README.md

---
license: apache-2.0
language: en
tags:
- sentence similarity
library_name: sentence-transformers
pipeline_tag: sentence-similarity
---


# Dataset Collection:
* The news dataset is collected from Kaggle[dataset](https://www.kaggle.com/competitions/fake-news/data)
* The dataset has news title ,news content and the label(the label shows the cosine similarity between news title and news content).
* Different strategies have been followed during the data gathering phase.

# sentence transformer is fine-tuned for semantic search and sentence similarity
* The model is fine-tuned on the dataset.
* This model can be used for semantic search,sentence similarity,recommendation system.
* This model can be used for the inference purpose as well.

# Data Fields:
 
**label**: cosine similarity between news title and news content
**news title**: The title of the news
**news content**:The content of the news

# Application:
* This model is useful for the semantic search,sentence similarity,recommendation system.
* You can fine-tune this model for your particular use cases.

# Model Implementation

# pip install -U sentence-transformers

```
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]))
```


# Github: [Sakil Ansari](https://github.com/Sakil786/sentence_similarity_semantic_search)