tree prompt下载 - tree prompt源代码下载

tree prompt

Ai源码

1.0.0

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？树提示？

树提示：有效的任务改编而无需微调，为树纸纸代码。

树提示使用培训示例学习提示树以进行分类，从而提高了基线集合的精度和提高效率。

Quickstart

安装： pip install treeprompt （或克隆此回购和pip install -e . ）。

 from treeprompt . treeprompt import TreePromptClassifier
import datasets
import numpy as np
from sklearn . tree import plot_tree
import matplotlib . pyplot as plt

# set up data
rng = np . random . default_rng ( seed = 42 )
dset_train = datasets . load_dataset ( 'rotten_tomatoes' )[ 'train' ]
dset_train = dset_train . select ( rng . choice (
    len ( dset_train ), size = 100 , replace = False ))
dset_val = datasets . load_dataset ( 'rotten_tomatoes' )[ 'validation' ]
dset_val = dset_val . select ( rng . choice (
    len ( dset_val ), size = 100 , replace = False ))

# set up arguments
prompts = [
    "This movie is" ,
    " Positive or Negative? The movie was" ,
    " The sentiment of the movie was" ,
    " The plot of the movie was really" ,
    " The acting in the movie was" ,
]
verbalizer = { 0 : " Negative." , 1 : " Positive." }
checkpoint = "gpt2"

# fit model
m = TreePromptClassifier (
    checkpoint = checkpoint ,
    prompts = prompts ,
    verbalizer = verbalizer ,
    cache_prompt_features_dir = None ,  # 'cache_prompt_features_dir/gp2',
)
m . fit ( dset_train [ "text" ], dset_train [ "label" ])


# compute accuracy
preds = m . predict ( dset_val [ 'text' ])
print ( ' n Tree-Prompt acc (val) ->' ,
      np . mean ( preds == dset_val [ 'label' ]))  # -> 0.7

# compare to accuracy for individual prompts
for i , prompt in enumerate ( prompts ):
    print ( i , prompt , '->' , m . prompt_accs_ [ i ])  # -> 0.65, 0.5, 0.5, 0.56, 0.51

# visualize decision tree
plot_tree (
    m . clf_ ,
    fontsize = 10 ,
    feature_names = m . feature_names_ ,
    class_names = list ( verbalizer . values ()),
    filled = True ,
)
plt . show ()

参考：

 @ misc { morris2023tree ,
    title = { Tree Prompting : Efficient Task Adaptation without Fine - Tuning },
    author = { John X. Morris and Chandan Singh and Alexander M. Rush and Jianfeng Gao and Yuntian Deng },
    year = { 2023 },
    eprint = { 2310.14034 },
    archivePrefix = { arXiv },
    primaryClass = { cs.CL }
}