Guides - Embeddings

https://beta.openai.com/docs/guides/embeddings/what-are-embeddings

OpenAI API

 

Embeddings

What are embeddings?

OpenAI’s text embeddings measure the relatedness of text strings. Embeddings are most commonly used for:

• Search (where results are ranked by relevance to a query string)
• Clustering (where text strings are grouped by similarity)
• Recommendations (where items with related text strings are recommended)
• Anomaly detection (where outliers with little relatedness are identified)
• Diversity measurement (where similarity distributions are analyzed)
• Classification (where text strings are classified by their most similar label)

 

OpenAI의 텍스트 임베딩은 텍스트 문자열의 관련성을 측정합니다. 임베딩은 다음 용도로 가장 일반적으로 사용됩니다.
* 검색(쿼리 문자열과의 관련성에 따라 결과 순위가 매겨짐)
* 클러스터링(텍스트 문자열이 유사성에 따라 그룹화됨)
* 권장 사항(관련 텍스트 문자열이 있는 항목이 권장되는 경우)
* 이상 감지(관련성이 거의 없는 이상값이 식별되는 경우)
* 다양성 측정(유사성 분포가 분석되는 경우)
* 분류(여기서 텍스트 문자열은 가장 유사한 레이블로 분류됨)

 

An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness.

Visit our pricing page to learn about Embeddings pricing. Requests are billed based on the number of tokens in the input sent.

 

임베딩은 부동 소수점 숫자의 벡터(목록)입니다. 두 벡터 사이의 거리는 관련성을 측정합니다. 작은 거리는 높은 관련성을 나타내고 먼 거리는 낮은 관련성을 나타냅니다.
임베딩 가격에 대해 알아보려면 가격 페이지를 방문하세요. 요청은 전송된 입력의 토큰 수에 따라 요금이 청구됩니다.

 

To see embeddings in action, check out our code samples

• Classification
• Topic clustering
• Search
• Recommendations

Browse Samples‍

 

How to get embeddings

To get an embedding, send your text string to the embeddings API endpoint along with a choice of embedding model ID (e.g., text-embedding-ada-002). The response will contain an embedding, which you can extract, save, and use.

 

임베딩을 받으려면 임베딩 모델 ID(예: text-embedding-ada-002) 선택과 함께 텍스트 문자열을 임베딩 API 엔드포인트로 보냅니다. 응답에는 추출, 저장 및 사용할 수 있는 임베딩이 포함됩니다.

 

Example requests:

 

response = openai.Embedding.create(
    input="Your text string goes here",
    model="text-embedding-ada-002"
)
embeddings = response['data'][0]['embedding']

 

 

Example response:

 

{
  "data": [
    {
      "embedding": [
        -0.006929283495992422,
        -0.005336422007530928,
        ...
        -4.547132266452536e-05,
        -0.024047505110502243
      ],
      "index": 0,
      "object": "embedding"
    }
  ],
  "model": "text-embedding-ada-002",
  "object": "list",
  "usage": {
    "prompt_tokens": 5,
    "total_tokens": 5
  }
}

 

See more Python code examples in the OpenAI Cookbook.

When using OpenAI embeddings, please keep in mind their limitations and risks.

 

Embedding models

OpenAI offers one second-generation embedding model (denoted with -002 in the model ID) and sixteen first-generation models (denoted with -001 in the model ID).

We recommend using text-embedding-ada-002 for nearly all use cases. It’s better, cheaper, and simpler to use. Read the blog post announcement.

 

OpenAI는 1개의 2세대 임베딩 모델(모델 ID에 -002로 표시됨)과 16개의 1세대 모델(모델 ID에 -001로 표시됨)을 제공합니다.
거의 모든 사용 사례에 대해 text-embedding-ada-002를 사용하는 것이 좋습니다. 더 좋고, 더 저렴하고, 더 간단하게 사용할 수 있습니다. 블로그 게시물 공지사항을 읽어보세요.

