PaLM API: البدء السريع للتضمينات باستخدام لغة بايثون

العرض على ai.google.dev تجربة ورقة ملاحظات Colab الاطّلاع على ورقة الملاحظات على GitHub

في ورقة الملاحظات هذه، ستتعرّف على كيفية بدء استخدام PaLM API التي تتيح لك الوصول إلى أحدث النماذج اللغوية الكبيرة من Google. ستتعرّف في هذا القسم على كيفية استخدام ميزات إنشاء التضمين في PaLM API، والاطّلاع على مثال على الإجراءات التي يمكنك اتّخاذها باستخدام هذه التضمينات.

ضبط إعدادات الجهاز

أولاً، عليك تنزيل وتثبيت مكتبة PaLM API Python.

pip install -U google-generativeai

import numpy as np
import google.generativeai as palm

الحصول على مفتاح واجهة برمجة التطبيقات

للبدء، عليك إنشاء مفتاح واجهة برمجة التطبيقات.

palm.configure(api_key='PALM_KEY')

ما هي التضمينات؟

التضمينات هي تقنية تُستخدَم لتمثيل النص (مثل الكلمات أو الجمل أو الفقرات الكاملة) كقائمة بأرقام النقاط العائمة في مصفوفة. هذه الأرقام ليست عشوائية. والفكرة الأساسية هي أن النص الذي له معانٍ متشابهة سيكون له تضمينات متشابهة. ويمكنك استخدام العلاقة بينهما في العديد من المهام المهمة.

إنشاء تضمين

في هذا القسم، سنتعرّف على طريقة إنشاء تضمينات لجزء من النص باستخدام الدالة palm.generate_embeddings في PaLM API. في ما يلي قائمة بالنماذج التي تتيح هذه الدالة.

for model in palm.list_models():
  if 'embedText' in model.supported_generation_methods:
    print(model.name)

models/embedding-gecko-001

استخدِم الدالة palm.generate_embeddings ومرِّر اسم النموذج بالإضافة إلى بعض النصوص. ستحصل على قائمة بقيم النقاط العائمة. ابدأ باستعلام "ماذا تأكل السناجب؟" ونرى مدى ارتباط سلسلتين مختلفتين بها.

x = 'What do squirrels eat?'

close_to_x = 'nuts and acorns'

different_from_x = 'This morning I woke up in San Francisco, and took a walk to the Bay Bridge. It was a good, sunny morning with no fog.'

model = "models/embedding-gecko-001"

# Create an embedding
embedding_x = palm.generate_embeddings(model=model, text=x)
embedding_close_to_x = palm.generate_embeddings(model=model, text=close_to_x)
embedding_different_from_x = palm.generate_embeddings(model=model, text=different_from_x)

print(embedding_x)

{'embedding': [-0.025894878, -0.02103396, 0.003574992, 0.00822288, 0.03276648, -0.10068223, -0.037702546, 0.01079403, 0.0001406235, -0.029412385, 0.01919925, 0.0048481044, 0.070619866, -0.013349887, 0.028378602, -0.018658886, -0.038629908, 0.056883123, 0.06332366, 0.039849922, -0.085393265, -0.016251814, -0.025535949, 0.0049480307, 0.048581485, -0.11295683, 0.033869933, 0.015498774, -0.07306243, 0.000857902, -0.022031788, -0.005298939, -0.08311722, -0.027091762, 0.042790364, 0.023175264, 0.011238991, -0.02432924, -0.0044626957, 0.05167071, 0.023430848, 0.027325166, -0.01492389, -0.018770715, -0.003783692, 0.040971957, -0.044652887, 0.033220302, -0.05659744, -0.055191413, -0.0023204528, -0.043687623, 0.030044463, -0.015966717, -0.04318426, 0.015735775, -0.038352676, -0.005009736, -0.03289721, 0.016246213, -0.005696393, -0.0010992853, -0.02768714, -0.03534994, -0.045970507, 0.05784305, -0.026696421, -0.013302212, 0.007055761, -0.05885901, 0.03330113, 0.04399591, 0.020755561, 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الآن وبعد أن أنشأت التضمينات، لنستخدم ناتج الضرب النقطي لنرى مدى صلة close_to_x وdifferent_from_x بالمعاملين x. يُرجع ناتج الضرب النقطي قيمة بين -1 و1، ويمثل مدى محاذاة الخطين المتجهين بشكلٍ وثيق فيما يتعلق بالاتجاه الذي يشيران إليه. كلما اقتربت القيمة من 0، قل التشابه مع الكائنات (في هذه الحالة، سلسلتان). كلما اقتربت القيمة من 1، زاد تشابهها.

similar_measure = np.dot(embedding_x['embedding'], embedding_close_to_x['embedding'])

print(similar_measure)

0.7314063252924405

different_measure = np.dot(embedding_x['embedding'], embedding_different_from_x['embedding'])

print(different_measure)

0.43560702838194704

كما هو موضّح هنا، تُظهر قيمة حاصل الضرب النقطي الأعلى بين تضمينَي x وclose_to_x ارتباطًا أكبر من تضمينات x وdifferent_from_x.

ما الذي يمكنك فعله بالتضمينات؟

لقد أنشأت أول مجموعة من عمليات التضمين باستخدام PaLM API. ولكن ماذا يمكنك أن تفعل بقائمة قيم النقاط العائمة هذه؟ يمكن استخدام التضمينات لمجموعة متنوعة من مهام معالجة اللغات الطبيعية (NLP)، بما في ذلك:

  • بحث (مستندات، ويب، إلخ.)
  • أنظمة الاقتراح
  • مجمّع
  • تحليل الآراء/تصنيف النصوص

يمكنك الاطّلاع على أمثلة هنا.