generator=pipeline(task="text2text-generation",model="google/flan-t5-small")output=generator("Translate to German: My name is Arthur")print(output) 执行后,自动下载模型文件并进行识别: 2.5 模型排名 在huggingface上,我们将文生文(text2text-generation)模型按下载量从高到低排序,总计3.2万个模型,google...
它使用seq2seq模型进行文本到文本生成(text to text generation)。 从pipeline()中加载Text2TextGenerationPipeline管道,任务标识为"text2text-generation"。 from transformers import pipelinetext2text = pipeline("text2text-generation") 3 任务处理管道(Pipeline)方法 3.1问题回答(question answering) 如果给定一个问...
fromtransformersimportpipeline# 创建推理管道pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer)# 评估模型def evaluate_model(pipe, test_data): predictions = [] for query in test_data['query']: prediction = pipe(query, max_length=128)[0]['generated_text'] predictions.appe...
float16,use_cache=True) pipe = pipeline( "text-generation",model=model,tokenizer=tokenizer,return_full_text=False,max_new_tokens=100) prompt = "### Database Schema\n\n['CREATE TABLE \"stadium\" (\\n\"Stadium_ID\" int,\\n\"Location\" text,\\n\"Name\" text,\\n\"Capacity\" ...
arrow_drop_up3 more_vert Built an application using Transformer Pipeline connected with Google text2text generation FastAPI. Used inbuild modelgoogle/flan-t5-small Libraries include fastapi==0.74.*requests==2.27.* uvicorn[stanard]==0.17.*sentencepiece==0.1.*torch==2.3.*transformers==4.* ...
You can use the pipeline class in your own scripts as shown in the example below. Run the following sample script from optimum-habana/examples/text-generation/text-generation-pipeline.import argparse import logging from pipeline import GaudiTextGenerationPipeline from run_generation import...
Text-Generation Pipeline on Intel® Gaudi® 2 AI AcceleratorWith the Generative AI (GenAI) revolution in full swing, text-generation with open-source transformer models like Llama 2 has become the talk of the town. AI enthusiasts as well as developers are looking to leverage the gen...
pipe = pipeline("text-generation",model=model,tokenizer=tokenizer,return_full_text=False,max_new_tokens=100) prompt ="### Database Schema\n\n['CREATE TABLE \"stadium\" (\\n\"Stadium_ID\" int,\\n\"Location\" text,\\n\"Name\" text,\\n\"Capacity\" int,\\n\"Highest\" int,\\...
model_path="Chat2DB/Chat2DB-SQL-7B"# 此处可换成模型的本地路径tokenizer=AutoTokenizer.from_pretrained(model_path,trust_remote_code=True)model=AutoModelForCausalLM.from_pretrained(model_path,device_map="auto",trust_remote_code=True,torch_dtype=torch.float16,use_cache=True)pipe=pipeline("text-...
We then pass a prompt to the Video Generation pipeline that provides a sequence of generated frames. The separate image frames are then combined using a diffuser's utility function, and a video is saved on the disk. FinalVideofromMuhammad ArhamonVimeo. ...