 

MODEL GENERATION.  TOKENIZER.                                           MAX INPUT TOKENS.  KNOWLEDGE CUTOFF

V2	cl100k_base	8191	Sep 2021
V1	GPT-2/GPT-3	2046	Aug 2020

 

Usage is priced per input token, at a rate of $0.0004 per 1000 tokens, or about ~3,000 pages per US dollar (assuming ~800 tokens per page):

 

사용량은 입력 토큰당 1,000개 토큰당 $0.0004 또는 미국 달러당 약 3,000페이지(페이지당 800개 토큰으로 가정)의 비율로 가격이 책정됩니다.

 

 

 

First-generation models (not recommended)

 

1 세대 모델 (권장 하지 않음)

 

All first-generation models (those ending in -001) use the GPT-3 tokenizer and have a max input of 2046 tokens.

모든 1세대 모델(-001로 끝나는 모델)은 GPT-3 토크나이저를 사용하며 최대 입력값은 2046개입니다.

 

First-generation embeddings are generated by five different model families tuned for three different tasks: text search, text similarity and code search. The search models come in pairs: one for short queries and one for long documents. Each family includes up to four models on a spectrum of quality and speed:

 

1세대 임베딩은 텍스트 검색, 텍스트 유사성 및 코드 검색의 세 가지 작업에 맞게 조정된 다섯 가지 모델군에 의해 생성됩니다. 검색 모델은 쌍으로 제공됩니다. 하나는 짧은 쿼리용이고 다른 하나는 긴 문서용입니다. 각 제품군에는 다양한 품질과 속도에 대해 최대 4개의 모델이 포함됩니다.

 

MODEL                                                                                                  OUTPUT DIMENSIONS

Ada	1024
Babbage	2048
Curie	4096
Davinci	12288

 

Davinci is the most capable, but is slower and more expensive than the other models. Ada is the least capable, but is significantly faster and cheaper.

 

Davinci는 가장 유능하지만 다른 모델보다 느리고 비쌉니다. Ada는 성능이 가장 낮지만 훨씬 빠르고 저렴합니다.

 

Similarity embeddings

Similarity models are best at capturing semantic similarity between pieces of text.

유사성 모델은 텍스트 조각 간의 의미론적 유사성을 포착하는 데 가장 적합합니다.

 

USE CASES                                                                                                            AVAILABLE MODELS

Clustering, regression, anomaly detection, visualization

text-similarity-ada-001 text-similarity-babbage-001 text-similarity-curie-001 text-similarity-davinci-001

 

Text search embeddings

 

Text search models help measure which long documents are most relevant to a short search query. Two models are used: one for embedding the search query and one for embedding the documents to be ranked. The document embeddings closest to the query embedding should be the most relevant.

 

텍스트 검색 모델은 짧은 검색 쿼리와 가장 관련성이 높은 긴 문서를 측정하는 데 도움이 됩니다. 두 가지 모델이 사용됩니다. 하나는 검색 쿼리를 포함하기 위한 것이고 다른 하나는 순위를 매길 문서를 포함하기 위한 것입니다. 쿼리 임베딩에 가장 가까운 문서 임베딩이 가장 관련성이 높아야 합니다.

 

 

USE CASES                                                                                               AVAILABLE MODELS

Search, context relevance, information retrieval

text-search-ada-doc-001 text-search-ada-query-001 text-search-babbage-doc-001 text-search-babbage-query-001 text-search-curie-doc-001 text-search-curie-query-001 text-search-davinci-doc-001 text-search-davinci-query-001

 

Code search embeddings

Similarly to search embeddings, there are two types: one for embedding natural language search queries and one for embedding code snippets to be retrieved.

검색 임베딩과 유사하게 두 가지 유형이 있습니다. 하나는 자연어 검색 쿼리를 포함하는 것이고 다른 하나는 검색할 코드 스니펫을 포함하는 것입니다.

 

USE CASES                                                                           AVAILABLE MODELS

Code search and relevance

code-search-ada-code-001 code-search-ada-text-001 code-search-babbage-code-001 code-search-babbage-text-001

 

With the -001 text embeddings (not -002, and not code embeddings), we suggest replacing newlines (\n) in your input with a single space, as we have seen worse results when newlines are present.

