How to create a keyword embedding model?

In the context of data science, here are the detailed steps given of how you can build and train a keyword embedding model for your given scenario:-

Data collection and preprocessing

Firstly, you would need to gather a diverse text in multiple languages that are relevant to the search queries. You should try to ensure that it covers the various domains and it should be large enough to train embedding effectively.

Text cleaning

You can try to perform text preprocessing such as tokenization, normalization, and removal of stop words and also the special characters for each language.

Handling multilingual data

You can use language detection tools to separate the corpus by language. For this libraries such as “langdetect” can be used:-

From langdetect import detect

From transformers import BertTokenizer

# Example of text normalization and tokenization

Text = “Bonjour! Comment ça va?”

Tokenizer = BertTokenizer.from_pretrained(‘bert-base-multilingual-cased’)

Tokens = tokenizer.tokenize(text.lower())

Print(tokens)

Choose and embed the model

Now you can use a pre-trained multilingual model. These models are capable of handling the multiple languages and they are also more trained on a vast corpus of multilingual text.

Embedding layer

You can use the embedding layer from the pre-trained model which can provide a dense vector representation of words in multiple languages. You should also perform fine-tuning the model on your specific task to adjust the embedding for the search context.

From transformers import AutoModel, AutoTokenizer

Model_name = “xlm-roberta-base”

Tokenizer = AutoTokenizer.from_pretrained(model_name)

Model = AutoModel.from_pretrained(model_name)

Training the model

You can create a pair of queries and also the relevant documents or even keywords. You should also try to ensure that the data is automated with the language information for better understanding.

You can use the contrastive loss or even triplet loss for training the embedding. You can also implement the training loop with the mini-batches and backpropagation for updating the weight of the model.

Import torch

From torch import nn, optim

From torch.utils.data import DataLoader

# Define a simple dataset and data loader

Class QueryDataset(torch.utils.data.Dataset):

    Def __init__(self, queries, documents):

        Self.queries = queries

        Self.documents = documents

    Def __len__(self):

        Return len(self.queries)

    Def __getitem__(self, idx):

        Return self.queries[idx], self.documents[idx]

# Example training loop

Dataset = QueryDataset(queries, documents)

Data_loader = DataLoader(dataset, batch_size=32, shuffle=True)

Optimizer = optim.Adam(model.parameters(), lr=1e-5)

Criterion = nn.TripletMarginLoss()

For epoch in range(num_epochs):

    For query, doc in data_loader:

        Optimizer.zero_grad()

        Query_embeddings = model(**tokenizer(query, return_tensors=’pt’))[0]

        Doc_embeddings = model(**tokenizer(doc, return_tensors=’pt’))[0]

        Loss = criterion(query_embeddings, doc_embeddings, negative_embeddings)

        Loss.backward()

        Optimizer.step()

Cross-lingual performance

Fine-tune the model so that you can ensure that it maintains the performance across the language.

From sklearn.metrics import precision_score, recall_score, f1_score

# Example evaluation metrics calculation

Precision = precision_score(y_true, y_pred, average=’macro’)

Recall = recall_score(y_true, y_pred, average=’macro’)

F1 = f1_score(y_true, y_pred, average=’macro’)

Print(f”Precision: {precision}, Recall: {recall}, F1 Score: {f1}”)

Scalability

You should try to ensure that the deployment should scale to handle a high volume of search queries.

From fastapi import FastAPI

App = FastAPI()

@app.post(“/embed/”)

Async def embed_query(query: str):

    Tokens = tokenizer(query, return_tensors=’pt’)

    Embeddings = model(**tokens)[0]

    Return {“embedding”: embeddings.tolist()}