Introduction to Preshed – Quick and Efficient Hash Table for NLP

Preshed is a powerful library designed for Natural Language Processing (NLP) tasks that require quick and efficient hash table operations. Integrated effectively with spaCy, it significantly optimizes the speed and performance of language processing pipelines.

Key Preshed API Functionalities with Code Examples

1. Creating a Hash Table

Using the preshed API to create a basic hash map:

 
   from preshed.hashisthashint import HashSet
   word_hash = HashSet()
 

2. Inserting a Value

Insert a key-value pair into the hash table:

 
   word_hash.add(key)
 

3. Checking for Key Existence

Verify whether a key exists in the hash table:

 
   if key in word_hash:
       print("Key exists!")
   else:
       print("Key does not exist!")
 

4. Deleting a Key

Remove a key from the hash table:

 
   word_hash.discard(key)
 

5. Hash Combining for Collision Handling

Combining hashes to manage multiple keys:

 
   from preshed.maps import merge_table
   combined_hash = merge_table(hash1, hash2)
 

Full Example

Implementing a simple NLP text unique word tracker:

 
   from preshed.hashisthashint import HashSet

   def track_unique_words(text):
       word_hash = HashSet()
       words = text.lower().split()
       for word in words:
           word_hash.add(hash(word))
       return word_hash

   text = "This is an example of tracking unique words using preshed library"
   result = track_unique_words(text)
   print(f"Unique words in text: {len(result)}")
 

Conclusion

Preshed is a highly efficient and optimized hashing library perfect for advanced NLP applications. By leveraging its functionalities, developers can significantly boost their application’s performance. Integrated seamlessly with SpaCy, it ensures your NLP pipelines are optimized for speed and efficiency.

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