-001 텍스트 임베딩(-002 및 코드 임베딩이 아님)을 사용하는 경우 입력의 줄 바꿈(\n)을 단일 공백으로 바꾸는 것이 좋습니다. 줄 바꿈이 있을 때 더 나쁜 결과가 나타났기 때문입니다.

 

Collapse‍

 

 

Use cases

Here we show some representative use cases. We will use the Amazon fine-food reviews dataset for the following examples.

여기서는 몇 가지 대표적인 사용 사례를 보여줍니다. 다음 예제에서는 Amazon 고급 식품 리뷰 데이터 세트를 사용합니다.

 

Obtaining the embeddings

The dataset contains a total of 568,454 food reviews Amazon users left up to October 2012. We will use a subset of 1,000 most recent reviews for illustration purposes. The reviews are in English and tend to be positive or negative. Each review has a ProductId, UserId, Score, review title (Summary) and review body (Text). For example:

데이터 세트에는 2012년 10월까지 Amazon 사용자가 남긴 총 568,454개의 음식 리뷰가 포함되어 있습니다. 설명을 위해 가장 최근 리뷰 1,000개의 하위 집합을 사용합니다. 리뷰는 영어로 되어 있으며 긍정적이거나 부정적인 경향이 있습니다. 각 리뷰에는 ProductId, UserId, 점수, 리뷰 제목(요약) 및 리뷰 본문(텍스트)이 있습니다. 예를 들어:

 

We will combine the review summary and review text into a single combined text. The model will encode this combined text and output a single vector embedding.

리뷰 요약과 리뷰 텍스트를 하나의 결합된 텍스트로 결합합니다. 모델은 이 결합된 텍스트를 인코딩하고 단일 벡터 임베딩을 출력합니다.

 

Obtain_dataset.ipynb

 

def get_embedding(text, model="text-embedding-ada-002"):
   text = text.replace("\n", " ")
   return openai.Embedding.create(input = [text], model=model)['data'][0]['embedding']
 
df['ada_embedding'] = df.combined.apply(lambda x: get_embedding(x, model='text-embedding-ada-002'))
df.to_csv('output/embedded_1k_reviews.csv', index=False)

 

To load the data from a saved file, you can run the following:

저장된 파일로부터 데이터를 로드 하려면 아래를 실행하면 됩니다.

 

import pandas as pd
 
df = pd.read_csv('output/embedded_1k_reviews.csv')
df['ada_embedding'] = df.ada_embedding.apply(eval).apply(np.array)

 

 

Data visualization in 2D

 

Visualizing_embeddings_in_2D.ipynb

The size of the embeddings varies with the complexity of the underlying model. In order to visualize this high dimensional data we use the t-SNE algorithm to transform the data into two dimensions.

임베딩의 크기는 기본 모델의 복잡성에 따라 다릅니다. 이 고차원 데이터를 시각화하기 위해 t-SNE 알고리즘을 사용하여 데이터를 2차원으로 변환합니다.

 

We colour the individual reviews based on the star rating which the reviewer has given:

리뷰어가 부여한 별점에 따라 개별 리뷰에 색상을 지정합니다.

• 1-star: red
• 2-star: dark orange
• 3-star: gold
• 4-star: turquoise
• 5-star: dark green

The visualization seems to have produced roughly 3 clusters, one of which has mostly negative reviews.

시각화는 대략 3개의 클러스터를 생성한 것으로 보이며 그 중 하나는 대부분 부정적인 리뷰를 가지고 있습니다.

