Sentence Similarity Semantic Search

🚀 Sentence Similarity and Semantic Search Model

This model is fine - tuned for semantic search and sentence similarity, offering solutions for various NLP tasks.

🚀 Quick Start

To use this model, first install the sentence-transformers library:

pip install -U sentence-transformers

✨ Features

• Fine - Tuned on Custom Dataset: The model is fine - tuned on a news dataset collected from Kaggle, which contains news titles, news content, and a label representing the cosine similarity between the title and content.
• Multiple Applications: It can be used for semantic search, sentence similarity calculation, and recommendation systems. It's also suitable for inference purposes.
• Customizable: You can fine - tune this model for your specific use cases.

📦 Installation

Install the necessary library using the following command:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer, InputExample, losses
import pandas as pd
from sentence_transformers import SentenceTransformer, InputExample
from torch.utils.data import DataLoader
from sentence_transformers import SentenceTransformer, util

model_name="Sakil/sentence_similarity_semantic_search"
model = SentenceTransformer(model_name)
sentences = ['A man is eating food.',
          'A man is eating a piece of bread.',
          'The girl is carrying a baby.',
          'A man is riding a horse.',
          'A woman is playing violin.',
          'Two men pushed carts through the woods.',
          'A man is riding a white horse on an enclosed ground.',
          'A monkey is playing drums.',
          'Someone in a gorilla costume is playing a set of drums.'
          ]

#Encode all sentences
embeddings = model.encode(sentences)

#Compute cosine similarity between all pairs
cos_sim = util.cos_sim(embeddings, embeddings)

#Add all pairs to a list with their cosine similarity score
all_sentence_combinations = []

for i in range(len(cos_sim)-1):

    for j in range(i+1, len(cos_sim)):
    
        all_sentence_combinations.append([cos_sim[i][j], i, j])

#Sort list by the highest cosine similarity score

all_sentence_combinations = sorted(all_sentence_combinations, key=lambda x: x[0], reverse=True)

print("Top-5 most similar pairs:")

for score, i, j in all_sentence_combinations[0:5]:

    print("{} \t {} \t {:.4f}".format(sentences[i], sentences[j], cos_sim[i][j]))

📚 Documentation

Dataset Collection

• The news dataset is collected from Kaggle dataset.
• The dataset includes news titles, news content, and a label indicating the cosine similarity between the news title and news content.
• Different strategies were employed during the data - gathering phase.

Data Fields

Application

This model is useful for semantic search, sentence similarity, and recommendation systems. You can fine - tune it for your particular use cases.

📄 License

This project is licensed under the Apache 2.0 license.

🔗 Github

Sakil Ansari