WebMar 3, 2024 · !pip install transformers from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained('t5-small') model = T5ForConditionalGeneration.from_pretrained('t5-small', return_dict=True) input = "My name is Azeem and I live in India" # You can also use "translate English to French" and … WebMar 23, 2024 · Our PEFT fine-tuned FLAN-T5-XXL achieved a rogue1 score of 50.38% on the test dataset. For comparison a full fine-tuning of flan-t5-base achieved a rouge1 score of 47.23. That is a 3% improvements. It is incredible to see that our LoRA checkpoint is only 84MB small and model achieves better performance than a smaller fully fine-tuned model.
google/flan-t5-base · Hugging Face
WebApr 10, 2024 · BMTrain[34] 是 OpenBMB开发的一个大模型训练工具,强调代码简化,低资源与高可用性。在其ModelCenter中,已经构建好如Flan-T5 与 GLM等模型结构可供直接使用。 FastMoE[35] 是一个基于pytorch的用于搭建混合专家模型的工具,并支持训练时数据与模型并行。 结束语 WebApr 12, 2024 · 我们 PEFT 微调后的 FLAN-T5-XXL 在测试集上取得了 50.38% 的 rogue1 分数。相比之下,flan-t5-base 的全模型微调获得了 47.23 的 rouge1 分数。rouge1 分数 … chinese treasury bonds yield
Flan-T5-XXL generates non-sensical text when load_in_8bit=True …
WebT5 uses a SentencePiece model for text tokenization. Below, we use a pre-trained SentencePiece model to build the text pre-processing pipeline using torchtext’s T5Transform. Note that the transform supports both batched and non-batched text input (for example, one can either pass a single sentence or a list of sentences), however the T5 … Web2 days ago · 我们 PEFT 微调后的 FLAN-T5-XXL 在测试集上取得了 50.38% 的 rogue1 分数。相比之下,flan-t5-base 的全模型微调获得了 47.23 的 rouge1 分数。rouge1 分数提高了 3%。 令人难以置信的是,我们的 LoRA checkpoint 只有 84MB,而且性能比对更小的模型进行全模型微调后的 checkpoint 更好。 WebMay 17, 2024 · Apply the T5 tokenizer to the article text, creating the model_inputs object. This object is a dictionary containing, for each article, an input_ids and an attention_mask arrays containing the ... grand whitestone