 

import pandas as pd
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import matplotlib
 
df = pd.read_csv('output/embedded_1k_reviews.csv')
matrix = df.ada_embedding.apply(eval).to_list()
 
# Create a t-SNE model and transform the data
tsne = TSNE(n_components=2, perplexity=15, random_state=42, init='random', learning_rate=200)
vis_dims = tsne.fit_transform(matrix)
 
colors = ["red", "darkorange", "gold", "turquiose", "darkgreen"]
x = [x for x,y in vis_dims]
y = [y for x,y in vis_dims]
color_indices = df.Score.values - 1
 
colormap = matplotlib.colors.ListedColormap(colors)
plt.scatter(x, y, c=color_indices, cmap=colormap, alpha=0.3)
plt.title("Amazon ratings visualized in language using t-SNE")

 

 

Embedding as a text feature encoder for ML algorithms

 

Regression_using_embeddings.ipynb

An embedding can be used as a general free-text feature encoder within a machine learning model. Incorporating embeddings will improve the performance of any machine learning model, if some of the relevant inputs are free text. An embedding can also be used as a categorical feature encoder within a ML model. This adds most value if the names of categorical variables are meaningful and numerous, such as job titles. Similarity embeddings generally perform better than search embeddings for this task.

임베딩은 기계 학습 모델 내에서 일반 자유 텍스트 기능 인코더로 사용할 수 있습니다. 임베딩을 통합하면 관련 입력 중 일부가 자유 텍스트인 경우 기계 학습 모델의 성능이 향상됩니다. 포함은 ML 모델 내에서 범주형 기능 인코더로 사용할 수도 있습니다. 이것은 범주형 변수의 이름이 직위와 같이 의미 있고 많은 경우 가장 큰 가치를 추가합니다. 유사성 임베딩은 일반적으로 이 작업에서 검색 임베딩보다 성능이 좋습니다.

 

We observed that generally the embedding representation is very rich and information dense. For example, reducing the dimensionality of the inputs using SVD or PCA, even by 10%, generally results in worse downstream performance on specific tasks.

 

우리는 일반적으로 임베딩 표현이 매우 풍부하고 정보 밀도가 높다는 것을 관찰했습니다. 예를 들어 SVD 또는 PCA를 사용하여 입력의 차원을 10%까지 줄이면 일반적으로 특정 작업에서 다운스트림 성능이 저하됩니다.

 

This code splits the data into a training set and a testing set, which will be used by the following two use cases, namely regression and classification.

 

이 코드는 데이터를 학습 세트와 테스트 세트로 분할하며, 회귀 및 분류라는 두 가지 사용 사례에서 사용됩니다.

from sklearn.model_selection import train_test_split
 
X_train, X_test, y_train, y_test = train_test_split(
    list(df.ada_embedding.values),
    df.Score,
    test_size = 0.2,
    random_state=42
)

 

Regression using the embedding features

Embeddings present an elegant way of predicting a numerical value. In this example we predict the reviewer’s star rating, based on the text of their review. Because the semantic information contained within embeddings is high, the prediction is decent even with very few reviews.

임베딩은 숫자 값을 예측하는 우아한 방법을 제공합니다. 이 예에서는 리뷰 텍스트를 기반으로 리뷰어의 별점을 예측합니다. 임베딩에 포함된 의미론적 정보가 높기 때문에 리뷰가 거의 없어도 예측이 괜찮습니다.

 

We assume the score is a continuous variable between 1 and 5, and allow the algorithm to predict any floating point value. The ML algorithm minimizes the distance of the predicted value to the true score, and achieves a mean absolute error of 0.39, which means that on average the prediction is off by less than half a star.

 

우리는 점수가 1과 5 사이의 연속 변수라고 가정하고 알고리즘이 부동 소수점 값을 예측할 수 있도록 합니다. ML 알고리즘은 예측 값과 실제 점수의 거리를 최소화하고 평균 절대 오차 0.39를 달성합니다.

 

from sklearn.ensemble import RandomForestRegressor
 
rfr = RandomForestRegressor(n_estimators=100)
rfr.fit(X_train, y_train)
preds = rfr.predict(X_test)

 

Classification using the embedding features

 

Classification_using_embeddings.ipynb

This time, instead of having the algorithm predict a value anywhere between 1 and 5, we will attempt to classify the exact number of stars for a review into 5 buckets, ranging from 1 to 5 stars.

이번에는 알고리즘이 1에서 5 사이의 값을 예측하는 대신 검토를 위한 정확한 별 수를 1에서 5개 범위의 5개 버킷으로 분류하려고 합니다.

 

After the training, the model learns to predict 1 and 5-star reviews much better than the more nuanced reviews (2-4 stars), likely due to more extreme sentiment expression.

