赞
踩
使用GPT2预训练模型的方法
flyfish
transformers库对所有模型统一的API
安装
pip install transformers
GPT2模型主要包括以下文件
config.json
merges.txt
model.safetensors
tokenizer.json
tokenizer_config.json
vocab.json
模型所在目录
\.cache\huggingface\hub\models--openai-community--gpt2\blobs
模型链接
.cache\huggingface\hub\models--openai-community--gpt2\snapshots
config.json [..\..\blobs\10c66461e4c109db5a2196bff4bb59be30396ed8]
merges.txt [..\..\blobs\226b0752cac7789c48f0cb3ec53eda48b7be36cc]
model.safetensors [..\..\blobs\248dfc3911869ec493c76e65bf2fcf7f615828b0254c12b473182f0f81d3a707]
tokenizer.json [..\..\blobs\4b988bccc9dc5adacd403c00b4704976196548f8]
tokenizer_config.json [..\..\blobs\be4d21d94f3b4687e5a54d84bf6ab46ed0f8defd]
vocab.json [..\..\blobs\1f1d9aaca301414e7f6c9396df506798ff4eb9a6]
可以到这里下载
链接:https://pan.baidu.com/s/1A8MLV_BxcJLEIr4_oOVsUQ
提取码:0000
简单示例
from transformers import AutoTokenizer, GPT2Model
import torch
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
model = GPT2Model.from_pretrained("openai-community/gpt2")
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
neuralforecast 的用法
from neuralforecast import NeuralForecast from neuralforecast.models import TimeLLM from neuralforecast.utils import AirPassengersPanel, augment_calendar_df from transformers import GPT2Config, GPT2Model, GPT2Tokenizer AirPassengersPanel, calendar_cols = augment_calendar_df(df=AirPassengersPanel, freq='M') Y_train_df = AirPassengersPanel[AirPassengersPanel.ds<AirPassengersPanel['ds'].values[-12]] # 132 train Y_test_df = AirPassengersPanel[AirPassengersPanel.ds>=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test gpt2_config = GPT2Config.from_pretrained('openai-community/gpt2') gpt2 = GPT2Model.from_pretrained('openai-community/gpt2', config=gpt2_config) gpt2_tokenizer = GPT2Tokenizer.from_pretrained('openai-community/gpt2') prompt_prefix = "The dataset contains data on monthly air passengers. There is a yearly seasonality" timellm = TimeLLM(h=12, input_size=36, llm=gpt2, llm_config=gpt2_config, llm_tokenizer=gpt2_tokenizer, prompt_prefix=prompt_prefix, batch_size=24, windows_batch_size=24) nf = NeuralForecast( models=[timellm], freq='M' ) nf.fit(df=Y_train_df, val_size=12) forecasts = nf.predict(futr_df=Y_test_df)
Copyright © 2003-2013 www.wpsshop.cn 版权所有,并保留所有权利。