模型:
Norod78/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。
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。
可用 here
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 * '-')