 

학습 후 모델은 보다 극단적인 감정 표현으로 인해 미묘한 차이가 있는 리뷰(2~4개)보다 별 1개 및 5개 리뷰를 훨씬 더 잘 예측하는 방법을 학습합니다.

 

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, accuracy_score
 
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)
preds = clf.predict(X_test)

 

 

Zero-shot classification

 

Zero-shot_classification_with_embeddings.ipynb

We can use embeddings for zero shot classification without any labeled training data. For each class, we embed the class name or a short description of the class. To classify some new text in a zero-shot manner, we compare its embedding to all class embeddings and predict the class with the highest similarity.

라벨이 지정된 학습 데이터 없이 제로샷 분류에 임베딩을 사용할 수 있습니다. 각 클래스에 대해 클래스 이름 또는 클래스에 대한 간단한 설명을 포함합니다. 새로운 텍스트를 제로 샷 방식으로 분류하기 위해 임베딩을 모든 클래스 임베딩과 비교하고 유사도가 가장 높은 클래스를 예측합니다.

 

from openai.embeddings_utils import cosine_similarity, get_embedding
 
df= df[df.Score!=3]
df['sentiment'] = df.Score.replace({1:'negative', 2:'negative', 4:'positive', 5:'positive'})
 
labels = ['negative', 'positive']
label_embeddings = [get_embedding(label, model=model) for label in labels]
 
def label_score(review_embedding, label_embeddings):
   return cosine_similarity(review_embedding, label_embeddings[1]) - cosine_similarity(review_embedding, label_embeddings[0])
 
prediction = 'positive' if label_score('Sample Review', label_embeddings) > 0 else 'negative'

 

Obtaining user and product embeddings for cold-start recommendation

 

User_and_product_embeddings.ipynb

We can obtain a user embedding by averaging over all of their reviews. Similarly, we can obtain a product embedding by averaging over all the reviews about that product. In order to showcase the usefulness of this approach we use a subset of 50k reviews to cover more reviews per user and per product.

모든 리뷰를 평균하여 임베딩하는 사용자를 얻을 수 있습니다. 마찬가지로 해당 제품에 대한 모든 리뷰를 평균화하여 제품 포함을 얻을 수 있습니다. 이 접근 방식의 유용성을 보여주기 위해 50,000개 리뷰의 하위 집합을 사용하여 사용자 및 제품당 더 많은 리뷰를 다루었습니다.

 

We evaluate the usefulness of these embeddings on a separate test set, where we plot similarity of the user and product embedding as a function of the rating. Interestingly, based on this approach, even before the user receives the product we can predict better than random whether they would like the product.

우리는 별도의 테스트 세트에서 이러한 임베딩의 유용성을 평가합니다. 여기서 사용자와 제품 임베딩의 유사성을 등급의 함수로 표시합니다. 흥미롭게도 이 접근 방식을 기반으로 사용자가 제품을 받기 전에도 사용자가 제품을 좋아할지 무작위보다 더 잘 예측할 수 있습니다.

 

user_embeddings = df.groupby('UserId').ada_embedding.apply(np.mean)
prod_embeddings = df.groupby('ProductId').ada_embedding.apply(np.mean)

 

 

 

Clustering

 

Clustering.ipynb

Clustering is one way of making sense of a large volume of textual data. Embeddings are useful for this task, as they provide semantically meaningful vector representations of each text. Thus, in an unsupervised way, clustering will uncover hidden groupings in our dataset.

클러스터링은 대량의 텍스트 데이터를 이해하는 한 가지 방법입니다. 임베딩은 각 텍스트의 의미론적으로 의미 있는 벡터 표현을 제공하므로 이 작업에 유용합니다. 따라서 감독되지 않은 방식으로 클러스터링은 데이터 세트에서 숨겨진 그룹을 발견합니다.

 

In this example, we discover four distinct clusters: one focusing on dog food, one on negative reviews, and two on positive reviews.

