模型:

Norod78/hebrew-gpt_neo-tiny

英文

hebrew-gpt_neo-tiny

基于 EleutherAI's gpt-neo 的希伯来文本生成模型。每个模型在通过 TPU Research Cloud 计划向我提供的 TPUv3-8 上进行训练。

数据集

  • 各种不同的希伯来语语料库 - 我已经提供了 here

  • oscar/unshuffled_deduplicated_he - Homepage | Dataset Permalink

  • 这个开放超大规模抓取的ALMAnaCH语料库是通过使用goclassy架构对Common Crawl语料库进行语言分类和过滤得到的庞大的多语言语料库。

    训练配置

    可用 here

    使用方法

    谷歌Colab笔记本

    可用 here

    Simple usage sample code
    !pip install tokenizers==0.10.2 transformers==4.6.0
    
    from transformers import AutoTokenizer, AutoModelForCausalLM
      
    tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-gpt_neo-tiny")
    model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-gpt_neo-tiny", pad_token_id=tokenizer.eos_token_id)
    
    prompt_text = "אני אוהב שוקולד ועוגות"
    max_len = 512
    sample_output_num = 3
    seed = 1000
    
    import numpy as np
    import torch
    
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    n_gpu = 0 if torch.cuda.is_available()==False else torch.cuda.device_count()
    
    print(f"device: {device}, n_gpu: {n_gpu}")
    
    np.random.seed(seed)
    torch.manual_seed(seed)
    if n_gpu > 0:
        torch.cuda.manual_seed_all(seed)
    
    model.to(device)
    
    encoded_prompt = tokenizer.encode(
        prompt_text, add_special_tokens=False, return_tensors="pt")
    
    encoded_prompt = encoded_prompt.to(device)
    
    if encoded_prompt.size()[-1] == 0:
            input_ids = None
    else:
            input_ids = encoded_prompt
    
    print("input_ids = " + str(input_ids))
    
    if input_ids != None:
      max_len += len(encoded_prompt[0])
      if max_len > 1024:
        max_len = 1024
    
    print("Updated max_len = " + str(max_len))
    
    stop_token = "<|endoftext|>"
    new_lines = "\n\n\n"
    
    sample_outputs = model.generate(
        input_ids,
        do_sample=True, 
        max_length=max_len, 
        top_k=50, 
        top_p=0.95, 
        num_return_sequences=sample_output_num
    )
    
    print(100 * '-' + "\n\t\tOutput\n" + 100 * '-')
    for i, sample_output in enumerate(sample_outputs):
    
      text = tokenizer.decode(sample_output, skip_special_tokens=True)
      
      # Remove all text after the stop token
      text = text[: text.find(stop_token) if stop_token else None]
    
      # Remove all text after 3 newlines
      text = text[: text.find(new_lines) if new_lines else None]
    
      print("\n{}: {}".format(i, text))
      print("\n" + 100 * '-')