Unduh tree prompt - Unduh Kode Sumber tree prompt

tree prompt

Kode Sumber AI

1.0.0

Unduh

? Meminta pohon?

Perkumpulan pohon: Adaptasi tugas yang efisien tanpa penyesuaian, kode untuk kertas yang dipesan pohon.

Pohon dorongan menggunakan contoh pelatihan untuk mempelajari pohon petunjuk untuk membuat klasifikasi, menghasilkan akurasi yang lebih tinggi dan efisiensi yang lebih baik daripada ansambel dasar.

QuickStart

Instalasi: pip install treeprompt (atau klon Repo dan 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 ()

Referensi:

 @ 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 }
}