이 예에서 우리는 4개의 뚜렷한 클러스터를 발견합니다. 하나는 개 사료에 초점을 맞추고, 하나는 부정적인 리뷰에 초점을 맞추고, 다른 하나는 긍정적인 리뷰에 초점을 맞춥니다.

 

import numpy as np
from sklearn.cluster import KMeans
 
matrix = np.vstack(df.ada_embedding.values)
n_clusters = 4
 
kmeans = KMeans(n_clusters = n_clusters, init='k-means++', random_state=42)
kmeans.fit(matrix)
df['Cluster'] = kmeans.labels_

 

Text search using embeddings

 

Semantic_text_search_using_embeddings.ipynb

To retrieve the most relevant documents we use the cosine similarity between the embedding vectors of the query and each document, and return the highest scored documents.

가장 관련성이 높은 문서를 검색하기 위해 쿼리와 각 문서의 임베딩 벡터 간의 코사인 유사성을 사용하고 점수가 가장 높은 문서를 반환합니다.

 

from openai.embeddings_utils import get_embedding, cosine_similarity
 
def search_reviews(df, product_description, n=3, pprint=True):
   embedding = get_embedding(product_description, model='text-embedding-ada-002')
   df['similarities'] = df.ada_embedding.apply(lambda x: cosine_similarity(x, embedding))
   res = df.sort_values('similarities', ascending=False).head(n)
   return res
 
res = search_reviews(df, 'delicious beans', n=3)

 

Code search using embeddings

 

Code_search.ipynb

Code search works similarly to embedding-based text search. We provide a method to extract Python functions from all the Python files in a given repository. Each function is then indexed by the text-embedding-ada-002 model.

코드 검색은 임베딩 기반 텍스트 검색과 유사하게 작동합니다. 주어진 리포지토리의 모든 Python 파일에서 Python 함수를 추출하는 방법을 제공합니다. 그런 다음 각 함수는 text-embedding-ada-002 모델에 의해 인덱싱됩니다.

 

To perform a code search, we embed the query in natural language using the same model. Then we calculate cosine similarity between the resulting query embedding and each of the function embeddings. The highest cosine similarity results are most relevant.

 

코드 검색을 수행하기 위해 동일한 모델을 사용하여 자연어로 쿼리를 포함합니다. 그런 다음 결과 쿼리 임베딩과 각 함수 임베딩 간의 코사인 유사성을 계산합니다. 가장 높은 코사인 유사성 결과가 가장 적합합니다.

 

from openai.embeddings_utils import get_embedding, cosine_similarity
 
df['code_embedding'] = df['code'].apply(lambda x: get_embedding(x, model='text-embedding-ada-002'))
 
def search_functions(df, code_query, n=3, pprint=True, n_lines=7):
   embedding = get_embedding(code_query, model='text-embedding-ada-002')
   df['similarities'] = df.code_embedding.apply(lambda x: cosine_similarity(x, embedding))
 
   res = df.sort_values('similarities', ascending=False).head(n)
   return res
res = search_functions(df, 'Completions API tests', n=3)

 

Recommendations using embeddings

 

Recommendation_using_embeddings.ipynb

Because shorter distances between embedding vectors represent greater similarity, embeddings can be useful for recommendation.

임베딩 벡터 사이의 거리가 짧을수록 유사성이 더 높기 때문에 임베딩은 추천에 유용할 수 있습니다.

 

Below, we illustrate a basic recommender. It takes in a list of strings and one 'source' string, computes their embeddings, and then returns a ranking of the strings, ranked from most similar to least similar. As a concrete example, the linked notebook below applies a version of this function to the AG news dataset (sampled down to 2,000 news article descriptions) to return the top 5 most similar articles to any given source article.

아래에서는 기본 추천자를 설명합니다. 문자열 목록과 하나의 '소스' 문자열을 가져와 임베딩을 계산한 다음 가장 유사한 항목부터 가장 유사한 항목 순으로 순위가 매겨진 문자열 순위를 반환합니다. 구체적인 예로서, 아래 링크된 노트북은 이 기능의 버전을 AG 뉴스 데이터 세트(2,000개의 뉴스 기사 설명으로 샘플링됨)에 적용하여 주어진 소스 기사와 가장 유사한 상위 5개 기사를 반환합니다.

def recommendations_from_strings(
   strings: List[str],
   index_of_source_string: int,
   model="text-embedding-ada-002",
) -> List[int]:
   """Return nearest neighbors of a given string."""

