 ai.google.dev'de görüntüleyin
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 Colab not defterini deneyin
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 GitHub'da not defterini görüntüle
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Bu not defterinde, Google'ın en yeni büyük dil modellerine erişmenizi sağlayan PaLM API'yi kullanmaya nasıl başlayacağınızı öğreneceksiniz. Burada, PaLM API'nin yerleştirme oluşturma özelliklerini nasıl kullanacağınızı öğrenebilir ve bu yerleştirmelerle neler yapabileceğinize dair bir örnek görebilirsiniz.
Kurulum
Önce PaLM API Python kitaplığını indirip yükleyin.
pip install -U google-generativeai
import numpy as np
import google.generativeai as palm
API Anahtarı Alma
Başlamak için bir API anahtarı oluşturmanız gerekir.
palm.configure(api_key='PALM_KEY')
Yerleştirme nedir?
Yerleştirmeler, bir dizideki kayan nokta sayılarının listesi olarak metni (ör. kelimeler, cümleler veya paragrafların tamamı) temsil etmek için kullanılan bir tekniktir. Bu sayılar rastgele değildir. Temel fikir, benzer anlamlara sahip metinlerin benzer yerleştirmelere sahip olmasıdır. Bunlar arasındaki ilişkiyi birçok önemli görev için kullanabilirsiniz.
Yerleştirme oluşturma
Bu bölümde, PaLM API'nin palm.generate_embeddings işlevi kullanılarak bir metin parçası için nasıl yerleştirme oluşturacağınızı öğreneceksiniz. Bu işlevi destekleyen modellerin listesi aşağıda verilmiştir.
for model in palm.list_models():
if 'embedText' in model.supported_generation_methods:
print(model.name)
models/embedding-gecko-001
palm.generate_embeddings işlevini kullanın ve modelin adıyla birlikte metin de iletin. Kayan nokta değerlerinin listesini görürsünüz. "Sincaplar ne yer?" diye bir sorguyla başlayın. ve iki farklı dizenin birbiriyle ne kadar alakalı olduğuna bakabilirsiniz.
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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Yerleştirmeleri oluşturduğunuza göre, close_to_x ve different_from_x öğelerinin x ile ne kadar alakalı olduğunu görmek için nokta çarpımını kullanalım. Nokta çarpımı, -1 ile 1 arasında bir değer döndürür ve iki vektörün, gösterdikleri yön açısından ne kadar yakın hizalandığını gösterir. Değer 0'a ne kadar yakınsa nesnelere o kadar az benzer (bu örnekte iki dize) vardır. Değer 1'e ne kadar yakınsa o kadar benzerdir.
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
Burada gösterildiği gibi, x ile close_to_x yerleşimleri arasındaki daha yüksek nokta ürün değeri, x ve different_from_x yerleştirmelerine kıyasla daha fazla alakalılık göstermektedir.
Yerleştirmelerle yapabilecekleriniz
PaLM API ile ilk yerleştirme grubunuzu oluşturdunuz. Peki bu kayan nokta değerleri listesiyle ne yapabilirsiniz? Yerleştirmeler, aşağıdakiler de dahil olmak üzere çok çeşitli doğal dil işleme (NLP) görevlerinde kullanılabilir:
- Arama (dokümanlar, web vb.)
- Öneri sistemleri
- Kümeleme
- Yaklaşım analizi/metin sınıflandırması
Örnekleri burada bulabilirsiniz.