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| # import torch | |
| # from nltk import sent_tokenize | |
| # import nltk | |
| # from tqdm import tqdm | |
| # from transformers import T5ForConditionalGeneration, T5Tokenizer | |
| # nltk.download("punkt") | |
| # # autodetect the available device | |
| # GPU_IDX = 1 # which GPU to use | |
| # if torch.cuda.is_available(): | |
| # num_gpus = torch.cuda.device_count() | |
| # print(f"Number of available GPUs: {num_gpus}") | |
| # assert GPU_IDX < num_gpus, f"GPU index {GPU_IDX} not available." | |
| # device = torch.device(f"cuda:{GPU_IDX}") | |
| # print(f"Using GPU: {GPU_IDX}") | |
| # else: | |
| # print("CUDA is not available. Using CPU instead.") | |
| # device = torch.device("cpu") | |
| # # Configuration for models and their adapters | |
| # model_config = { | |
| # "Base Model": "polygraf-ai/poly-humanizer-base", | |
| # "Large Model": "polygraf-ai/poly-humanizer-large", | |
| # # "XL Model": { | |
| # # "path": "google/flan-t5-xl", | |
| # # "adapters": { | |
| # # "XL Model Adapter": "polygraf-ai/poly-humanizer-XL-adapter", | |
| # # "XL Law Model Adapter": "polygraf-ai/poly-humanizer-XL-law-adapter", | |
| # # "XL Marketing Model Adapter": "polygraf-ai/marketing-cleaned-13K-grad-acum-4-full", | |
| # # "XL Child Style Model Adapter": "polygraf-ai/poly-humanizer-XL-children-adapter-checkpoint-4000", | |
| # # }, | |
| # # }, | |
| # } | |
| # # cache the base models, tokenizers, and adapters | |
| # models, tokenizers = {}, {} | |
| # for name, config in model_config.items(): | |
| # path = config if isinstance(config, str) else config["path"] | |
| # # initialize model and tokenizer | |
| # model = T5ForConditionalGeneration.from_pretrained(path, torch_dtype=torch.bfloat16).to(device) | |
| # models[name] = model | |
| # tokenizers[name] = T5Tokenizer.from_pretrained(path) | |
| # # load all avalable adapters, each being additional roughly 150M parameters | |
| # if isinstance(config, dict) and "adapters" in config: | |
| # for adapter_name, adapter_path in config["adapters"].items(): | |
| # model.load_adapter(adapter_path, adapter_name=adapter_name) | |
| # print(f"Loaded adapter: {adapter_name}, Num. params: {model.num_parameters()}") | |
| # def paraphrase_text( | |
| # text, | |
| # model_name="Base Model", | |
| # temperature=1.2, | |
| # repetition_penalty=1.0, | |
| # top_k=50, | |
| # length_penalty=1.0, | |
| # ): | |
| # # select the model, tokenizer and adapter | |
| # if "XL" in model_name: # dynamic adapter load/unload for XL models | |
| # # all adapter models use the XL model as the base | |
| # tokenizer, model = tokenizers["XL Model"], models["XL Model"] | |
| # # set the adapter if it's not already set | |
| # if model.active_adapters() != [f"{model_name} Adapter"]: | |
| # model.set_adapter(f"{model_name} Adapter") | |
| # print(f"Using adapter: {model_name} Adapter") | |
| # else: | |
| # tokenizer = tokenizers[model_name] | |
| # model = models[model_name] | |
| # # paraphrase each chunk of text | |
| # sentences = sent_tokenize(text) # sentence boundary detection | |
| # paraphrases = [] | |
| # for sentence in tqdm(sentences): | |
| # sentence = sentence.strip() | |
| # if len(sentence) == 0: | |
| # continue | |
| # inputs = tokenizer("Please paraphrase this sentence: " + sentence, return_tensors="pt").to(device) | |
| # outputs = model.generate( | |
| # **inputs, | |
| # do_sample=True, | |
| # temperature=temperature, | |
| # repetition_penalty=repetition_penalty, | |
| # max_length=128, | |
| # top_k=top_k, | |
| # length_penalty=length_penalty, | |
| # ) | |
| # paraphrased_sentence = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # paraphrases.append(paraphrased_sentence) | |
| # print(f"\nOriginal: {sentence}") | |
| # print(f"Paraphrased: {paraphrased_sentence}") | |
| # combined_paraphrase = " ".join(paraphrases) | |
| # return combined_paraphrase |