   # get embeddings for all strings
   embeddings = [embedding_from_string(string, model=model) for string in strings]
   
   # get the embedding of the source string
   query_embedding = embeddings[index_of_source_string]
   
   # get distances between the source embedding and other embeddings (function from embeddings_utils.py)
   distances = distances_from_embeddings(query_embedding, embeddings, distance_metric="cosine")
   
   # get indices of nearest neighbors (function from embeddings_utils.py)
   indices_of_nearest_neighbors = indices_of_nearest_neighbors_from_distances(distances)
   return indices_of_nearest_neighbors

 

Limitations & risks

Our embedding models may be unreliable or pose social risks in certain cases, and may cause harm in the absence of mitigations.

당사의 임베딩 모델은 신뢰할 수 없거나 경우에 따라 사회적 위험을 초래할 수 있으며 완화 조치가 없을 경우 해를 끼칠 수 있습니다.

 

Social bias

Limitation: The models encode social biases, e.g. via stereotypes or negative sentiment towards certain groups.

We found evidence of bias in our models via running the SEAT (May et al, 2019) and the Winogender (Rudinger et al, 2018) benchmarks. Together, these benchmarks consist of 7 tests that measure whether models contain implicit biases when applied to gendered names, regional names, and some stereotypes.

우리는 SEAT(May et al, 2019) 및 Winogender(Rudinger et al, 2018) 벤치마크를 실행하여 모델에서 편향의 증거를 발견했습니다. 이 벤치마크는 성별 이름, 지역 이름 및 일부 고정관념에 적용될 때 모델에 암시적 편향이 포함되어 있는지 여부를 측정하는 7가지 테스트로 구성됩니다.

 

For example, we found that our models more strongly associate (a) European American names with positive sentiment, when compared to African American names, and (b) negative stereotypes with black women.

 

예를 들어, 우리 모델은 (a) 아프리카계 미국인 이름과 비교할 때 긍정적인 정서가 있는 유럽계 미국인 이름 및 (b) 흑인 여성에 대한 부정적인 고정관념과 더 강하게 연관되어 있음을 발견했습니다.

 

These benchmarks are limited in several ways: (a) they may not generalize to your particular use case, and (b) they only test for a very small slice of possible social bias.

 

이러한 벤치마크는 다음과 같은 몇 가지 방식으로 제한됩니다. (a) 특정 사용 사례에 대해 일반화할 수 없으며 (b) 가능한 사회적 편향의 아주 작은 조각에 대해서만 테스트합니다.

 

These tests are preliminary, and we recommend running tests for your specific use cases. These results should be taken as evidence of the existence of the phenomenon, not a definitive characterization of it for your use case. Please see our usage policies for more details and guidance.

 

이러한 테스트는 예비 테스트이며 특정 사용 사례에 대한 테스트를 실행하는 것이 좋습니다. 이러한 결과는 사용 사례에 대한 결정적인 특성이 아니라 현상의 존재에 대한 증거로 간주되어야 합니다. 자세한 내용과 지침은 당사의 사용 정책을 참조하십시오.

 

Please reach out to embeddings@openai.com if you have any questions; we are happy to advise on this.

 

질문이 있는 경우 embeddings@openai.com으로 문의하십시오. 우리는 이에 대해 기꺼이 조언합니다.

 

English only

Limitation: Models are most reliable for mainstream English that is typically found on the Internet. Our models may perform poorly on regional or group dialects.

 

Researchers have found (Blodgett & O’Connor, 2017) that common NLP systems don’t perform as well on African American English as they do on mainstream American English. Our models may similarly perform poorly on dialects or uses of English that are not well represented on the Internet.

