\u5c42\u6ce8\u610f\u529b\u6743\u91cd<\/span><\/span>\n    context_vector, attentions<\/span> = encoder.encode(input_ids, attention_mask)_<\/span>\n<\/code><\/pre>\n
        8. Transformer\u89e3\u7801\u5e76\u751f\u6210\u56de\u7b54<\/h1>\n
        <\/div>
        ini<\/span>
        \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>def transformer_decode(input_ids, attention_mask, max_length<\/span>=512<\/span>):<\/span>\n    # \u4f7f\u7528\u9884\u8bad\u7ec3\u597d\u7684\u5b8c\u6574GPT\u6a21\u578b\u8fdb\u884c\u89e3\u7801\u751f\u6210<\/span><\/span>\n    model<\/span> = GPT2LMHeadModel.from_pretrained('gpt2'<\/span>)<\/span>\n    tokenizer<\/span> = transformers.GPT2Tokenizer.from_pretrained('gpt2'<\/span>)<\/span>\n<\/span>\n    output<\/span> = model.generate(<\/span>\n        input_ids<\/span>=input_ids,<\/span>\n        max_length<\/span>=max_length,<\/span>\n        pad_token_id<\/span>=model.config.eos_token_id,<\/span>\n        no_repeat_ngram_size<\/span>=3<\/span>,  # \u63a7\u5236\u751f\u6210\u4e0d\u91cd\u590dn-gram<\/span><\/span>\n        do_sample<\/span>=False<\/span>,  # \u5f00\u542f\u6216\u5173\u95ed\u91c7\u6837\u751f\u6210<\/span><\/span>\n        temperature<\/span>=1.0<\/span>,  # \u63a7\u5236\u751f\u6210\u968f\u673a\u6027<\/span><\/span>\n        top_p<\/span>=1.0<\/span>,  # \u8bbe\u7f6etop-p\u91c7\u6837\u7b56\u7565\uff08\u6b64\u5904\u8bbe\u7f6e\u4e3a1\u4ee3\u8868\u4f7f\u7528\u6982\u7387\u6700\u9ad8\u7684token\uff09<\/span><\/span>\n        eos_token_id<\/span>=model.config.eos_token_id,  # \u6307\u5b9a\u7ed3\u675f\u7b26ID<\/span><\/span>\n        num_return_sequences<\/span>=1<\/span>,  # \u8fd4\u56de\u5355\u4e2a\u5e8f\u5217<\/span><\/span>\n        truncation<\/span>=True<\/span>  # \u5f00\u542f\u622a\u65ad<\/span><\/span>\n    )<\/span>\n<\/span>\n    # \u89e3\u7801\u751f\u6210\u6700\u7ec8\u7528\u6237\u770b\u5230\u7684\u6587\u672c<\/span><\/span>\n    responseText<\/span> = tokenizer.decode(output[0<\/span>], skip_special_tokens=True<\/span>)<\/span>\n    return responseText<\/span>\n<\/code><\/pre>","protected":false},"excerpt":{"rendered":"
        ChatGPT\u662f\u4e00\u79cd\u57fa\u4e8eTransformer\u7684\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b,\u672c\u6587\u4f7f\u7528Python\u4f2a\u4ee3\u7801\u63cf\u8ff0\u5b83\u7684\u6838\u5fc3\u6267\u884c\u6d41\u7a0b<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"rank_math_title":"","rank_math_description":"","rank_math_focus_keyword":"","views":"4","footnotes":""},"categories":[3],"tags":[136,126,127,128,129],"collection":[],"class_list":["post-1899","post","type-post","status-publish","format-standard","hentry","category-fenlei2","tag-136","tag-gpt","tag-ai","tag-128","tag-129"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/posts\/1899","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/comments?post=1899"}],"version-history":[{"count":0,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/posts\/1899\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/media?parent=1899"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/categories?post=1899"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/tags?post=1899"},{"taxonomy":"collection","embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/collection?post=1899"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}