 

연구자들은 (Blodgett & O'Connor, 2017) 일반적인 NLP 시스템이 주류 미국 영어에서처럼 아프리카계 미국인 영어에서 잘 수행되지 않는다는 사실을 발견했습니다. 우리의 모델은 인터넷에서 잘 표현되지 않는 방언이나 영어 사용에 대해 제대로 작동하지 않을 수 있습니다.

 

Blindness to recent events

Limitation: Models lack knowledge of events that occurred after August 2020.

Our models are trained on datasets that contain some information about real world events up until 8/2020. If you rely on the models representing recent events, then they may not perform well.

우리 모델은 2020년 8월까지 실제 이벤트에 대한 일부 정보가 포함된 데이터 세트에서 학습됩니다. 최근 이벤트를 나타내는 모델에 의존하는 경우 성능이 좋지 않을 수 있습니다.

 

Frequently asked questions

How can I tell how many tokens a string will have before I embed it?

For second-generation embedding models, as of Dec 2022, there is not yet a way to count tokens locally. The only way to get total token counts is to submit an API request.

2세대 임베딩 모델의 경우 2022년 12월 현재 로컬에서 토큰을 계산하는 방법이 아직 없습니다. 총 토큰 수를 얻는 유일한 방법은 API 요청을 제출하는 것입니다.

 

• If the request succeeds, you can extract the number of tokens from the response: response[“usage”][“total_tokens”]
• If the request fails for having too many tokens, you can extract the number of tokens from the error message: e.g., This model's maximum context length is 8191 tokens, however you requested 10000 tokens (10000 in your prompt; 0 for the completion). Please reduce your prompt; or completion length.

* 요청이 성공하면 응답에서 토큰 수를 추출할 수 있습니다. response[“usage”][“total_tokens”]
* 토큰이 너무 많아 요청이 실패하는 경우 오류 메시지에서 토큰 수를 추출할 수 있습니다. 예: 이 모델의 최대 컨텍스트 길이는 8191 토큰이지만 10000 토큰을 요청했습니다(프롬프트에서 10000, 완료를 위해 0). 프롬프트를 줄이십시오. 또는 완료 길이.

 

For first-generation embedding models, which are based on GPT-2/GPT-3 tokenization, you can count tokens in a few ways:

GPT-2/GPT-3 토큰화를 기반으로 하는 1세대 임베딩 모델의 경우 몇 가지 방법으로 토큰을 계산할 수 있습니다.

• For one-off checks, the OpenAI tokenizer page is convenient
• In Python, transformers.GPT2TokenizerFast (the GPT-2 tokenizer is the same as GPT-3)
• In JavaScript, gpt-3-encoder

* 일회성 확인의 경우 OpenAI 토크나이저 페이지가 편리합니다.
* Python에서 transformers.GPT2TokenizerFast(GPT-2 토크나이저는 GPT-3과 동일함)
* JavaScript에서 gpt-3-encoder

Python example:

 

from transformers import GPT2TokenizerFast

def num_tokens_from_string(string: str, tokenizer) -> int:
    return len(tokenizer.encode(string))

string = "your text here"
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")

num_tokens_from_string(string, tokenizer)

 

 

How can I retrieve K nearest embedding vectors quickly?

For searching over many vectors quickly, we recommend using a vector database.

많은 벡터를 빠르게 검색하려면 벡터 데이터베이스를 사용하는 것이 좋습니다.

 

Vector database options include:

• Pinecone, a fully managed vector database
• Weaviate, an open-source vector search engine
• Faiss, a vector search algorithm by Facebook
•  

Which distance function should I use?

We recommend cosine similarity. The choice of distance function typically doesn’t matter much.

코사인 유사성을 권장합니다. 거리 함수의 선택은 일반적으로 그다지 중요하지 않습니다.

 

OpenAI embeddings are normalized to length 1, which means that:

• Cosine similarity can be computed slightly faster using just a dot product
• Cosine similarity and Euclidean distance will result in the identical rankings

OpenAI 임베딩은 길이 1로 정규화되며 이는 다음을 의미합니다.

* 코사인 유사도는 내적만 사용하여 약간 더 빠르게 계산할 수 있습니다.
* 코사인 유사성과 유클리드 거리는 동일한 순위가 됩니다.